CN117784623A - Multi-strategy collaborative intelligent optimization method and device for vehicle path with load constraint - Google Patents
Multi-strategy collaborative intelligent optimization method and device for vehicle path with load constraint Download PDFInfo
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Abstract
The invention provides a multi-strategy collaborative intelligent optimization method and device for a vehicle path with load constraint, comprising the following steps: acquiring a path starting point, a path ending point and a plurality of target points to be planned; initializing parameters of a whale optimization algorithm based on a path starting point, a path ending point and a target point to obtain an initial path planning scheme; the initial path planning scheme is updated sequentially through an improved position updating formula in a whale optimizing algorithm, an improved speed in an gravitation searching algorithm and a position updating formula, so that an updated path planning scheme corresponding to the current iteration times and a corresponding fitness value of the updated path planning scheme are obtained; and continuously updating the updated path planning scheme corresponding to the current iteration times until the preset iteration stop condition is met, so as to obtain the target path planning scheme. The invention can obviously improve the problem of easy local optimum sinking existing in the prior path planning technology, and improve the efficiency, accuracy and stability of path planning.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a multi-strategy collaborative intelligent optimization method and device for a vehicle path with load constraint.
Background
With the advancement of modern technology and society, vehicle path problems in the transportation field have become an important factor affecting the development of various industries, and more typically, vehicle path problems with load constraints, in this context, efficient vehicle path planning has become critical. The vehicle path planning problem with load constraints (Capacitated Vehicle Routing Problem, CVRP) is a classical variant of the vehicle path planning problem. The method is also a hot problem in the fields of combination optimization and operation research, has a very wide application background in the field of transportation, and therefore has very high practical application and theoretical research value on how to effectively solve the CVRP.
The applicant found that there are at least the following problems in the related art: at present, the problem of vehicle path planning with load constraint is easy to fall into a local optimum problem, so that the accuracy of a final path planning scheme is not accurate enough.
Disclosure of Invention
In view of the above, the present invention aims to provide a multi-strategy collaborative intelligent optimization method and device for constraining a vehicle path with load, which can significantly improve the problem of easy local optimization in the existing path planning technology, and improve the efficiency, accuracy and stability of path planning.
In a first aspect, an embodiment of the present invention provides a multi-strategy collaborative intelligent optimization method for a vehicle path with load constraint, including:
acquiring a path starting point, a path ending point and a plurality of target points to be planned;
initializing parameters of a whale optimization algorithm based on the path starting point, the path ending point and the target point to obtain an initial path planning scheme;
updating the initial path planning scheme sequentially through an improved position updating formula in the whale optimizing algorithm, an improved speed in the gravity searching algorithm and a position updating formula to obtain an updated path planning scheme corresponding to the current iteration times and a corresponding fitness value of the updated path planning scheme; the improved position updating formula is constructed based on a nonlinear contraction factor and an adaptive weight, the improved speed and position updating formula is constructed based on the adaptive weight, and the fitness value is used for evaluating the quality of the updated path planning scheme;
continuing to update the updated path planning scheme corresponding to the current iteration times until a preset iteration stop condition is met, so as to obtain a target path planning scheme; starting from the path starting point, vehicles sequentially travel to each target point according to the target path planning scheme, deliver cargoes to the target points and return to the path ending point, wherein the number of the vehicles is at least one.
In one embodiment, the step of updating the initial path planning scheme to obtain an updated path planning scheme corresponding to the current iteration number and a corresponding fitness value thereof sequentially through an improved position updating formula in the whale optimization algorithm, an improved speed in the gravity searching algorithm and a position updating formula includes:
determining a nonlinear contraction factor and an adaptive weight according to the current iteration number and the preset final iteration number, so as to construct an improved position updating formula in the whale optimizing algorithm based on the nonlinear contraction factor and the adaptive weight, and constructing an improved speed and position updating formula in an gravitation searching algorithm based on the adaptive weight;
updating the initial path planning scheme through the improved position updating formula, and updating the updated initial path planning scheme through the improved speed and the position updating formula to obtain an updated path planning scheme corresponding to the current iteration times;
updating the fitness value corresponding to the updated path planning scheme through the cooled Metropolis criterion corresponding to the previous iteration times, and cooling the Metropolis criterion by using a fire-reduction rate coefficient after updating the fitness value so as to obtain the cooled Metropolis criterion corresponding to the current iteration times; wherein the fire rate coefficient is determined based on the final number of iterations.
In one embodiment, the step of determining the nonlinear contraction factor and the adaptive weight according to the current iteration number and the preset final iteration number includes:
the nonlinear contraction factor is calculated according to the following formula:
；
the adaptive weights are calculated according to the following formula:
；
wherein,is nonlinear contraction factor->Is adaptive weight, ++>For the final iteration number>For the current iteration number>Is a correlation coefficient.
In one embodiment, the step of constructing an improved location update formula in the whale optimization algorithm based on the nonlinear contraction factor and the adaptive weights comprises:
determining a first coefficient vector according to the first random number and the nonlinear contraction factor, and determining a second coefficient vector according to the second random number;
an improved location update formula in the whale optimization algorithm is constructed based on the first coefficient vector, the second coefficient vector, and the adaptive weights.
In one embodiment, the step of updating the initial path planning scheme by the improved location update formula comprises:
under the condition that the probability of the predation mechanism is larger than or equal to a first preset value, updating the initial path planning scheme by adopting an improved position updating formula in a bubble attack mode; wherein, the probability of the predation mechanism is a random number between 0 and 1, and the improved position updating formula under the bubble attack mode is as follows:
；
；
Under the condition that the probability of the predation mechanism is smaller than the first preset value and the first coefficient vector in the whale optimization algorithm is larger than or equal to the second preset value, updating the initial path planning scheme by adopting an improved position updating formula in a search predation mode, wherein the improved position updating formula in the search predation mode is as follows:
；
；
and under the condition that the probability of predation mechanism is smaller than the first preset value and the first coefficient vector is smaller than the second preset value, updating the initial path planning scheme by adopting an improved position updating formula in a surrounding prey mode, wherein the improved position updating formula in the surrounding prey mode is as follows:
；
；
wherein,is->Second iteration->Position of whale individual->Is->Position of optimal whale individual at several iterations, < ->Is->Second iteration->Position of whale individual->Is->Position of any whale individual at the next iteration,/->、/>All are->Distance between the position of the whale individual alone and the position of the optimal whale individual,/i>Is->Distance between the position of the whale individual to the position of any whale individual, which is used to characterize the updated initial path planning scheme,/for example >For the number of iterations->Is adaptive weight, ++>Is constant (I)>Defines the shape of a logarithmic spiral, +.>Is a random number +.>For the first coefficient vector, +.>Is the second coefficient vector.
In one embodiment, the improved speed and location update formula is as follows:
；
+/>；
wherein,is->Second iteration->Speed of whale individual in d-dimensional space, < >>Is adaptive weight, ++>Is->Second iteration->Speed of whale individual in d-dimensional space, < >>For the correlation coefficient +.>In the form of a random number,is->Position of optimal whale individual at several iterations, < ->Is->Second iteration->Position of whale individual in d-dimensional space, < >>Is->Second iteration->Acceleration of whale individual in d-dimensional space, < >>Is->Second iteration->Position of whale individual in d-dimensional space.
In one embodiment, initializing parameters of a whale optimization algorithm based on the path start point, the path end point, and the target point to obtain an initial path planning scheme includes:
initializing parameters of a whale optimization algorithm based on the path starting point, the path ending point and the target point to determine a plurality of candidate path planning schemes and corresponding fitness values thereof;
And comparing the fitness value corresponding to each candidate path planning scheme to determine an initial path planning from the candidate path planning schemes based on the comparison result.
In a second aspect, an embodiment of the present invention further provides a multi-policy collaborative intelligent optimization apparatus for restricting a vehicle path with a load, including:
the acquisition module is used for acquiring a path starting point, a path ending point and a plurality of target points to be planned;
the initialization module is used for initializing parameters of a whale optimization algorithm based on the path starting point, the path ending point and the target point so as to obtain an initial path planning scheme;
the path planning module is used for updating the initial path planning scheme sequentially through an improved position updating formula in the whale optimizing algorithm, an improved speed in the gravity searching algorithm and a position updating formula so as to obtain an updated path planning scheme corresponding to the current iteration times and a corresponding fitness value of the updated path planning scheme; the improved position updating formula is constructed based on a nonlinear contraction factor and an adaptive weight, the improved speed and position updating formula is constructed based on the adaptive weight, and the fitness value is used for evaluating the quality of the updated path planning scheme;
The path planning module is further used for continuously updating the updated path planning scheme corresponding to the current iteration times until a preset iteration stop condition is met, so as to obtain a target path planning scheme; starting from the path starting point, vehicles sequentially travel to each target point according to the target path planning scheme, deliver cargoes to the target points and return to the path ending point, wherein the number of the vehicles is at least one.
In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory storing computer-executable instructions executable by the processor to implement the method of any one of the first aspects.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of the first aspects.
The embodiment of the invention provides a multi-strategy collaborative intelligent optimization method and device for a vehicle path with load constraint, which comprises the steps of firstly acquiring a path starting point, a path ending point and a plurality of target points to be planned; initializing parameters of a whale optimization algorithm based on a path starting point, a path ending point and a target point to obtain an initial path planning scheme; the initial path planning scheme is updated sequentially through an improved position updating formula in a whale optimizing algorithm, an improved speed in an gravitation searching algorithm and a position updating formula, so that an updated path planning scheme corresponding to the current iteration number and a corresponding fitness value of the updated path planning scheme are obtained, the improved position updating formula is constructed based on a nonlinear contraction factor and self-adaptive weight, the improved speed and the position updating formula are constructed based on the self-adaptive weight, and the fitness value is used for evaluating the quality of the updated path planning scheme; and continuously updating the updated path planning scheme corresponding to the current iteration times until the preset iteration stop condition is met, obtaining a target path planning scheme, starting from a path starting point, sequentially driving the vehicles to each target point according to the target path planning scheme, distributing cargoes for the target points, and returning to a path ending point, wherein the number of the vehicles is at least one. The method is improved on the basis of the traditional whale optimization algorithm, the global and local searching capacity is well regulated by introducing the nonlinear contraction factor, the diversity of the population is well maintained by introducing the self-adaptive weight, the convergence and the searching performance of the algorithm are improved, and the searching capacity of the algorithm is further improved by introducing the gravitation searching algorithm, so that the problem that the existing path planning technology is easy to fall into local optimum is remarkably improved, and the efficiency, the accuracy and the stability of path planning are improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a multi-strategy collaborative intelligent optimization method for constraining a vehicle path with a load according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another multi-strategy collaborative intelligent optimization method for restricting a vehicle path with load according to an embodiment of the present invention;
FIG. 3 is a diagram of a GA distribution trace provided in an embodiment of the present invention;
FIG. 4 is a graph of a GSA-WOA distribution trajectory according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a multi-strategy collaborative intelligent optimization device with load constraint for vehicle paths according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The cetrimide et al developed a whale optimization algorithm (Whale Optimization Algorithm, WOA) based on the predation behavior of whales on the seat. The method has the advantages of few control parameters, simplicity in calculation and the like. However, the classical whale optimization algorithm has slow convergence, and the later iteration of the algorithm lacks population diversity and is easy to fall into local optimum. Therefore, when the whale optimization algorithm is used for carrying out the on-load constraint path planning, the related technology is easy to fall into the problem of local optimization, so that the accuracy of a final path planning scheme is not accurate enough. Based on the method and the device, the implementation of the invention provides a multi-strategy collaborative intelligent optimization method and device for restraining the vehicle path with load, which can obviously improve the problem that the existing path planning technology is easy to fall into local optimum and improve the efficiency, accuracy and stability of path planning.
For the understanding of the present embodiment, first, a detailed description will be given of a multi-strategy collaborative intelligent optimization method for a vehicle path with load constraint disclosed in the present embodiment, and referring to a flow chart of a multi-strategy collaborative intelligent optimization method for a vehicle path with load constraint shown in fig. 1, the method mainly includes steps S102 to S108:
step S102, a path starting point, a path ending point and a plurality of target points to be planned are obtained.
In one example, the path start point and the path end point may be the same or different, such as in a cargo delivery scenario where the path start point and the path end point may be the same delivery center from which the vehicle starts and returns to the delivery center after delivery of the cargo to customers at the respective target points.
Step S104, initializing parameters of the whale optimization algorithm based on the path starting point, the path ending point and the target point to obtain an initial path planning scheme.
In one example, the initialized content includes population size N, current iteration numberThe final number of iterations (i.e. maximumIteration number)/(>Initial temperature->Fire-reducing rate coefficient->And the adaptive value is used for evaluating the quality of the alternative path planning schemes and selecting an optimal individual position, namely the initial path planning scheme, from the adaptive values by comparing the adaptive values.
Step S106, the initial path planning scheme is updated sequentially through an improved position updating formula in the whale optimizing algorithm, an improved speed in the gravity searching algorithm and a position updating formula, so that an updated path planning scheme corresponding to the current iteration number and a corresponding fitness value of the updated path planning scheme are obtained.
The improved position updating formula is constructed based on the nonlinear contraction factor and the adaptive weight, and the improved speed and position updating formula is constructed based on the adaptive weight. In one example, firstly, determining a nonlinear contraction factor and an adaptive weight corresponding to the current iteration number; substituting the nonlinear contraction factor and the adaptive weight into an improved position updating formula to update the initial path planning scheme by using the formula; substituting the self-adaptive weight value into an improved speed and position updating formula, and updating the updated initial path planning scheme again by using the formula to obtain an updated path planning scheme; and finally, determining the fitness value corresponding to the updated path planning scheme by using a Metropolis criterion.
And S108, continuously updating the updated path planning scheme corresponding to the current iteration times until a preset iteration stop condition is met, and obtaining the target path planning scheme.
The preset iteration stop condition may be the final iteration number, and the target path planning scheme is a vehicle path line. Starting from the starting point of the path, the vehicles sequentially travel to each target point according to the target path planning scheme, deliver cargoes to the target points, and return to the ending point of the path, wherein the number of the vehicles is at least one. Loaded generally refers to vehicles having certain capacity limitations that may represent weight, volume, etc. metrics of the items, ensuring that each vehicle must not load items beyond its maximum capacity limit. In one example, if the current iteration number does not reach the final iteration number, the step S106 is repeatedly executed until the final iteration number is reached, and the execution of the step S106 is stopped, where the fitness value is the smallest fitness value in the whole iteration period, so that the corresponding path planning scheme is the best path planning scheme in the whole iteration period, and therefore the path planning scheme output by the iteration is used as the target path planning scheme.
The multi-strategy collaborative intelligent optimization method for the vehicle path with the load constraint provided by the embodiment of the invention is improved on the basis of the traditional whale optimization algorithm, the global and local searching capacity is well adjusted by introducing the nonlinear contraction factor, the diversity of the population is well maintained by introducing the self-adaptive weight, the convergence and the searching capacity of the algorithm are improved, and the searching capacity of the algorithm is further improved by introducing the gravitation searching algorithm, so that the problem that the existing path planning technology is easy to fall into the local optimal problem is remarkably improved, and the efficiency, the accuracy and the stability of the path planning are improved.
For easy understanding, the embodiment of the invention provides a specific implementation mode of a multi-strategy collaborative intelligent optimization method for restraining a vehicle path with load.
For the foregoing step S104, the embodiment of the present invention provides an implementation manner of initializing parameters of a whale optimization algorithm based on a path start point, a path end point and a target point to obtain an initial path planning scheme. Specific: firstly, initializing parameters of a whale optimization algorithm to determine a plurality of candidate path planning schemes and corresponding fitness values thereof; and then comparing the fitness value corresponding to each candidate path planning scheme to determine an initial path planning from the candidate path planning schemes based on the comparison result.
In practical applications, the positions of the initial WOA and GSA (gravity search algorithm) populations are randomized based on the path start point, path end point and target point, including the current population sizeCurrent iteration number +.>Maximum number of iterations reachedAnd +.>Fire-reducing rate coefficient->Initializing the equal parameters to determine fitness value of each whale individual in the population, and determining optimal individual position by comparison >I.e. the initial path planning scheme. Where whale individuals generally refer to a set of vehicle path schemes, i.e., a solution is a set of routes, each route representing a vehicle path. In the iteration of the algorithm, these whale individuals are updated through an optimization process, looking for a more optimal solution, i.e. a more optimal vehicle path-line.
Further, explanation is made on the conventional optimization algorithm: the whale optimization algorithm simulates a unique search method and a mechanism for searching for an optimal solution around the hunting seat whale, wherein the mechanism mainly comprises three important content stages, namely surrounding a prey, a bubble network attack strategy and searching for the prey. In the whale optimization algorithm, the location of each whale represents a search agent. The whale optimization algorithm continuously updates the optimal solution search agent of the global optimization problem through the three stages.
The individual updating position mode of the whale optimization algorithm is as follows: is provided withIs->Random number between->Is the probability of predation mechanism.
In one example, ifThe individual position of the whale individual is updated in a spiral mode by adopting a bubble attack mode, namely the current whale individual approaches to the current optimal whale individual in a spiral mode. Under the bubble attack mode, the traditional whale individual position updating formula is as follows:
；
；
Wherein,representing next generation->Position of whale, herba Cistanchis>Representing the global optimum vector at the current moment, i.e. the current optimum whale individual position>The state of the logarithmic spiral is defined for a constant; />Is->An indefinite number within a zone. />The representation is +.>Distance of the position of the whale individual to the current optimal whale individual position.
In one example, ifAnd->The whale individuals perform individual position updating by adopting a search predation mode, namely the whale individuals may not approach the optimal whale individuals, but randomly select one whale individual to approach. In the search predation mode, the traditional whale individual position updating formula is as follows:
；
；
wherein, it is assumed thatIn the dimensional space, use->To indicate the position of a random whale in the selected current whale population,/->Then indicate +.>Distance from the position of individual whale to the position of random whale in the current population,/->，/>Wherein->Is->Random numbers in between.
In one example, ifAnd->The whale individual performs an individual location update in a manner surrounding the prey. In the surrounding prey approach, the traditional whale individual location update formula is:
；
；
；
；
；
wherein,representing the% >The position of whale individual to the currentOptimal whale individual position distance, < >>，/>Wherein->Is->Random numbers in between.
Further, explanation is made on a conventional gravity search algorithm:
in general, when solving some of the non-linearity problems with swarm intelligence algorithms, it is found that some of the capabilities of the algorithm are affected by other causes, mainly by early convergence and also by its convergence speed. The ability to search for a space and the ability to open up are also important in the balancing algorithm.
The gravity search algorithm is a heuristic optimization method, the inspiration comes from physics, and the most influenced is the population optimization algorithm of universal gravitation and Newton's law. When the attractive force exists, the substance moves in a direction which can take time and has the shortest distance, and the better the position is, the larger the mass is. Under the influence of gravity, all populations eventually accumulate in the range of maximum mass, with the particle of maximum mass being in the optimal position.
In the conventional gravity search algorithm, N particles are in the d-dimensional search space, the thThe positions of the individual particles are:wherein->Indicate- >The individual particles are->The position in the dimension. Wherein the inertial mass of the individual particles is influenced by an adaptation value derived from the position in which the particles are currently located, moment t, inertial mass +.>Is defined as follows:
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；
wherein,is particle->At->An adaptive R-degree function of time; best (t) represents the moment +.>In (2) the best solution, workt (>) Indicating time->If the worst solution is the problem of minimum value, best corresponds to the minimum value of the particle fitness, and worst corresponds to the maximum value; if the maximum problem is solved, the opposite is true.
At the moment of timeParticle->Is subject to particles in d-dimensional space>The gravitation of (2) is:
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wherein at the moment of timeParticle->And->The inertial masses of (2) are respectively->And->Indicating (I)>A constant is generally adopted, and the situation that the denominator is 0 needs to be avoided; />Expressed as particles->And->Euclidean distance between them; />Representation->Universal gravitation constant at moment; />And->Respectively expressed in->Current particle +.>And->The position in d-dimensional space at this point in time.
；
Wherein,is the gravitational constant; />T is the maximum number of iterations of the algorithm.
To sum up, inAt time, the d-th dimension acts on the particle +.>The resultant force calculation formula of (2) is:
；
wherein, Is [0,1]Random number in range, < >>Indicating particle->Is subject to particles in d-dimensional space>Is a magnetic force of gravity.
In each iteration, the conventional improvement speed and location update formula is:
；
+/>
；
wherein,and->Are respectively->Time particle->Velocity and acceleration in d-dimensional space; />Is [0,1]Random numbers within the range; />For acting on particles in the d-dimension +.>Is a combination of the above.
It should be noted that, after the attraction search algorithm is introduced into the whale optimization algorithm, the time in the attraction search algorithmEngravingEquivalent to the number of iterations +.>Particle in gravity search algorithm>Particle->A whale individual equivalent to the whale optimization algorithm +.>Whale individual->。
However, both the conventional whale optimization algorithm and the conventional gravity search algorithm are in need of improvement.
In particular, the conventional whale optimization algorithm suffers from two drawbacks in solving the vehicle path problem: (1) The three position updating formulas of the whale optimizing algorithm are used for updating the positions of whales individuals at the moment through the variation of the most individual positions, so that the loss of population diversity is caused, and the initial solution searching of the algorithm is blind when solving the problem; (2) The whale optimization algorithm lacks a disturbance mechanism, and has the defects of low convergence speed in the later stage of the algorithm, easiness in sinking into local optimum and the like.
The following improvements are made for several of the above drawbacks: (1) By adopting the new contraction factor method, the global and local searching capability is well regulated. (2) The adaptive weight strategy is introduced to well maintain the diversity of the population, so that the convergence and the searching performance of the algorithm are improved. (3) Introduction ofThe global optimizing capability is improved by accepting the worse points with a certain probability, so that the algorithm is prevented from falling into a local optimal solution. (4) Searching for prey in whale optimization algorithmThe improved GSA searching algorithm is adopted to accelerate the optimizing speed of the algorithm, and the searching range of the population in the solution space is enlarged, so that the searching capability and convergence accuracy of the whole algorithm are improved.
On this basis, for the foregoing step S106, the embodiment of the present invention provides an implementation manner in which the initial path planning scheme is updated sequentially through the improved position updating formula in the whale optimization algorithm, the improved speed in the gravity searching algorithm, and the position updating formula, so as to obtain an updated path planning scheme corresponding to the current iteration number and a corresponding fitness value thereof, which is described in the following steps 1 to 3:
step 1, determining a nonlinear contraction factor and an adaptive weight according to the current iteration number and the preset final iteration number, constructing an improved position updating formula in a whale optimization algorithm based on the nonlinear contraction factor and the adaptive weight, and constructing an improved speed and position updating formula in an gravitation searching algorithm based on the adaptive weight.
In one example, the contraction factor of the traditional whale optimization algorithm is a linear contraction factor,the value of (2) decreases linearly from 2 to 0, and the convergence speed is slower, resulting in longer search time and lower algorithm efficiency. The embodiment of the invention can effectively improve all searching capacity of the algorithm by adopting the method of nonlinear contraction factors, and the calculation formula of the nonlinear contraction factors is as follows:
；
wherein,is nonlinear contraction factor->For the final iteration number>For the current iterationNumber of times (I)>Is the correlation coefficient of the expression, in order to make the convergence factor +.>Satisfy decreasing from 2 to 0, select +.>，/>。
In one example, the location update is known by the search mechanism of the whale optimization algorithm to update the location of the whale individual at this time through the location variation of the optimal individual, which can result in the loss of population diversity, and the solution search is blind in the initial stage of the large-scale problem algorithm. By introducing an adaptive value duty cycle to the three location update formulas for whales, a better solution to these problems is achieved. Adaptive weightsThe calculation formula of (2) is as follows:
；
wherein,is adaptive weight, ++>For the final iteration number>The current iteration number. The diversity of the population is well maintained by adopting the self-adaptive weight strategy, so that the convergence and the searching performance of the algorithm are improved.
Adaptive weights in embodiments of the present inventionThe adaptive weight is continuously adjusted along with the increase of the iteration times of the population>Is adaptive to the size of the weight +.>The size of (2) will influence the specific gravity of the location update formula, the adaptive weight +.>The algorithm will focus on global contraction to the late iterative adaptive weight +.>The algorithm is focused on local development, so that the performance of the algorithm can be effectively improved, and experimental data also prove the point. Therefore, the blindness problem of the existing algorithm search can be effectively solved, the defect of searching for a solution mechanically at the later stage of the algorithm is avoided, the balance between the global search and the local development of the algorithm is not facilitated, and the self-adaption weight is->This disadvantage is just ameliorated.
On the basis of the foregoing embodiment, the embodiment of the present invention further provides an implementation manner for constructing an improved location update formula in a whale optimization algorithm based on a nonlinear contraction factor and an adaptive weight: the first coefficient vector may be determined from the first random number and the nonlinear contraction factor, and the second coefficient vector may be determined from the second random number; an improved location update formula in a whale optimization algorithm is constructed based on the first coefficient vector, the second coefficient vector, and the adaptive weights.
In one example, a first coefficient vectorThe calculation formula of (2) is as follows:
；
in one example, the second coefficient vectorThe calculation formula of (2) is as follows:
；
wherein,is->Random numbers in between.
In practical application, the first coefficient vectorSecond coefficient vector->Step size, first coefficient vector, mainly serving to control whale position update>Mainly controlling the step size between the next generation and the current generation, the second coefficient vector +.>Mainly controlling the distance step between the current generation and the optimal individual in the current generation.
And step 2, updating the initial path planning scheme by improving a position updating formula, and updating the updated initial path planning scheme by improving the speed and the position updating formula to obtain an updated path planning scheme corresponding to the current iteration times.
In concrete implementation, the mixed GSA-WOA (for short of the method provided by the embodiment of the invention) is a combination of the gravity search algorithm and the whale optimization algorithm, and by adding nonlinear contraction factors into the two algorithms at the same time, the performance of the two algorithms is improved, so that the optimizing capability of the algorithm is more outstanding.
In one example, when the step of updating the initial path planning scheme by improving the location update formula may be performed, the following steps a to c may be referred to:
Step a, under the condition that the probability of the predation mechanism is larger than or equal to a first preset value, updating an initial path planning scheme by adopting an improved position updating formula in a bubble attack mode; wherein, the probability of predation mechanism is a random number between 0 and 1, and the improved position updating formula under the bubble attack mode is as follows:
；
；
wherein,is->Second iteration->Position of whale individual->Is->Position of optimal whale individual at several iterations, < ->Is->Second iteration->Whale onlyPosition of body->Is->Distance between the position of the whale individual alone and the position of the optimal whale individual, which is used for characterizing the updated initial path planning scheme,/for>For the number of iterations->Is adaptive weight, ++>Is constant (I)>Defines the shape of a logarithmic spiral, +.>Is->Random number of->Is the first coefficient vector.
For example, inIn the case of the air bubble attack mode, an improved position updating formula is adopted to update the initial path planning scheme.
Step b, under the condition that the probability of the predation mechanism is smaller than a first preset value and the first coefficient vector in the whale optimization algorithm is larger than or equal to a second preset value, updating the initial path planning scheme by adopting an improved position updating formula in a predation searching mode, wherein the improved position updating formula in the predation searching mode is shown as follows:
；
；
Wherein,is->Second iteration->Position of whale individual->Is->Second iteration->Position of whale individual->Is->Position of any whale individual at the next iteration,/->Is->Distance between the position of the whale individual to the position of any whale individual, which is used to characterize the updated initial path planning scheme,/for example>For the number of iterations->Is adaptive weight, ++>For the first coefficient vector, +.>Is the second coefficient vector.
For example, the number of the cells to be processed,and->In the case of (1), the initial path planning scheme is updated using an improved location update formula in the search predation mode.
And c, under the condition that the probability of the predation mechanism is smaller than a first preset value and the first coefficient vector is smaller than a second preset value, updating the initial path planning scheme by adopting an improved position updating formula in a surrounding prey mode, wherein the improved position updating formula in the surrounding prey mode is as follows:
；
；
wherein,is->Second iteration->Position of whale individual->Is->Position of optimal whale individual at several iterations, < ->Is->Second iteration->Position of whale individual->Is->Distance between the position of the whale individual alone and the position of the optimal whale individual, which is used for characterizing the updated initial path planning scheme,/for >For the number of iterations->Is adaptive weight, ++>Is the first coefficient vector.
For example, inAnd->In the case of the surrounding prey, the initial path planning scheme is updated by using an improved location update formula.
Further, after updating with the modified location update formula, it is necessary to continue updating with the modified velocity and location update formula, which is shown below:
；
+/>；
wherein,is->Second iteration->Speed of whale individual in d-dimensional space, < >>Is adaptive weight, ++>Is->Second iteration->Speed of whale individual in d-dimensional space, < >>For the correlation coefficient +.>In the form of a random number,is->Position of optimal whale individual at several iterations, < ->Is->Second iteration->Position of whale individual in d-dimensional space, < >>Is->Second iteration->Acceleration of whale individual in d-dimensional space, < >>Is->Second iteration->Position of whale individual in d-dimensional space.
In the embodiment of the invention, the cognitive part in the formula is updated by introducing the particle swarm algorithm speed, and the cognitive part is updated by the particle swarm algorithm speedAnd->The value is changed for a plurality of times, so that the 'gravitational rule', 'memory' and the influence capability of the particles in the whole motion process can be changed.
Because the gravitation search algorithm has strong capability of obtaining the optimal solution, but has relatively poor capability of searching space. In contrast to the whale optimization algorithm, the space searching capability is strong, but the capability of solving the optimal solution is limited by the convergence speed. Therefore, the gravity search algorithm is applied in the development stage of the whale optimization algorithm to obtain the complementary effect, so that the capability of obtaining the global optimal solution of the whole algorithm problem is improved.
And 3, updating the fitness value corresponding to the updated path planning scheme through the cooled Metropolis criterion corresponding to the previous iteration times, and cooling the Metropolis criterion by using the fire-reduction rate coefficient after updating the fitness value so as to obtain the cooled Metropolis criterion corresponding to the current iteration times. The fire rate coefficient is determined based on the final number of iterations.
Wherein, in 1953Instead of using a completely defined rule, a significance sampling method is proposed, i.e. accepting new states with probability, called +.>Criteria. The core idea is that when the energy is increased, the energy is received with a certain probability, namely, the energy is sunk into the local optimum, and the local optimum can be jumped out with a certain probability, wherein the energy is changed according to the energy change amount and +. >Decision probability->Is of the size of>This value is dynamic. />
The classical whale optimization algorithm lacks a disturbance mechanism, and has the defects of low convergence speed in the later stage of the algorithm, easiness in sinking into local optimum and the like. After updating the whale individuals in the whale population, the following is followedA criterion that accepts individuals of inferior quality with a certain probability. The probability formula is calculated as follows:
；
wherein,the initial iteration temperature is +.>100./>Is->Optimal path under current iteration of whale only, < ->Is the +.>Optimum path of whale only. The cooling formula after each iteration is as follows: />Wherein->To reduce the fire rate coefficient, 0.99 was set.
The embodiment of the invention introduces an annealing principle in a simulated annealing algorithm, wherein 'temperature' is an abstract concept, represents the probability of the algorithm to accept an inferior solution in the searching process, is used for controlling the randomness degree of algorithm searching, has higher early temperature, leads the algorithm to have larger disturbance at the initial stage of iteration, avoids premature convergence and is in local optimum, and has lower temperature gradually at the later stage of iteration, leads the disturbance to have smaller disturbance, leads the searching process of the algorithm to trend to be deterministic, can be finally converged to a better solution (path planning scheme), and can be considered to be in self-adaptive weight On the basis of the above, the algorithm is further guaranteed to balance global searching and local development, and the aim is to improve the possibility that the algorithm finds a globally optimal solution.
In summary, because the whale optimization algorithm lacks a disturbance mechanism, imbalance between global search and local development is often caused, so that the algorithm is easy to fall into local optimum, global search performance of the whale optimization algorithm is enhanced by introducing the gravitation search algorithm once, after the gravitation search algorithm searches, the obtained individual position receives an inferior solution with a certain probability according to the Metropolis criterion, global and local are further balanced, and the algorithm performance is more outstanding.
The embodiment of the invention better adjusts the global and local searching capacity and searching precision by adopting a new contraction factor, introducing a self-adaptive weight strategy and a Metropolis criterion and fusing a GSA algorithm. The method is improved when solving the vehicle path problem with load constraint so as to better accelerate the convergence speed and the solving precision of the algorithm, thereby better meeting the demands of practical application on efficiency and accuracy.
For easy understanding, the embodiment of the invention also provides a specific application example, which is specifically as follows: assuming 5 trucks, starting from the distribution center, the goods are returned to the distribution center after being distributed to the required customers, each truck has a load capacity of 100 units and 20 customers, and the goods requirements of each customer are different.
Distance of customer to distribution center: 5,8,2,10,3,15,12,6,8,18,4,9,11,10,5,7,12,9,3,13;
customer requirements: 30,40,10,20,30,5,25,35,15,20,30,20,25,30,20,10,40,30,25,35;
the solution according to the algorithm can obtain one possible optimal path:
vehicle 1 route: distribution center- > (3) customer 5- > (5) customer 14- > (8) customer 9- > (3) customer 19- > -distribution center; cargo weight 100, delivery distance 19 (excluding return distance);
vehicle 2 route: distribution center- > (15) customer 6- > (12) customer 17- > (7) customer 16- > (6) customer 8- > (2) customer 3- > -distribution center; cargo weight 100, delivery distance 42 (excluding return distance);
vehicle 3 route: distribution center- > (5) customer 1- > (9) customer 12- > (4) customer 11- > (5) customer 15- > -distribution center; cargo weight 100, delivery distance 23 (excluding return distance);
vehicle 4 route: distribution center- > (8) customer 2- > (9) customer 18- > (11) customer 13- > -distribution center; weight of cargo 95, delivery distance 28 (excluding return distance);
vehicle 5 route: distribution center- > (10) customer 4- > (12) customer 7- > (13) customer 20- > (18) customer 10- > -distribution center; cargo weight 100, delivery distance 53 (excluding return distance).
To determine the optimal path planning scheme, the following steps may be performed: initializing parameters of a whale algorithm; secondly, changing the original contraction factor of the whale optimization algorithm into a nonlinear contraction factor, and introducing self-adaptive weight to obtain an improved position updating formula; thirdly, updating the moving speed and the positions of the GSA population individuals by utilizing an improved position updating formula and introducing an gravitation searching algorithm; (IV) after updating the moving speed and the positions of the GSA population individuals, updating the population and calculating a population fitness value by using Metropolis criterion; (V) judging whether the maximum iteration number is reachedIf the maximum iteration times are reached, outputting the optimal whale individual position, obtaining the optimal whale individual path (namely, the optimal path planning result), and ending the calculation; if the maximum iteration number is not reached, continuing to execute the process of changing the original contraction factor of the whale optimization algorithm into a nonlinear contraction factor, introducing self-adaptive weight to obtain an improved position updating formula, executing downwards until the maximum iteration number is reached, outputting the optimal whale individual position to obtain the optimal whale individual path (namely, the optimal path planning result), and ending the calculation.
The embodiment of the invention better adjusts the global and local searching capacity by adopting a new contraction factor method. The adaptive weight strategy is introduced to well maintain the diversity of the population, so that the convergence and the searching performance of the algorithm are improved. The Metropolis criterion is introduced, global optimizing capability is improved by accepting poorer points with certain probability, and the algorithm is prevented from falling into a local optimal solution. In the whale optimizing algorithm prey searching stage, an improved gravitation searching algorithm is adopted to accelerate the optimizing speed of the algorithm, and the searching range of the population in the solution space is enlarged, so that the searching capacity and convergence accuracy of the whole algorithm are improved.
In a specific embodiment, referring to a flowchart of another multi-strategy collaborative intelligent optimization method for a load-constrained vehicle path shown in fig. 2, the method includes the following steps S202 to S222:
step S202, initializing a whale optimization algorithm, an gravitation search algorithm and related parameters of a mathematical model;
step S204, initially randomly generating whale individual path schemes;
step S206, updating relevant parameters such as a fire-down rate coefficient, an adaptive weight, a first coefficient vector, a second coefficient vector and the like;
step S208, searching solutions by means of bubble attack, surrounding hunting, searching predation and the like;
Step S210, introducing gravitation search to perform global search;
step S212, updating the population by using Metropolis criterion, and receiving inferior solutions with a certain probability;
step S214, calculating a population fitness value and finding an optimal solution;
in step S216, the Metropolis criterion is cooled, that is:；
step S218, the counter is updated, that is:；
step S220, judging whether the iteration number is smaller than the maximum iteration number, i.eThe method comprises the steps of carrying out a first treatment on the surface of the If yes, go to step S206; if not, then step S222 is performed;
step S222, outputting the target path planning scheme.
In order to verify the effectiveness of the foregoing steps S202 to S222, the embodiment of the present invention selects five CVRP examples with different scales from the CVRPLIB for comparison, and further improves the operation result of the whale algorithm by comparing the whale algorithm, wherein the operation times of each problem are selected 10 times to be the minimum value, and the examples are subjected to necessary simulation test simulation.
In which the instance name is shaped as'"represents a data set, and the embodiment of the present invention selects 2 instances from four data, wherein" - ">"indicates the number of nodes in a warehouse,">"indicates the number of vehicles owned by the warehouse. See the optimal solution for the running results of one of the algorithms shown in table 1, the average value for the running results of one of the algorithms shown in table 2, and the test error table for one of the algorithms shown in table 3:
TABLE 1
TABLE 2
TABLE 3 Table 3
By testing the above results using the CVRP Standard example, it is known that the GSA-WOA algorithm gives better solutions than the genetic algorithm and the traditional whale optimization algorithm in all 4 examples of the 4 data sets selected. In Table 3, the GSA-WOA algorithm also yields a better solution than the whale optimization algorithm. From the above results, it can be seen that the GSA-WOA algorithm has relatively excellent solving capability in CVRP. In the table of the average values, the average value of the GSA-WOA algorithm is much smaller than that of the traditional whale optimization algorithm, and the algorithm can be obtained to have good performance in terms of solving capacity and stability.
Referring to a GA distribution trajectory diagram shown in fig. 3, and a GSA-WOA distribution trajectory diagram shown in fig. 4, each route shows client nodes through which each vehicle passes, node No. 0 represents a distribution center from which the vehicle starts and returns to the distribution center. As can be seen from fig. 3 and 4, when there are 40 client points, the vehicle distribution route obtained by solving the CVRP problem by using the GA is 6, the total travel distance of the vehicle is 1575, the vehicle distribution route obtained by the algorithm provided by the embodiment of the present invention is 5, and the total travel distance of the vehicle is 1083, which is more reasonable than the distribution scheme obtained by the GSAWOA algorithm of the GA algorithm.
When 60 client points exist, the number of the vehicle delivery routes obtained by solving the CVRP problem through the GA is 6, the total running distance of the vehicles is 2578, the number of the vehicle delivery routes obtained through the algorithm provided by the embodiment of the invention is 5, and the total running distance of the vehicles is 1926, and as the number of the client demand points increases, the advantage of solving the delivery routes through the algorithm provided by the embodiment of the invention is more and more obvious in the future, and compared with the delivery scheme obtained through the GSA-WOA algorithm of the GA algorithm, the method provided by the embodiment of the invention is more reasonable.
Experimental results show that combining Whale Optimization Algorithm (WOA) with Gravity Search Algorithm (GSA) can be effectively applied to solving the vehicle path problem (CVRP) with load constraints. The hybrid algorithm performs well in terms of both objective function values and capacity constraint violations.
The embodiment of the invention combines a Whale Optimization Algorithm (WOA), a Simulated Annealing (SA) and a Gravity Search Algorithm (GSA) to be applied to the problem of vehicle paths with load constraint. By the method, the cost is reduced, the efficiency is improved, and various constraint conditions are met. The method can effectively solve the problem of vehicle path constraint with capacity, has stronger optimizing capability, proves the effectiveness and feasibility of the algorithm, and has better solving quality than the compared algorithm.
The embodiment of the invention improves the basic whale optimization algorithm, and introduces the self-adaptive weight strategy to well maintain the diversity of the population, so that the convergence and the searching performance of the algorithm are improved. The gravity search algorithm is introduced to enhance the search capability of the whale optimization algorithm, the Metropolis criterion is introduced, the search precision is improved by accepting the worse points with a certain probability, the balance of the algorithm between the development stage and the exploration stage is better realized, and the algorithm is prevented from falling into a local optimal solution. Experimental results show that the whale optimization algorithm with the cooperation of multiple strategies can effectively jump out of local optimum, the stability is higher, and the obtained result is more accurate.
On the basis of the foregoing embodiments, the embodiment of the present invention provides a multi-strategy collaborative intelligent optimization apparatus for a vehicle path with load constraint, and referring to a schematic structural diagram of the multi-strategy collaborative intelligent optimization apparatus for a vehicle path with load constraint shown in fig. 5, the apparatus mainly includes the following parts:
an obtaining module 502, configured to obtain a path start point, a path end point, and a plurality of target points to be planned;
an initialization module 504, configured to initialize parameters of a whale optimization algorithm based on a path start point, a path end point, and a target point, so as to obtain an initial path planning scheme;
The path planning module 506 is configured to update the initial path planning scheme sequentially through an improved position updating formula in the whale optimization algorithm, an improved speed in the gravity search algorithm, and an improved position updating formula in the gravity search algorithm, so as to obtain an updated path planning scheme corresponding to the current iteration number and a corresponding fitness value thereof; the improved position updating formula is constructed based on the nonlinear contraction factor and the self-adaptive weight, the improved speed and the position updating formula are constructed based on the self-adaptive weight, and the fitness value is used for evaluating the quality of the updated path planning scheme;
the path planning module 506 is further configured to continuously update the updated path planning scheme corresponding to the current iteration number until a preset iteration stop condition is met, thereby obtaining a target path planning scheme; the vehicles start from the path starting point, sequentially travel to each target point according to the target path planning scheme, deliver cargoes to the target points, return to the path ending point, and the number of the vehicles is at least one.
The multi-strategy collaborative intelligent optimization device for the vehicle path with load constraint provided by the embodiment of the invention is improved on the basis of the traditional whale optimization algorithm, the global and local searching capacity is well adjusted by introducing the nonlinear contraction factor, the diversity of the population is well maintained by introducing the self-adaptive weight, the convergence and the searching capacity of the algorithm are improved, and the searching capacity of the algorithm is further improved by introducing the gravitation searching algorithm.
In one embodiment, the path planning module 506 is further configured to:
according to the current iteration times and the preset final iteration times, determining a nonlinear contraction factor and an adaptive weight, constructing an improved position updating formula in a whale optimization algorithm based on the nonlinear contraction factor and the adaptive weight, and constructing an improved speed and position updating formula in an gravitation searching algorithm based on the adaptive weight;
updating the initial path planning scheme by improving a position updating formula, and updating the updated initial path planning scheme by improving the speed and the position updating formula to obtain an updated path planning scheme corresponding to the current iteration times;
updating the fitness value corresponding to the updated path planning scheme through the cooled Metropolis criterion corresponding to the previous iteration times, and cooling the Metropolis criterion by using the fire-down rate coefficient after updating the fitness value to obtain the cooled Metropolis criterion corresponding to the current iteration times; wherein the fire rate coefficient is determined based on the final number of iterations.
In one embodiment, the path planning module 506 is further configured to:
the nonlinear contraction factor is calculated according to the following formula:
；
The adaptive weights are calculated according to the following formula:
；
wherein,is nonlinear contraction factor->Is adaptive weight, ++>For the final iteration number>For the current iteration number>Is a correlation coefficient.
In one embodiment, the path planning module 506 is further configured to:
determining a first coefficient vector according to the first random number and the nonlinear contraction factor, and determining a second coefficient vector according to the second random number;
an improved location update formula in a whale optimization algorithm is constructed based on the first coefficient vector, the second coefficient vector, and the adaptive weights.
In one embodiment, the path planning module 506 is further configured to:
under the condition that the probability of the predation mechanism is larger than or equal to a first preset value, updating an initial path planning scheme by adopting an improved position updating formula in a bubble attack mode; wherein, the probability of predation mechanism is a random number between 0 and 1, and the improved position updating formula under the bubble attack mode is as follows:
；
；
under the condition that the probability of the predation mechanism is smaller than a first preset value and the first coefficient vector in the whale optimization algorithm is larger than or equal to a second preset value, updating the initial path planning scheme by adopting an improved position updating formula in a predation searching mode, wherein the improved position updating formula in the predation searching mode is as follows:
；
；
Under the condition that the probability of the predation mechanism is smaller than a first preset value and the first coefficient vector is smaller than a second preset value, updating the initial path planning scheme by adopting an improved position updating formula in a surrounding prey mode, wherein the improved position updating formula in the surrounding prey mode is as follows:
；
；
wherein,is->Second iteration->Position of whale individual->Is->Position of optimal whale individual at several iterations, < ->Is->Second iteration->Position of whale individual->Is->Position of any whale individual at the next iteration,/->、/>All are->Distance between the position of the whale individual alone and the position of the optimal whale individual,/i>Is->Distance between the position of the whale individual to the position of any whale individual, which is used to characterize the updated initial path planning scheme,/for example>For the number of iterations->Is adaptive weight, ++>Is constant (I)>Defines the shape of a logarithmic spiral, +.>Is a random number +.>For the first coefficient vector, +.>Is the second coefficient vector.
In one embodiment, the improvement speed and location update formula is as follows:
；
+/>；
wherein,is->Second iteration->Speed of whale individual in d-dimensional space, < > >Is adaptive weight, ++>Is->Second iteration->Speed of whale individual in d-dimensional space, < >>For the correlation coefficient +.>In the form of a random number,is->Position of optimal whale individual at several iterations, < ->Is->Second iteration->Position of whale individual in d-dimensional space, < >>Is->Second iteration->Acceleration of whale individual in d-dimensional space, < >>Is->Second iteration->Position of whale individual in d-dimensional space.
In one embodiment, the initialization module 504 is further configured to:
initializing parameters of a whale optimization algorithm based on a path starting point, a path ending point and a target point to determine a plurality of candidate path planning schemes and corresponding fitness values thereof;
and comparing the fitness value corresponding to each candidate path planning scheme to determine an initial path planning from the candidate path planning schemes based on the comparison result.
The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
The embodiment of the invention provides electronic equipment, which comprises a processor and a storage device; the storage means has stored thereon a computer program which, when executed by the processor, performs the method of any of the embodiments described above.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device 100 includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, the processor 60, the communication interface 63 and the memory 61 being connected by the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The memory 61 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is achieved via at least one communication interface 63 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 62 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 6, but not only one bus or type of bus.
The memory 61 is configured to store a program, and the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus for flow defining disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60 or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in the processor 60. The processor 60 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 61 and the processor 60 reads the information in the memory 61 and in combination with its hardware performs the steps of the method described above.
The computer program product of the readable storage medium provided by the embodiment of the present invention includes a computer readable storage medium storing a program code, where the program code includes instructions for executing the method described in the foregoing method embodiment, and the specific implementation may refer to the foregoing method embodiment and will not be described herein.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The multi-strategy collaborative intelligent optimization method for the vehicle path with load constraint is characterized by comprising the following steps of:
acquiring a path starting point, a path ending point and a plurality of target points to be planned;
initializing parameters of a whale optimization algorithm based on the path starting point, the path ending point and the target point to obtain an initial path planning scheme;
Updating the initial path planning scheme sequentially through an improved position updating formula in the whale optimizing algorithm, an improved speed in the gravity searching algorithm and a position updating formula to obtain an updated path planning scheme corresponding to the current iteration times and a corresponding fitness value of the updated path planning scheme; the improved position updating formula is constructed based on a nonlinear contraction factor and an adaptive weight, the improved speed and position updating formula is constructed based on the adaptive weight, and the fitness value is used for evaluating the quality of the updated path planning scheme;
continuing to update the updated path planning scheme corresponding to the current iteration times until a preset iteration stop condition is met, so as to obtain a target path planning scheme; starting from the path starting point, vehicles sequentially travel to each target point according to the target path planning scheme, deliver cargoes to the target points and return to the path ending point, wherein the number of the vehicles is at least one.
2. The multi-strategy collaborative intelligent optimization method for restricting a vehicle path with load according to claim 1, wherein the step of updating the initial path planning scheme to obtain an updated path planning scheme corresponding to the current iteration number and a corresponding fitness value thereof sequentially through an improved position updating formula in the whale optimizing algorithm, an improved speed in the gravity searching algorithm and a position updating formula comprises the steps of:
Determining a nonlinear contraction factor and an adaptive weight according to the current iteration number and the preset final iteration number, so as to construct an improved position updating formula in the whale optimizing algorithm based on the nonlinear contraction factor and the adaptive weight, and constructing an improved speed and position updating formula in an gravitation searching algorithm based on the adaptive weight;
updating the initial path planning scheme through the improved position updating formula, and updating the updated initial path planning scheme through the improved speed and the position updating formula to obtain an updated path planning scheme corresponding to the current iteration times;
updating the fitness value corresponding to the updated path planning scheme through the cooled Metropolis criterion corresponding to the previous iteration times, and cooling the Metropolis criterion by using a fire-reduction rate coefficient after updating the fitness value so as to obtain the cooled Metropolis criterion corresponding to the current iteration times; wherein the fire rate coefficient is determined based on the final number of iterations.
3. The multi-strategy collaborative intelligent optimization method of a load-constrained vehicle path according to claim 2, wherein the step of determining a nonlinear contraction factor and an adaptive weight based on a current number of iterations and a preset final number of iterations comprises:
The nonlinear contraction factor is calculated according to the following formula:
；
the adaptive weights are calculated according to the following formula:
；
wherein,is nonlinear contraction factor->Is adaptive weight, ++>For the final iteration number>For the current iteration number>Is a correlation coefficient.
4. The multi-strategy collaborative intelligent optimization method of a load-constrained vehicle path according to claim 2, wherein the step of constructing an improved location update formula in the whale optimization algorithm based on the nonlinear contraction factor and the adaptive weights includes:
determining a first coefficient vector according to the first random number and the nonlinear contraction factor, and determining a second coefficient vector according to the second random number;
an improved location update formula in the whale optimization algorithm is constructed based on the first coefficient vector, the second coefficient vector, and the adaptive weights.
5. The multi-strategy collaborative intelligent optimization method for a load-constrained vehicle path according to claim 2, wherein the step of updating the initial path planning scheme via the improved location update formula comprises:
under the condition that the probability of the predation mechanism is larger than or equal to a first preset value, updating the initial path planning scheme by adopting an improved position updating formula in a bubble attack mode; wherein, the probability of the predation mechanism is a random number between 0 and 1, and the improved position updating formula under the bubble attack mode is as follows:
；
；
Under the condition that the probability of the predation mechanism is smaller than the first preset value and the first coefficient vector in the whale optimization algorithm is larger than or equal to the second preset value, updating the initial path planning scheme by adopting an improved position updating formula in a search predation mode, wherein the improved position updating formula in the search predation mode is as follows:
；
；
and under the condition that the probability of predation mechanism is smaller than the first preset value and the first coefficient vector is smaller than the second preset value, updating the initial path planning scheme by adopting an improved position updating formula in a surrounding prey mode, wherein the improved position updating formula in the surrounding prey mode is as follows:
；
；
wherein,is->Second iteration->Position of whale individual->Is->Position of optimal whale individual at several iterations, < ->Is->Second iteration->Position of whale individual->Is->Position of any whale individual at the next iteration,/->、/>All are->Distance between the position of the whale individual alone and the position of the optimal whale individual,/i>Is->Distance between the position of the whale individual to the position of any whale individual, which is used to characterize the updated initial path planning scheme,/for example >For the number of iterations->Is adaptive weight, ++>Is constant (I)>Defines the shape of a logarithmic spiral, +.>Is a random number +.>For the first coefficient vector, +.>Is the second coefficient vector.
6. The multi-strategy collaborative intelligent optimization method for a load-constrained vehicle path according to claim 2, wherein the improved speed and location update formula is as follows:
；
+/>；
wherein,is->Second iteration->Speed of whale individual in d-dimensional space, < >>Is adaptive weight, ++>Is->Second iteration->Speed of whale individual in d-dimensional space, < >>For the correlation coefficient +.>In the form of a random number,is->Position of optimal whale individual at several iterations, < ->Is->Second iteration->Position of whale individual in d-dimensional space, < >>Is->Second iteration->Acceleration of whale individual in d-dimensional space, < >>Is->Second iteration->Position of whale individual in d-dimensional space.
7. The multi-strategy collaborative intelligent optimization method for a load-constrained vehicle path according to claim 1, wherein initializing parameters of a whale optimization algorithm based on the path start point, the path end point, and the target point to obtain an initial path planning scheme comprises:
Initializing parameters of a whale optimization algorithm based on the path starting point, the path ending point and the target point to determine a plurality of candidate path planning schemes and corresponding fitness values thereof;
and comparing the fitness value corresponding to each candidate path planning scheme to determine an initial path planning from the candidate path planning schemes based on the comparison result.
8. A multi-strategy collaborative intelligent optimization device for constraining a vehicle path with a load, comprising:
the acquisition module is used for acquiring a path starting point, a path ending point and a plurality of target points to be planned;
the initialization module is used for initializing parameters of a whale optimization algorithm based on the path starting point, the path ending point and the target point so as to obtain an initial path planning scheme;
the path planning module is used for updating the initial path planning scheme sequentially through an improved position updating formula in the whale optimizing algorithm, an improved speed in the gravity searching algorithm and a position updating formula so as to obtain an updated path planning scheme corresponding to the current iteration times and a corresponding fitness value of the updated path planning scheme; the improved position updating formula is constructed based on a nonlinear contraction factor and an adaptive weight, the improved speed and position updating formula is constructed based on the adaptive weight, and the fitness value is used for evaluating the quality of the updated path planning scheme;
The path planning module is further used for continuously updating the updated path planning scheme corresponding to the current iteration times until a preset iteration stop condition is met, so as to obtain a target path planning scheme; starting from the path starting point, vehicles sequentially travel to each target point according to the target path planning scheme, deliver cargoes to the target points and return to the path ending point, wherein the number of the vehicles is at least one.
9. An electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of claims 1 to 7.
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