CN113705879A - Multi-yard multi-vehicle type vehicle path planning method - Google Patents
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Abstract
The invention relates to a multi-parking lot multi-vehicle type vehicle path planning method, which comprises the following steps: acquiring vehicle information and customer information, and determining a mixed code; determining an initial feasible solution according to the mixed codes, reserving iteration elite individuals, updating an pheromone matrix, and carrying out individual screening according to individual target values of the iteration elite individuals to form a screened elite population; based on the conditions, improving by utilizing a two-side successive correction algorithm to determine improved individuals, and adding the improved individuals to the screened elite population until the initial population scale is reached; and (3) performing cross variation on the individuals by adopting a fixed-point cross algorithm, selecting orthogonal experiment parameters, optimizing the algorithm calculation capacity, and outputting an iterative optimal solution. According to the method, on the basis of an initial feasible solution, an improved pheromone updating strategy and an elite population strategy are combined, a two-side successive correction algorithm is used for improvement, a fixed-point cross algorithm is used for carrying out cross variation on individuals, the search space is expanded, the convergence speed is improved, and the calculation performance is optimized.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a multi-vehicle-yard multi-vehicle-type vehicle path planning method.
Background
In logistics enterprises, the problem of vehicle routes has great significance for enterprise development, and the logistics cost is high or low and the transportation efficiency is high or low. In practical application, many factors, such as parking lot vehicle types, customer demands, time sequence and the like, need to be considered in reasonable vehicle path planning, and with the development of the logistics industry, the distribution scale is continuously increased, the path planning under the traditional single-parking lot single-vehicle type scene obviously becomes less suitable for the real situation, and joint transportation of a plurality of distribution centers and distribution by adopting different vehicle types are more common in the real life. However, the vehicle scheduling and customer service sequence of multiple vehicle yards and multiple vehicle models needs to consider more factors, and the solution is more complex and tedious. In the prior art, a deep learning network or a particle swarm algorithm is directly called to be applied to the scene, so that the defects in multiple aspects exist, on one hand, the connection between a client time window and space-time congestion is not fully considered, and the algorithm has poor universality; on the other hand, the solving speed is slow, so that the real-time applicability of the algorithm is poor. Therefore, how to improve the efficiency and the rapidity of path planning in a complex scene is an urgent problem to be solved.
Disclosure of Invention
In view of this, it is necessary to provide a multi-yard multi-vehicle type vehicle path planning method for overcoming the defects of poor path planning universality and slow solution speed in the complex scene in the prior art.
In order to solve the technical problem, the invention provides a multi-yard multi-vehicle type vehicle path planning method, which comprises the following steps:
acquiring vehicle information and customer information of different parking lots, performing corresponding coding, and determining a mixed code;
determining a corresponding initial feasible solution according to the hybrid coding mechanism and the improved ant colony algorithm, updating pheromones by adopting an improved pheromone updating strategy, performing descending order arrangement on target values of the initial populations based on the initial populations, and screening to form elite populations;
determining an individual to be improved based on a roulette method, improving the elite individual by utilizing a two-side successive correction algorithm, and adding the improved individual to the screened elite population until the initial population scale is reached;
based on the cross rate and the variation rate selected by the orthogonal parameters, selecting individuals needing cross variation by using a roulette method, carrying out cross variation on the individuals by using a fixed-point cross algorithm, selecting orthogonal experimental parameters to continuously optimize the performance of the algorithm until preset conditions are met, and outputting an iterative optimal solution.
Further, the acquiring vehicle information and customer information of different yards and performing corresponding encoding, wherein the determining of the hybrid encoding includes:
according to the vehicle information, a three-digit code is adopted to form a vehicle number, wherein the first digit code is used for preventing the code from being repeated with a customer code, the second digit code is used for representing the position of a parking lot, and the third digit code is used for representing the vehicle type;
and forming a customer set among different vehicle numbers according to the customer information, wherein the customer set is used for representing the service sequence with the previous vehicle.
Further, the determining the corresponding initial feasible solution according to the hybrid coding mechanism and the improved ant colony algorithm includes:
randomly selecting a vehicle serving the customer according to the hybrid code;
when the corresponding client set is not an empty set, determining the corresponding state transition probability according to the pheromone intensity from the current vehicle position to the client which is not accessed, and selecting the next client to be served by adopting a roulette method;
and judging whether the corresponding vehicle mileage meets a preset mileage condition, if so, outputting the planned path, and calculating an overall target value corresponding to the planned path.
Further, the updating the pheromone by using the improved pheromone updating strategy comprises the following steps:
according to the elite reservation strategy, reserving the elite individuals subjected to the last iterative screening to enter the next generation;
and recording the optimal solution of the iteration, updating the corresponding pheromone matrix, and clearing the corresponding tabu list.
Further, the updating the corresponding pheromone matrix comprises:
besides pheromones left on the road sections by common ants, a preset number of pheromones are manually applied to each road section on the current optimal path;
wherein, the mode of pheromone updating is realized by the following formula
τij＝(1-ρ)τij+Δτij
Where M represents the total number of customers, τijIndicates the intensity of pheromone on the road section between the ith client and the jth client, p indicates the volatilization speed of pheromone, and delta tauijIndicating the amount of change in intensity of the pheromone,
representing the artificial pheromone left by the person on the road section between the ith customer and the jth customer, e representing the intensity of the manual intervention, Q representing the intensity of the pheromone, TbsA target value representing the current optimal path.
Further, the sorting the target values in descending order based on the initial population, and the screening to form the elite population comprises:
sorting in descending order according to the individual target values of the iteration elite individuals;
and according to an elite population strategy, selecting the top 10% of sorted elite individuals as screened elite individuals to form the screened elite population.
Further, the elite population strategy comprises:
selecting the first 10% of individuals after the population fitness value is subjected to descending order arrangement after preliminary optimization as an elite population;
calculating the probability of selecting an individual according to the weight of the target value in the elite population to the sum of the target values;
wherein the probability that the individual is selected is represented by the following formula:
wherein p isiRepresenting the probability of the individual being selected, fiAnd (3) representing the fitness value of the ith elite individual in the elite population, and S represents the set of the current elite population.
Further, the determining an individual to be improved based on roulette method, and the improving the elite individual by using the two-side successive correction algorithm includes:
determining the selection probability of the elite population according to the target value of the elite population;
based on the roulette method elite individual, the improvement is carried out by utilizing a two-edge successive correction algorithm, wherein the two-edge successive correction algorithm comprises the following steps:
obtaining an improved path needing improvement;
according to the service sequence, selecting potential improvement points, and turning paths among the potential improvement points to form a new path;
when all the improved points are traversed, judging whether all the improved points are vehicles;
if not, calculating the vehicle travel and load of the new path, and judging whether the vehicle travel or load in the new path has a vehicle exceeding a corresponding rated value;
if not, calculating the path length and the customer average satisfaction corresponding to the improved path and the new path, and judging whether preset output conditions are met according to the corresponding path length and the customer average satisfaction;
if the path is not satisfied, replacing the improved path with the new path, and returning to the step of acquiring the improved path needing to be improved.
Further, the performing cross mutation on the individual by using the fixed-point cross algorithm includes:
randomly selecting two individuals from the improved elite population, randomly selecting corresponding individual genes, and exchanging the individual genes to other positions of the individuals of the other party to form a corresponding new path;
judging whether the new path has a vehicle exceeding the rated load and the maximum vehicle travel, if so, returning to the step of exchanging the individual gene to other positions of the opposite individual;
and if not, outputting the corresponding new path.
Further, the selecting orthogonal experiment parameters to continuously optimize the performance of the algorithm until a preset condition is met, and outputting an iterative optimal solution comprises:
selecting a set of the orthogonal experimental parameters to apply to the current iteration by using a roulette method;
judging whether the average level of the current elite population is improved;
if so, recording the current optimal parameter combination and the population optimal solution, and if not, increasing the performance value of the current orthogonal experiment parameter by a preset value.
Compared with the prior art, the invention has the beneficial effects that: firstly, effectively acquiring vehicle information and customer information, and coding the vehicle information and the customer information so as to facilitate the application of a subsequent optimization algorithm; then, based on the initial feasible solution, carrying out screening of the elite individuals, updating pheromone, further carrying out screening according to individual target values of the iterative elite individuals, ensuring the convergence rate of the algorithm through multiple times of elite population screening, and continuing the excellence in the population evolution process; then, based on the formed screening elite population, individual screening is carried out according to the target value of the elite population, and improvement is carried out by combining a successive correction algorithm on two sides, so that the improved individuals are supplemented to the screened elite population, thereby accelerating the convergence speed of the algorithm, improving the accuracy of the algorithm and further continuing the excellence in the population evolution process; and finally, combining a fixed-point crossing algorithm to carry out individual crossing variation, reducing invalid calculation caused in the crossing and variation processes, selecting optimal orthogonal experiment parameters until an iterative optimal solution is solved, improving the solving performance and generalization capability of the algorithm, and ensuring the accuracy and real-time performance of path planning. According to the method, on the basis of an initial feasible solution, pheromone updating and elite population strategies are combined, a successive correction algorithm on two sides is utilized for improvement, a fixed-point cross algorithm is adopted for carrying out cross variation on individuals, invalid calculation is reduced, meanwhile, an optimal parameter is selected by combining orthogonal experiment parameters, the convergence speed of the algorithm is improved, the performance of the algorithm is optimized, an iterative optimal solution is output, and the reasonability, the efficiency and the real-time performance of path planning under a complex scene are guaranteed.
Drawings
Fig. 1 is a scene schematic diagram of an embodiment of an application system of a multi-yard multi-vehicle type vehicle path planning method provided by the present invention;
FIG. 2 is a schematic flow chart illustrating an embodiment of a multi-yard multi-vehicle type vehicle path planning method according to the present invention;
FIG. 3 is a flowchart illustrating an embodiment of step S1 in FIG. 2 according to the present invention;
FIG. 4 is a schematic encoding diagram of an embodiment of a hybrid encoding scheme provided by the present invention;
FIG. 5 is a flowchart illustrating an embodiment of determining an initial feasible solution at step S2 in FIG. 2 according to the present invention;
FIG. 6 is a flowchart illustrating an embodiment of the pheromone update of step S2 in FIG. 2 according to the present invention;
FIG. 7 is a schematic flow chart illustrating an embodiment of the present invention for forming the screened elite population in step S2 in FIG. 2;
FIG. 8 is a flowchart illustrating an embodiment of step S3 in FIG. 2 according to the present invention;
FIG. 9 is a schematic flow chart of an embodiment of a two-edge successive correction algorithm provided by the present invention;
FIG. 10 is a flowchart illustrating an embodiment of the fixed point crossing algorithm in step S4 of FIG. 2 according to the present invention;
fig. 11 is a schematic flowchart of an embodiment of selecting orthogonal experimental parameters in step S4 in fig. 2 according to the present invention;
FIG. 12 is a graph illustrating an example of an improved ant colony-genetic algorithm iterative evolutionary graph according to the present invention;
fig. 13 is a schematic structural diagram of an embodiment of a multi-yard multi-vehicle type vehicle path planning apparatus provided by the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Further, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Reference throughout this specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the described embodiments can be combined with other embodiments.
The invention provides a multi-parking lot and multi-vehicle type vehicle path planning method which is applied to path planning in a complex scene, continues the excellence in the population evolution process according to various improved algorithms and provides a new idea for further improving the high efficiency and the rapidity of the path planning in the complex scene. The following are detailed below:
an embodiment of the present invention provides an application system of a multi-yard multi-vehicle type vehicle path planning method, and fig. 1 is a scene schematic diagram of an embodiment of the application system of the multi-yard multi-vehicle type vehicle path planning method provided by the present invention, where the system may include a server 100, and a multi-yard multi-vehicle type vehicle path planning device, such as the server in fig. 1, is integrated in the server 100.
The server 100 in the embodiment of the present invention is mainly used for:
acquiring vehicle information and customer information of different parking lots, performing corresponding coding, and determining a mixed code;
determining a corresponding initial feasible solution according to the mixed codes, reserving a corresponding iteration elite individual, updating an pheromone matrix, and carrying out individual screening according to an individual target value of the iteration elite individual to form a screened elite population;
carrying out individual screening according to the elite population target value corresponding to the screened elite individual, improving by utilizing a successive correction algorithm on two sides, determining an improved individual, and adding the improved individual to the screened elite population until the initial population scale is reached;
and performing cross variation on the individuals by adopting a fixed-point cross algorithm, selecting orthogonal experiment parameters until preset conditions are met, and outputting an iterative optimal solution.
In this embodiment of the present invention, the server 100 may be an independent server, or may be a server network or a server cluster composed of servers, for example, the server 100 described in this embodiment of the present invention includes, but is not limited to, a computer, a network host, a single network server, a plurality of network server sets, or a cloud server composed of a plurality of servers. Among them, the Cloud server is constituted by a large number of computers or web servers based on Cloud Computing (Cloud Computing).
It is to be understood that the terminal 200 used in the embodiments of the present invention may be a device that includes both receiving and transmitting hardware, i.e., a device having receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device having a single line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific terminal 200 may be a desktop, a laptop, a web server, a Personal Digital Assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, an embedded device, and the like, and the type of the terminal 200 is not limited in this embodiment.
Those skilled in the art can understand that the application environment shown in fig. 1 is only one application scenario corresponding to the present invention, and does not constitute a limitation on the application scenario of the present invention, and that other application environments may further include more or fewer terminals than those shown in fig. 1, for example, only 2 terminals are shown in fig. 1, and it can be understood that the application system of the multi-vehicle yard and multi-vehicle type vehicle path planning method may further include one or more other terminals, which is not limited herein.
In addition, as shown in fig. 1, the application system of the multi-yard multi-vehicle type vehicle path planning method may further include a memory 200 for storing data, such as vehicle information, customer information, elite population screening, iterative optimal solution and the like.
It should be noted that the scene schematic diagram of the application system of the multi-yard multi-vehicle type vehicle path planning method shown in fig. 1 is merely an example, the application system and the scene of the multi-yard multi-vehicle type vehicle path planning method described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present invention, and do not constitute a limitation on the technical solution provided in the embodiment of the present invention, and it can be known by those skilled in the art that the technical solution provided in the embodiment of the present invention is also applicable to similar technical problems along with the evolution of the application system of the multi-yard multi-vehicle type vehicle path planning method and the appearance of a new service scene.
An embodiment of the present invention provides a method for planning a path of a multi-yard multi-vehicle type vehicle, and referring to fig. 2, fig. 2 is a schematic flow chart of an embodiment of the method for planning a path of a multi-yard multi-vehicle type vehicle provided by the present invention, and the method includes steps S1 to S4, where:
in step S1, vehicle information and customer information of different parking lots are obtained, corresponding codes are performed, and a hybrid code is determined
In step S2, determining a corresponding initial feasible solution according to the hybrid coding mechanism and the improved ant colony algorithm, updating pheromones by using an improved pheromone updating strategy, sorting the target values in a descending order based on the initial population, and screening to form elite populations;
in step S3, determining an individual to be improved based on a roulette method, improving the elite individual using a two-side successive correction algorithm, and adding the improved individual to the screened elite population until an initial population size is reached;
in step S4, based on the intersection rate and the variation rate selected by the orthogonal parameters, individuals requiring cross variation are selected by using a roulette method, cross variation is performed on the individuals by using a fixed-point cross algorithm, and orthogonal experimental parameters are selected to continuously optimize the performance of the algorithm until a preset condition is satisfied, and an iterative optimal solution is output.
In the embodiment of the invention, firstly, the vehicle information and the customer information are effectively acquired and coded so as to facilitate the application of the subsequent optimization algorithm; then, based on the initial feasible solution, carrying out screening of the elite individuals, updating pheromone, further carrying out screening according to individual target values of the iterative elite individuals, ensuring the convergence rate of the algorithm through multiple times of elite population screening, and continuing the excellence in the population evolution process; then, based on the formed screening elite population, individual screening is carried out according to the target value of the elite population, and improvement is carried out by combining a successive correction algorithm on two sides, so that the improved individuals are supplemented to the screened elite population, thereby accelerating the convergence speed of the algorithm, improving the accuracy of the algorithm and further continuing the excellence in the population evolution process; finally, the individual cross variation is carried out by combining a fixed point cross algorithm, invalid calculation caused in the process of multi-point or indefinite point cross and variation is reduced, the optimal orthogonal experiment parameters are selected until the iterative optimal solution is solved, the algorithm solving performance and generalization capability are improved, and the accuracy and real-time performance of path planning are ensured.
It should be noted that, the present invention does not well exert the positive feedback effect of the optimal solution in consideration of the conventional pheromone update. An improved ant colony algorithm with manual intervention on pheromone updates is presented. And the solution after the preliminary optimization of the improved ant colony algorithm is used as an improved genetic algorithm initial population embedded into the two-side successive correction algorithm. And (4) providing an elite retention strategy, and retaining the optimal solution of the last iteration to continue the excellence in the population evolution process. And (3) providing an elite population strategy, extracting the elite individuals with the fitness value of 30 percent in the front, and locally improving by using a two-side successive correction algorithm to form the elite population. And a fixed-point crossing algorithm is provided for crossing variation, so that invalid calculation caused in crossing and variation processes is reduced. An orthogonal test design is provided, algorithm parameters are optimized, and algorithm performance is improved.
As a preferred embodiment, referring to fig. 3, fig. 3 is a schematic flowchart of an embodiment of step S1 in fig. 2 provided by the present invention, and includes steps S11 to S12, where:
in step S11, according to the vehicle information, a three-digit code is used to form a vehicle number, wherein the first digit code is used to prevent the duplication with the customer code, the second digit code is used to represent the location of the parking lot, and the third digit code is used to represent the vehicle type;
in step S12, a customer set is formed between different vehicle numbers based on the customer information, wherein the customer set is used for indicating a service order with a previous vehicle.
In the embodiment of the invention, the vehicle information and the customer information are effectively acquired and coded to form a mixed code, so that the subsequent determination of the initial feasible solution is facilitated.
In a specific embodiment of the present invention, referring to fig. 4, fig. 4 is a schematic coding diagram of an embodiment of a hybrid coding pattern provided by the present invention, and an algorithm adopts decimal hybrid coding in consideration of convenience of coding. The vehicle and the client are prevented from being coded repeatedly, and the vehicle adopts three-digit coding. The first digit is used to prevent duplication with customer codes, the second digit is used to represent yard location, and the third digit represents the vehicle model. Such as: and 112, a second vehicle type of the first yard. The set of customers between two vehicles represents the service and sequence of service to the set by the previous vehicle.
As a preferred embodiment, referring to fig. 5, fig. 5 is a schematic flowchart of an embodiment of determining an initial feasible solution in step S2 in fig. 2, provided by the present invention, and includes steps S21 to S23, where:
in step S21, a vehicle serving the customer is randomly selected based on the hybrid code;
in step S22, when the corresponding client set is not an empty set, determining a corresponding state transition probability according to the intensity of pheromones from the current vehicle position to the non-visited client, and selecting the next served client by using a roulette method;
in step S23, it is determined whether the corresponding vehicle mileage satisfies a preset mileage condition, and if so, the planned route is output, and an overall target value corresponding to the planned route is calculated.
In the embodiment of the invention, based on mixed coding, the initial feasible solution is solved by combining the state transition probability and the roulette method, and the corresponding overall target value is calculated so as to facilitate the subsequent elite population screening.
In a specific embodiment of the present invention, the solution process of the initial feasible solution is as follows:
the first step, randomly selecting a vehicle serving a customer, and turning to the second step;
secondly, when the client set is not an empty set, calculating the corresponding state transition probability according to the distance from the current vehicle position to the client which is not visited and the intensity of the road section pheromone, selecting the next client to be served by adopting a roulette method, and turning to the third step; otherwise, turning to the fourth step;
thirdly, judging whether the mileage of the vehicle can return to the parking lot and whether the capacity is remained, and if so, turning to the second step; otherwise, turning to the first step;
and fourthly, outputting a path, calculating the average customer satisfaction, the number of vehicles and the path length, and calculating an overall target value.
As a preferred embodiment, referring to fig. 6, fig. 6 is a schematic flow chart of an embodiment of the pheromone update of step S2 in fig. 2 provided by the present invention, and the schematic flow chart includes steps S24 to S25, where:
in step S24, the elite individuals iteratively screened last time are retained to enter the next generation according to the elite retention policy;
in step S25, the optimal solution of the current iteration is recorded, the corresponding pheromone matrix is updated, and the corresponding tabu table is cleared.
In the embodiment of the invention, the excellence of the previous generation population is kept according to the elite retention strategy, and the pheromone is updated, so that the next iterative screening is convenient to carry out.
As a preferred embodiment, the state transition probability is expressed by the following formula:
wherein p isijRepresenting the probability of a vehicle from customer i to customer j, τijIntensity of pheromones representing road segments between client i and client J, J representing a set of currently unvisited clients, ηijRepresenting a distance heuristic factor, DijIndicating the distance between clients i and j, and alpha, beta indicating the relative weight of the pheromone and the desired heuristic factor. From the set of customers that are not currently being serviced, the next customer to be serviced is selected based on the customer state transition improvement and roulette.
As a preferred embodiment, the updating the corresponding pheromone matrix includes:
besides pheromones left on the road sections by common ants, a preset number of pheromones are manually applied to each road section on the current optimal path;
wherein, the mode of pheromone updating is realized by the following formula
τij＝(1-ρ)τij+Δτij
Where M represents the total number of customers, τijIndicates the intensity of pheromone on the road section between the ith client and the jth client, p indicates the volatilization speed of pheromone, and delta tauijIndicating the amount of change in intensity of the pheromone,
representing the artificial pheromone left by the person on the road section between the ith customer and the jth customer, e representing the intensity of the manual intervention, Q representing the intensity of the pheromone, TbsA target value representing the current optimal path.
In the embodiment of the invention, the improved ant colony algorithm with manual intervention on pheromone updating is provided, and the convergence and accuracy of algorithm solving are ensured by adopting the pheromone with manual intervention.
As a preferred embodiment, referring to fig. 7, fig. 7 is a schematic flow chart of an embodiment of forming the elite population screened in step S2 in fig. 2 according to the present invention, which includes steps S26 to S27, wherein:
in step S26, sorting in descending order according to the individual target values of the iterative elite individuals;
in step S27, the top 10% of the ranking is selected as the screened elite individual according to the elite population strategy to form the screened elite population.
In the embodiment of the invention, according to an elite population strategy, the top 10% of excellent sequences are selected as elite individuals, the convergence rate of the algorithm is ensured, and the excellence in the population evolution process is continued.
As a preferred embodiment, the above elite population strategy comprises:
selecting the first 10% of individuals after the population fitness value is subjected to descending order arrangement after preliminary optimization as an elite population;
calculating the probability of selecting an individual according to the weight of the target value in the elite population to the sum of the target values;
wherein the probability that the individual is selected is represented by the following formula:
wherein p isiRepresenting the probability of the individual being selected, fiAnd (3) representing the fitness value of the ith elite individual in the elite population, and S represents the set of the current elite population.
In the embodiment of the invention, the population fitness value is taken as a sequencing standard, so that the excellence of population evolution is ensured.
As a preferred embodiment, referring to fig. 8, fig. 8 is a schematic flowchart of an embodiment of step S3 in fig. 2 provided by the present invention, and step S3 includes steps S31 to S38, where:
in step S31, determining a selection probability of the elite population based on the target value of the elite population;
in step S32, the two-side sequential correction algorithm is used to improve the roulette-based elite individual.
In the embodiment of the invention, based on the formed screening elite population, individual screening is carried out according to the target value of the elite population, and the improved two-side successive correction algorithm is combined for improvement, so that the improved individual is supplemented to the screened elite population, thereby accelerating the convergence speed of the algorithm and improving the accuracy of the algorithm.
In a specific embodiment of the present invention, the above elite retention strategy is to extend the elite performance of the population of the previous generation, which is preferably retained by individuals entering the next generation. That is, if NC >2, R (1) is R _ best (NC-1). R _ best (NC-1) is the optimal solution of the NC-1 generation.
In a specific embodiment of the present invention, the above elite population strategy has a certain randomness due to pheromone update, and the result of the preliminary optimizationThere is still much room for improvement. And selecting the first 10 percent of the population fitness values after the preliminary optimization and the descending order as an elite population. And calculating the probability of the selected individual according to the weight of the target value in the current population to the sum of the target values. And combining the roulette method and the selected probability, selecting an individual to be improved, and improving by using a two-side successive correction algorithm. And the expansion and optimization of the elite population are realized. Wherein f isiExpressing the fitness value of an individual i in the elite population, S expressing the set of the current elite population, and the selection probability is expressed by the following formula:
as a preferred embodiment, referring to fig. 9, fig. 9 is a schematic flowchart of an embodiment of a two-side successive correction algorithm provided by the present invention, and includes steps S901 to S906, where:
in step S901, an improved path that needs to be improved is acquired;
in step S902, according to the service sequence, selecting potential improvement points, and flipping paths between the potential improvement points to form a new path;
in step S903, when all the improvement points are traversed, it is determined whether all the improvement points are vehicles;
in step S904, if not, calculating the vehicle journey and load of the new route, and determining whether there is a vehicle exceeding a corresponding rated value in the vehicle journey or load of the new route;
in step S905, if not, calculating a path length and a customer average satisfaction corresponding to the improved path and the new path, and determining whether a preset output condition is satisfied according to the corresponding path length and the customer average satisfaction;
in step S906, if the new path is satisfied, the new path is output, and if the new path is not satisfied, the improved path is replaced with the new path, and the step of obtaining the improved path that needs to be improved is returned to.
In the embodiment of the invention, the corresponding improved individuals are determined by utilizing the successive correction algorithm on two sides, and the screened elite population is added, so that the excellence of the population is ensured.
In a specific embodiment of the present invention, the algorithm flow of the two-side successive correction algorithm is as follows:
firstly, inputting a path R needing to be improved;
secondly, selecting potential improvement points according to the service sequence, turning over paths among the improvement points, namely R (i + 1: j) ═ R (j: -1: i +1), recording the new paths as R1, judging whether the improvement points are traversed or not, and turning to the seventh step if the improvement points are traversed; otherwise, turning to the third step;
step three, judging whether the improved points i and j or the improved points i +1 and j +1 are vehicles, if so, turning to the step two, and if not, turning to the step two; otherwise, turning to the fourth step;
fourthly, calculating the stroke and the load of each vehicle on the new path R1, and turning to the fifth step;
fifthly, judging whether a vehicle exceeding a rated value exists in the stroke or the load in the new path R1, and turning to the second step if the vehicle exceeds the rated value in the stroke or the load in the new path R1; otherwise, turning to the sixth step;
sixthly, calculating R, R1 path length and average customer satisfaction, which are respectively marked as len, len1 and S, S1; if len is more than len1 and S is less than S1, the route R1 is better, and the seventh step is carried out; otherwise, turning to the second step when i is i +1 and R1 is R;
and seventhly, outputting the improved individual R1.
As a preferred embodiment, referring to fig. 10, fig. 10 is a schematic flowchart of an embodiment of the fixed-point interleaving algorithm in step S4 in fig. 2 provided by the present invention, and includes steps S41 to S43, where:
in step S41, randomly selecting two individuals from the improved elite population, randomly selecting corresponding individual genes, and exchanging the individual genes to other positions of the opposite individual to form a corresponding new path;
in step S42, it is determined whether or not a vehicle exceeding a rated load and a maximum vehicle travel exists in the new route, and if so, the process returns to the step of genetically exchanging the individual to another position of the opponent individual;
if not, in step S43, the corresponding new route is output.
In the embodiment of the invention, the individual cross variation is carried out by combining the fixed point cross algorithm, and the invalid calculation caused in the process of cross and variation is reduced.
In a specific embodiment of the present invention, the algorithm flow of the fixed-point intersection algorithm is as follows:
the first step, randomly selecting two individuals from a population, marking as R1 and R2, and turning to the second step;
step two, randomly selecting two genes from R1 and R2, and respectively marking as a step three and a step three;
the third step, using find (a ═ R2 (1:)), find (b ═ R1 (1:)) function, find the position of a in R2 respectively, the position of b in R1, record the position of gene in the path with p1, p2 respectively, go to the fourth step;
fourthly, randomly and correspondingly exchanging the genes at the positions p1 and p2 at other positions (non-first positions) of R1 and R2 by the algorithm, respectively marking the generated new paths as R _ new1 and R _ new2, and turning to the fifth step;
fifthly, calculating the mileage and the load of each vehicle of R _ new1 and R _ new2, and turning to a sixth step;
sixthly, judging whether a vehicle exceeding the rated load and the maximum travel exists in the R _ new1 or the R _ new2, and turning to the fourth step; otherwise, turning to the seventh step;
and seventhly, outputting R _ new1 and R _ new 2.
As a preferred embodiment, referring to fig. 11, fig. 11 is a schematic flow chart of an embodiment of selecting orthogonal experimental parameters in step S4 in fig. 2 provided by the present invention, and includes steps S44 to S46, where:
in step S44, a set of the orthogonal experimental parameters is selected to be applied to the current iteration using roulette;
in step S45, it is determined whether the current elite population average level is improved;
in step S46, if yes, recording the current optimal parameter combination and the population optimal solution, and if not, increasing the performance value of the current orthogonal experimental parameter by a preset value.
In the embodiment of the invention, the optimal orthogonal experiment parameters are selected until the iterative optimal solution is solved, the algorithm solving performance and generalization capability are improved, and the accuracy and real-time performance of path planning are ensured.
In a specific embodiment of the present invention, the algorithm flow for orthogonal experimental parameter selection is as follows:
the first step, a group of parameters are selected by using a roulette method and applied to the current iteration, and the second step is carried out;
secondly, judging whether the average level of the population is improved, if so, switching to the third step, and increasing the performance value of the group of parameters by 1, otherwise, switching to the first step;
and thirdly, recording the current optimal parameter combination and the population optimal solution.
It should be noted that the constant algorithm parameters have subjectivity, and a plurality of alternative schemes are set for the algorithm parameters, and parameter experience setting and scheme comparison are performed. The orthogonal test optimization algorithm parameters are designed, so that the subjective influence of constant parameters can be effectively reduced, and the algorithm performance is fully exerted. 5 parameters of the algorithm are selected, and L is designed25(56) And (4) an orthogonal table. Initial performance value N of each group of parameter numbersiTo 1, the probability of selection for each set of parameters is represented by:
in a specific embodiment of the present invention, the overall flow of the multi-yard multi-vehicle type vehicle path planning method is as follows:
firstly, setting algorithm initial parameters, importing client and yard data, generating an initial feasible solution based on a heuristic algorithm, and turning to a second step;
and step two, solving the feasible solution target value of the current generation, and reserving the elite individuals iterated last time according to the elite reservation strategy. If NC is greater than 2, R (1) is R _ best (NC-1), R _ best (NC-1) is the NC-1 generation optimal solution, otherwise, the third step is carried out;
thirdly, recording the optimal solution of the iteration, updating pheromone, emptying a taboo table and turning to the fourth step;
fourthly, the target values of the generation individuals are sorted in descending order. Selecting the first 10% as elite individuals according to an elite population strategy, and turning to the fifth step;
fifthly, determining the selected probability of the elite population according to the target value of the elite population, wherein the calculation method is shown in the selected probability formula, and the elite individual is improved by utilizing a two-side successive correction algorithm based on a roulette method, and turning to the sixth step;
sixthly, adding the improved individuals into the elite population, judging whether the population reaches the initial population scale, if so, turning to the seventh step, otherwise, turning to the fifth step;
seventhly, judging whether the individual needs cross variation based on a roulette method, if so, performing cross variation on the individual based on a fixed-point cross algorithm, and turning to the eighth step;
eighthly, selecting orthogonal test parameters based on a roulette method, calculating target values of the elite population after cross variation, solving the average value of the target values, and turning to the ninth step;
ninth, judging whether the average population level is improved, if so, turning to the tenth step, and if not, turning to the eighth step;
step ten, recording the optimal solution of the current generation population, if NC is greater than 2, judging whether R (1) is R _ best (NC-1), if so, turning to the step ten, otherwise, turning to the step two;
and eleventh, outputting the optimal solution.
In a specific embodiment of the present invention, the multi-yard multi-vehicle-type vehicle path planning method is named as an improved ant colony-genetic algorithm (ACO-GA), and is compared with an ant colony Algorithm (ACO), a Genetic Algorithm (GA), and an Improved Genetic Algorithm (IGA) through experiments, wherein:
(1) algorithm operating environment
The algorithm operating environment is CPU2.20GHz, the memory is 4.00GB, the operating system is 64 bits of Windows10, and the programming language adopts MATLABR2016 a;
(2) algorithm parameter setting
And selecting the ant number m, the pheromone elicitation factor alpha, the expected elicitation factor beta, the cross rate Pc and the variation rate Pm to carry out orthogonal test design. The parameter levels are shown in Table 1 and the partial orthogonality is shown in Table 2.
TABLE 1
| Parameter(s) | |
|
|
|
Level 5 |
| M | 15 | 35 | 43 | 47 | 50 |
| |
1 | 2 | 2.5 | 3 | 3.5 |
| |
3 | 3.5 | 4 | 4.5 | 5 |
| ∈ | 0.2 | 0.4 | 0.6 | 0.8 | 1.0 |
| Pc | 0.25 | 0.45 | 0.60 | 0.70 | 0.90 |
| pm | 0.002 | 0.004 | 0.006 | 0.007 | 0.1 |
TABLE 2
In consideration of the complexity of the orthogonal test, other parameters of the algorithm adopt empirical values. The pheromone evaporation coefficient ρ is 0.1, the iteration number G is 300, the population NP is 200, and the pheromone increase intensity coefficient Q is 100.
(3) Test model and data
Testing the model: and comprehensively considering the customer satisfaction, the vehicle mileage and the vehicle number during the model test, and weighting multiple targets into a single target to solve through expert scoring. The weights of the weights are respectively 0.31, 0.47 and 0.22 after being scored by experts. The model takes into account vehicle mileage and capacity constraints. Suppose a customer is served by only one vehicle
Test data: the method comprises the steps of representing customer groups under different distributions by Solomon arithmetic examples with different distribution characteristics, and setting a plurality of parking lots on the basis of an original parking lot, wherein each parking lot has a plurality of vehicle types, different parking lot positions are different, and the maximum mileage and the maximum capacity among the vehicle types are different. The data of the parking lot are shown in a table 3 and a vehicle type data table 4.
TABLE 3
| Number of parking lot | x | y | |
| 1 | 30 | 17 | 1、2、3 |
| 2 | 16 | 32 | 1、2 |
| 3 | 25 | 52 | 2、3 |
| 4 | 50 | 55 | 1、3 |
| 5 | 25 | 60 | 2、3 |
TABLE 4
(4) Analysis of results
The improved ant colony-genetic algorithm (ACO-GA) of the split combination improved ant colony algorithm, the genetic algorithm and the successive correction algorithm on two sides is the improved ant colony Algorithm (ACO), the Genetic Algorithm (GA), the Improved Genetic Algorithm (IGA) and the improved ant colony-genetic algorithm (ACO-GA) are respectively calculated by using the same model, the results are compared and analyzed, and the optimization degree result is shown in a table 5 (unit:%).
TABLE 5
The ant number M of the ACO algorithm is 50, the pheromone elicitation factor δ is 2.5, the expected elicitation factor μ is 4.5, the pheromone elicitation factor e is 0.4, the pheromone strength Q is 100, the GA and the IGA have the same parameters except for embedding the difference of the successive correction algorithms on two sides, wherein the population size NP is 100, the crossing rate Pc is 0.7, the variation rate Pm is 0.006, and the iteration number G of the algorithm is 500. As can be seen from table 3, although the improved ant colony-genetic algorithm calculation result is not as good as that of the conventional algorithm with respect to part of data, the overall calculation capability of the ACO-GA algorithm is better than that of the conventional algorithm, and the average optimization is more than 5%, it is seen that the improved ant colony-genetic algorithm has stronger calculation capability and better optimization effect compared with the conventional algorithm, but the algorithm has longer calculation time when the maximum iteration number is used as the iteration termination condition due to the nested orthogonal test design and other local optimization modules in the algorithm. As can be seen from FIG. 3, the ACO-GA algorithm completes the convergence of the model in 150 times, has a good convergence effect, and effectively considers the customer satisfaction, the vehicle mileage and the vehicle number. The result shows that the algorithm achieves a better effect on solving the problem of the vehicle path of multiple vehicles with constraints in multiple parking lots and multiple vehicle types.
An embodiment of the present invention further provides a multi-yard multi-vehicle type vehicle path planning apparatus, and when viewed in conjunction with fig. 13, fig. 13 is a schematic structural diagram of an embodiment of the multi-yard multi-vehicle type vehicle path planning apparatus provided by the present invention, and the apparatus includes:
an obtaining unit 1301, configured to obtain vehicle information and customer information of different yards, perform corresponding coding, and determine a hybrid coding;
a processing unit 1302, configured to determine a corresponding initial feasible solution according to the hybrid coding, retain a corresponding iteration elite individual, update an pheromone matrix, and perform individual screening according to an individual target value of the iteration elite individual to form a screened elite population; the method is also used for screening individuals according to the elite population target value corresponding to the screened elite individual, improving by utilizing a successive correction algorithm on two sides, determining improved individuals, and adding the improved individuals to the screened elite population until the initial population scale is reached;
an output unit 1303: the method is used for carrying out cross variation on individuals by adopting a fixed-point cross algorithm, selecting orthogonal experiment parameters until preset conditions are met, and outputting an iterative optimal solution.
The more specific implementation of each unit of the multi-yard multi-vehicle type vehicle path planning device can be referred to the description of the multi-yard multi-vehicle type vehicle path planning method of the invention, and has similar beneficial effects, and the detailed description is omitted here.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the multi-yard multi-vehicle type vehicle path planning method as described above.
Generally, computer instructions for carrying out the methods of the present invention may be carried using any combination of one or more computer-readable storage media. Non-transitory computer readable storage media may include any computer readable medium except for the signal itself, which is temporarily propagating.
A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages, and in particular may employ Python languages suitable for neural network computing and TensorFlow, PyTorch-based platform frameworks. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The embodiment of the invention also provides multi-parking lot and multi-vehicle type vehicle path planning equipment, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the program, the multi-parking lot and multi-vehicle type vehicle path planning method is realized.
According to the computer-readable storage medium and the computing device provided by the above embodiments of the present invention, the content specifically described for implementing the multi-yard multi-vehicle type vehicle path planning method according to the present invention can be referred to, and the method has similar beneficial effects to the multi-yard multi-vehicle type vehicle path planning method described above, and is not repeated herein.
The invention discloses a multi-parking lot multi-vehicle type vehicle path planning method, which comprises the following steps of firstly, effectively acquiring vehicle information and customer information, and coding the vehicle information and the customer information so as to facilitate the application of a subsequent optimization algorithm; then, based on the initial feasible solution, carrying out screening of the elite individuals, updating pheromone, further carrying out screening according to individual target values of the iterative elite individuals, ensuring the convergence rate of the algorithm through multiple times of elite population screening, and continuing the excellence in the population evolution process; then, based on the formed screening elite population, individual screening is carried out according to the target value of the elite population, and improvement is carried out by combining a successive correction algorithm on two sides, so that the improved individuals are supplemented to the screened elite population, thereby accelerating the convergence speed of the algorithm, improving the accuracy of the algorithm and further continuing the excellence in the population evolution process; and finally, combining a fixed-point crossing algorithm to carry out individual crossing variation, reducing invalid calculation caused in the crossing and variation processes, selecting optimal orthogonal experiment parameters until an iterative optimal solution is solved, improving the solving performance and generalization capability of the algorithm, and ensuring the accuracy and real-time performance of path planning.
According to the technical scheme, on the basis of an initial feasible solution, pheromone updating and elite population strategies are combined, two-side successive correction algorithms are used for improvement, a fixed-point cross algorithm is used for carrying out cross variation on individuals, invalid calculation is reduced, meanwhile, optimal parameters are selected by combining orthogonal experiment parameters, the convergence speed of the algorithm is improved, the performance of the algorithm is optimized, an iterative optimal solution is output, and the reasonability, the high efficiency and the real-time performance of path planning in a complex scene are guaranteed.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A multi-yard multi-vehicle type vehicle path planning method is characterized by comprising the following steps:
acquiring vehicle information and customer information of different parking lots, performing corresponding coding, and determining a mixed code;
determining a corresponding initial feasible solution according to the hybrid coding mechanism and the improved ant colony algorithm, updating pheromones by adopting an improved pheromone updating strategy, performing descending order arrangement on target values of the initial populations based on the initial populations, and screening to form elite populations;
determining an individual to be improved based on a roulette method, improving the elite individual by utilizing a two-side successive correction algorithm, and adding the improved individual to the screened elite population until the initial population scale is reached;
based on the cross rate and the variation rate selected by the orthogonal parameters, selecting individuals needing cross variation by using a roulette method, carrying out cross variation on the individuals by using a fixed-point cross algorithm, selecting orthogonal experimental parameters to continuously optimize the performance of the algorithm until preset conditions are met, and outputting an iterative optimal solution.
2. The method of claim 1, wherein the obtaining vehicle information and customer information of different yards and performing corresponding encoding comprises:
according to the vehicle information, a three-digit code is adopted to form a vehicle number, wherein the first digit code is used for preventing the code from being repeated with a customer code, the second digit code is used for representing the position of a parking lot, and the third digit code is used for representing the vehicle type;
and forming a customer set among different vehicle numbers according to the customer information, wherein the customer set is used for representing the service sequence with the previous vehicle.
3. The multi-yard multi-vehicle type vehicle path planning method of claim 1, wherein the initial feasible solution comprises a planned path, and the determining a corresponding initial feasible solution according to the hybrid coding scheme and the improved ant colony algorithm comprises:
randomly selecting a vehicle serving the customer according to the hybrid code;
when the corresponding client set is not an empty set, determining the corresponding state transition probability according to the pheromone intensity from the current vehicle position to the client which is not accessed, and selecting the next client to be served by adopting a roulette method;
and judging whether the corresponding vehicle mileage meets a preset mileage condition, if so, outputting the planned path, and calculating an overall target value corresponding to the planned path.
4. The multi-yard multi-vehicle-type vehicle path planning method of claim 1, wherein updating the pheromone with the improved pheromone update strategy comprises:
according to the elite reservation strategy, reserving the elite individuals subjected to the last iterative screening to enter the next generation;
and recording the optimal solution of the iteration, updating the corresponding pheromone matrix, and clearing the corresponding tabu list.
5. The multi-yard multi-vehicle-type vehicle path planning method of claim 4, wherein said updating the corresponding pheromone matrix comprises:
besides pheromones left on the road sections by common ants, a preset number of pheromones are manually applied to each road section on the current optimal path;
wherein, the mode of pheromone updating is realized by the following formula
τij＝(1-ρ)τij+Δτij
Where M represents the total number of customers, τijIndicates the intensity of pheromone on the road section between the ith client and the jth client, p indicates the volatilization speed of pheromone, and delta tauijIndicating the amount of change in intensity of the pheromone,
representing the artificial pheromone left by the person on the road section between the ith customer and the jth customer, e representing the intensity of the manual intervention, Q representing the intensity of the pheromone, TbsA target value representing the current optimal path.
6. The method of claim 1, wherein the sorting of the target values in descending order based on the initial population and the screening to form the elite population comprises:
sorting in descending order according to the individual target values of the iteration elite individuals;
and according to an elite population strategy, selecting the top 10% of sorted elite individuals as screened elite individuals to form the screened elite population.
7. The multi-yard multi-vehicle-type vehicle path planning method according to claim 4 or 6, wherein the elite population strategy comprises:
selecting the first 10% of individuals after the population fitness value is subjected to descending order arrangement after preliminary optimization as an elite population;
calculating the probability of selecting an individual according to the weight of the target value in the elite population to the sum of the target values;
wherein the probability that the individual is selected is represented by the following formula:
wherein p isiRepresenting the probability of the individual being selected, fiAnd (3) representing the fitness value of the ith elite individual in the elite population, and S represents the set of the current elite population.
8. The method of claim 1, wherein the determining an individual to be modified based on roulette and modifying the elite individual using a two-side progressive modification algorithm comprises:
determining the selection probability of the elite population according to the target value of the elite population;
based on the roulette method elite individual, the improvement is carried out by utilizing a two-edge successive correction algorithm, wherein the two-edge successive correction algorithm comprises the following steps:
obtaining an improved path needing improvement;
according to the service sequence, selecting potential improvement points, and turning paths among the potential improvement points to form a new path;
when all the improved points are traversed, judging whether all the improved points are vehicles;
if not, calculating the vehicle travel and load of the new path, and judging whether the vehicle travel or load in the new path has a vehicle exceeding a corresponding rated value;
if not, calculating the path length and the customer average satisfaction corresponding to the improved path and the new path, and judging whether preset output conditions are met according to the corresponding path length and the customer average satisfaction;
if the path is not satisfied, replacing the improved path with the new path, and returning to the step of acquiring the improved path needing to be improved.
9. The method for planning the path of a multi-vehicle type vehicle in a multi-vehicle yard according to claim 1, wherein the performing cross variation on individuals by using a fixed-point cross algorithm comprises:
randomly selecting two individuals from the improved elite population, randomly selecting corresponding individual genes, and exchanging the individual genes to other positions of the individuals of the other party to form a corresponding new path;
judging whether the new path has a vehicle exceeding the rated load and the maximum vehicle travel, if so, returning to the step of exchanging the individual gene to other positions of the opposite individual;
and if not, outputting the corresponding new path.
10. The multi-yard multi-vehicle type vehicle path planning method of claim 9, wherein the selecting orthogonal experimental parameters to continue optimizing algorithm performance until a preset condition is satisfied, and outputting an iterative optimal solution comprises:
selecting a set of the orthogonal experimental parameters to apply to the current iteration by using a roulette method;
judging whether the average level of the current elite population is improved;
if so, recording the current optimal parameter combination and the population optimal solution, and if not, increasing the performance value of the current orthogonal experiment parameter by a preset value.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114169804A (en) * | 2022-01-07 | 2022-03-11 | 昆明理工大学 | Optimized scheduling method for multi-distribution-center transport vehicle |
| CN114169804B (en) * | 2022-01-07 | 2024-05-14 | 昆明理工大学 | Optimized scheduling method for transport vehicles of multiple distribution centers |
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