CN115271023A - Engineering optimization method based on improved multivariate universe optimization algorithm and related equipment - Google Patents
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Abstract
The invention discloses an engineering optimization method based on an improved multivariate cosmic optimization algorithm and related equipment, wherein the method comprises the following steps: acquiring engineering parameter data, and inputting the engineering parameter data into a preset engineering optimization model; obtaining a preset engineering optimization model based on the improved multivariate universe optimization algorithm; and processing the engineering parameter data through the preset engineering optimization model to obtain and output the engineering cost data with the minimum manufacturing cost. The invention adopts a scattered foraging strategy to update the position parameters of the universe individuals so as to obtain higher convergence precision, and adopts an orthogonal learning mechanism to disturb the population position so as to avoid the algorithm from being trapped in a local optimal trap, thereby improving the overall performance of the multi-universe optimization algorithm and being better suitable for optimization problems in different fields, so that a preset engineering optimization model is obtained based on the improved multi-universe optimization algorithm, and the engineering parameters are optimized through the preset engineering optimization model.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an engineering optimization method, system, terminal and computer readable storage medium based on an improved multivariate universe optimization algorithm.
Background
The swarm intelligence optimization algorithm is an algorithm for simulating the swarm behaviors of organisms in various natural fields and is widely applied to various types of optimization problems. Compared with the traditional gradient algorithm, the group intelligent optimization algorithm has stronger adaptability and higher optimization efficiency. In recent years, researchers have proposed many advanced algorithms such as bat optimization algorithm (BA), differential evolution algorithm (DE), moth optimization algorithm (MFO), whale Optimization Algorithm (WOA), particle swarm optimization algorithm (PSO), sine and cosine optimization algorithm (SCA), drosophila optimization algorithm (FOA), and the like, in succession. In 2014, a Multi-universe optimization algorithm (MVO) has been proposed, which has the characteristics of simplicity and high efficiency and has strong capability of solving an optimal scheme, so that researchers are widely applied to various fields. Meanwhile, researchers provide many enhanced versions of MVO algorithm variants aiming at the problems that the original MVO algorithm is poor in searching and developing efficiency and easy to fall into local optimization; but none of the algorithms can adapt to the problems of all areas.
For example, the existing multivariate universe optimization algorithm cannot be applied to the field of engineering optimization, cannot optimize multiple parameters of an engineering optimization model, and cannot achieve the purpose of obtaining the minimum manufacturing cost.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
The invention mainly aims to provide an engineering optimization method, an engineering optimization system, a terminal and a computer-readable storage medium based on an improved multivariate cosmic optimization algorithm, and aims to solve the problems that the multivariate cosmic optimization algorithm in the prior art is weak in adaptability in the field and cannot optimize engineering parameters.
In order to achieve the above object, the present invention provides an engineering optimization method based on an improved multivariate cosmic optimization algorithm, which comprises the following steps:
acquiring engineering parameter data, and inputting the engineering parameter data into a preset engineering optimization model; the preset engineering optimization model is generated through the following steps:
initializing a universe population and universe individuals;
calculating the fitness value of each cosmic individual;
updating the position parameters of the universe individuals through a diet distribution strategy;
updating the optimal universe individual based on a black hole and white hole transfer mechanism;
perturbing the universe population through an orthogonal learning strategy;
replacing the current optimal universe individual by a greedy selection method;
when the maximum iteration times are reached, returning the optimal solution to obtain an improved multivariate cosmic optimization algorithm;
obtaining a preset engineering optimization model based on the improved multivariate universe optimization algorithm;
and processing the engineering parameter data through the preset engineering optimization model to obtain and output the engineering cost data with the minimum manufacturing cost.
Optionally, the engineering optimization method based on the improved multivariate cosmic optimization algorithm updates the cosmic individual location parameters through the diet strategy, and specifically includes:
the position updating equation in the dispersing foraging process is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the j +1 th parameter representing the ith cosmic individual,
represents the jth parameter of the ith universe individual, mu is the migration coefficient of Harris eagle, mu-N (0.5,0.1)2) And N represents the number of universes,
and
it is shown that two search individuals are present,
denotes the distance between any two searched individuals, n1And n2Denotes {1,2, ·, N } and N1≠n2Not equal to random integer in i, Pi jIs a logical value used to determine whether harris hawk employs a decentralized foraging strategy, the formula is as follows:
wherein r is5Is [0,1]Represents a dispersion factor, and the parameters that decrease non-linearly with iteration are defined as follows:
wherein epsilon0Is a constant and epsilon0Number of iterations is =0.4, T is the maximumThe number of iterations.
Optionally, the engineering optimization method based on the improved multivariate cosmic optimization algorithm, where the updating of the optimal cosmic individuals based on the black hole and white hole transfer mechanism specifically includes:
the universe population consists of N universe individuals, is searched in the D-dimensional space, and is initialized, and the formula is as follows:
wherein X represents a cosmic population consisting of a plurality of cosmic individuals,
a line vector composed of a d parameter and a plurality of parameters representing the n cosmic individuals
A cosmic individual representing a cosmic population;
in each iteration, sorting is performed according to the expansion rate of the universe, and a white hole is selected through the roulette wheel, and the formula is as follows:
wherein, XiRepresenting the ith cosmic individual; NI (X)i) Is the normalized expansion ratio, r, of the ith cosmic individual1Is [0,1]Of the number of the random number (c) in (c),
a jth parameter representing a kth universe selected by the roulette wheel selection mechanism;
calculating the wormhole existence probability WEP and the travel distance rate TDR, wherein the formula is as follows:
wherein, WEPmaxIs the maximum value of WEP, WEPminIs the minimum value of WEP, L represents the current iteration times, L represents the maximum iteration times, and p defines the search precision in the iteration process;
updating the position of the cosmic individuals and finding the optimal cosmic individuals, wherein the formula is as follows:
wherein x isjThe j-th parameter, lb, representing the optimal universe of individuals formed so farjDenotes the lower limit, ub, of the j-th variablejDenotes the upper limit of the ith variable, r2，r3，r4Is [0,1]The random number of (2).
Optionally, the engineering optimization method based on the improved multivariate cosmic optimization algorithm, where the perturbing the cosmic population through the orthogonal learning strategy specifically includes:
obtaining a guide vector T in an orthogonal learning mechanismi，TiRepresenting an optimal solution or construction vector for guiding the search individual to move between the solution vector and the guide vector, calculating a candidate solution ViThe formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing orthogonal operation, MiRepresenting the location of the ith cosmic individual;
if ViRatio MiGood then MiQuilt ViSubstituted, otherwise, MiRemain unchanged.
Optionally, the engineering optimization method based on the improved multivariate cosmic optimization algorithm is described, wherein the fitness value of the candidate solution is an expansion rate of the universe.
Optionally, in the engineering optimization method based on the improved multivariate cosmic optimization algorithm, the wormhole existence probability WEP is linearly increased in an iterative process, and the travel distance rate TDR is continuously decreased in the iterative process.
Optionally, the engineering optimization method based on the improved multivariate cosmic optimization algorithm includes that the maximum number of iterations ranges from 100 to 100000 times.
In addition, in order to achieve the above object, the present invention further provides an engineering optimization system based on an improved multivariate cosmic optimization algorithm, wherein the engineering optimization system based on the improved multivariate cosmic optimization algorithm includes:
the system comprises a parameter acquisition input module, a parameter optimization module and a parameter analysis module, wherein the parameter acquisition input module is used for acquiring engineering parameter data and inputting the engineering parameter data into a preset engineering optimization model;
the engineering optimization model generation module is used for generating a preset engineering optimization model according to the improved multivariate cosmic optimization algorithm;
the preset engineering optimization model is generated through the following steps:
initializing a universe population and universe individuals;
calculating the fitness value of each cosmic individual;
updating the position parameters of the universe individuals through a diet strategy;
updating the optimal universe individual based on a black hole and white hole transfer mechanism;
perturbing the universe population through an orthogonal learning strategy;
replacing the current optimal universe individual by a greedy selection method;
when the maximum iteration times are reached, returning the optimal solution to obtain an improved multivariate cosmic optimization algorithm;
obtaining a preset engineering optimization model based on the improved multivariate universe optimization algorithm;
and the data processing and outputting module is used for processing the engineering parameter data through the preset engineering optimization model to obtain and output the engineering cost data with the minimum manufacturing cost.
In addition, to achieve the above object, the present invention further provides a terminal, wherein the terminal includes: the system comprises a memory, a processor and an engineering optimization program based on the improved multi-universe optimization algorithm, wherein the engineering optimization program is stored on the memory and can run on the processor, and when being executed by the processor, the engineering optimization program based on the improved multi-universe optimization algorithm realizes the steps of the engineering optimization method based on the improved multi-universe optimization algorithm.
In addition, to achieve the above object, the present invention further provides a computer readable storage medium, wherein the computer readable storage medium stores an engineering optimization program based on an improved multivariate cosmic optimization algorithm, and the engineering optimization program based on the improved multivariate cosmic optimization algorithm, when executed by a processor, implements the steps of the engineering optimization method based on the improved multivariate cosmic optimization algorithm as described above.
In the invention, engineering parameter data are obtained and input into a preset engineering optimization model; obtaining a preset engineering optimization model based on the improved multivariate universe optimization algorithm; and processing the engineering parameter data through the preset engineering optimization model to obtain and output the engineering cost data with the minimum manufacturing cost. According to the invention, a scattered foraging strategy is adopted to update the position parameters of the universe individuals so as to obtain higher convergence precision, an orthogonal learning mechanism is adopted to disturb the population position so as to avoid the algorithm from falling into a local optimal trap, the overall performance of the multi-element universe optimization algorithm is improved, and the optimization problem in different fields is better applied, so that a preset engineering optimization model is obtained based on the improved multi-element universe optimization algorithm, and the engineering parameters are optimized through the preset engineering optimization model.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the engineering optimization method based on the improved multivariate cosmic optimization algorithm of the present invention;
FIG. 2 is a flow chart of a preset engineering optimization model generation process in the preferred embodiment of the engineering optimization method based on the improved multivariate cosmic optimization algorithm of the present invention;
FIG. 3 is a flow chart of the improved multivariate cosmic algorithm in the preferred embodiment of the engineering optimization method based on the improved multivariate cosmic optimization algorithm of the invention;
FIG. 4 is a schematic diagram of a preferred embodiment of the present invention engineering optimization system based on an improved multivariate cosmic optimization algorithm;
FIG. 5 is a diagram illustrating an operating environment of a terminal according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the engineering optimization method based on the improved multivariate cosmic optimization algorithm according to the preferred embodiment of the present invention includes the following steps:
and S10, acquiring engineering parameter data, and inputting the engineering parameter data into a preset engineering optimization model.
And S20, generating a preset engineering optimization model according to the improved multivariate cosmic optimization algorithm.
Specifically, as shown in fig. 2, the preset engineering optimization model is generated through the following steps:
and S21, initializing a universe population and universe individuals.
Specifically, each different individual is a different feasible solution of the problem, and the process of optimizing the group intelligence algorithm is a process of updating the individual, which is also called a search process. The individual continuously carries out ordered search according to the formula of the algorithm, so the individual continuously approaches to the global optimal solution, namely continuously approaches to the optimal solution, and the search is stopped until the maximum search frequency is reached, wherein the frequency is artificially set and is called as the maximum iteration frequency. Therefore, the names of different swarm intelligence search individuals are different, but the roles are the same. In the invention, the searched individuals are universes, and a whole group (population) is composed of a plurality of individuals (a plurality of universes), for example, the number of the individuals of a general algorithm is all 30 which are manually set, and the population is only 1.
And S22, calculating the fitness value of each cosmic individual.
Specifically, the fitness value is an evaluation value for the solution found by the algorithm, and the better the evaluation value, the closer the obtained solution is to the global optimal solution of the problem, the larger the fitness value is.
And S23, updating the cosmic individual position parameters through the diet strategy.
In order to solve the problem that the original multivariate universe optimization algorithm (MVO) is low in searching efficiency, individual position parameters are updated finely by introducing a scattered foraging strategy, so that the global optimization efficiency is improved.
Specifically, the process of dispersive foraging is determined by a dispersion factor epsilon, only the individuals meeting the dispersion condition can perform position updating operation, and the position updating equation in the process of dispersive foraging is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the j +1 th parameter representing the ith cosmic individual,
represents the jth parameter of the ith universe individual, mu is the migration coefficient of Harris eagle, mu-N (0.5,0.1)2) The parameter setting of mu is consistent with the default value, N represents the number of universes,
and
it is shown that two search individuals are present,
denotes the distance between any two searched individuals, n1And n2Denotes {1,2, ·, N } and N1≠n2Not equal to random integer in i, Pi jIs a logical value used to determine whether harris hawk employs a decentralised foraging strategy, the formula is as follows:
wherein r is5Is [0,1]Represents a dispersion factor, and the parameters that decrease non-linearly with iteration are defined as follows:
wherein epsilon0Is a constant and epsilon0T is the number of iterations and T is the maximum number of iterations.
The scattered foraging strategy can select some individuals to perform scattered operation, and the rest individuals are kept at the original positions, so that the method has the advantage that all the individuals do not need to search unknown areas, and the development of some areas is reserved while the diversity of the population is increased. Therefore, the epsilon value of the early stage is larger, only a small part of individuals are scattered to forage, the individuals are beneficial to improving the convergence rate of the early stage, and almost all the individuals are scattered to forage along with the reduction of the epsilon value, so that the individuals are prevented from falling into local optimum.
And S24, updating the optimal universe individual based on the black hole and white hole transfer mechanism.
The optimal universe individual is obtained through formula iteration updating, and the optimal mode is determined in such a way that an optimal solution is found out through greedy selection in each circulation; and updating the universe individual by updating the parameters of the individual through a formula so that the universe individual is updated.
The core idea of MVO is: the model of the algorithm is based on three important concepts of the multi-universe theory, namely white holes, black holes and wormholes. The white holes are objects which only emit substances which are not attracted, the black holes absorb all matters in the universe, and the wormholes are used as time/space travel tunnels; objects can travel through the wormholes to any corner of an individual's universe on an instantaneous basis, even from one universe to another. The universe can gradually reach a stable state through the interaction of white holes, black holes and wormholes. The universe is defined as a candidate solution, and the fitness value of the candidate solution is the expansion rate of the universe, and the MVO algorithm model can be described by simply dividing into the following steps:
first, assuming that the universe population consists of N universe individuals, search in a D-dimensional space (e.g., D =3, representing a three-dimensional space), and initialize the universe population, the formula is as follows:
wherein X represents a cosmic population consisting of a plurality of cosmic individuals,
a line vector composed of multiple parameters and representing the d-th parameter of the n-th cosmic individual
A cosmic individual representing a cosmic population; in order to establish a mathematical model between a white hole and a black hole and exchange objects in the universe, a roulette type selection mechanism is introduced, wherein the roulette type selection mechanism is also called roulette wheel selection method, in the method, the selection probability of each individual is proportional to the fitness value of the individual, and the larger the fitness value is, the larger the selection probability is; fitness value is an evaluation of the solution to the algorithmThe better the estimate and the better the estimate, the closer the resulting solution is to the global optimal solution of the problem, the larger the fitness value.
In each iteration, sorting is performed according to the expansion rate of the universe, and a white hole is selected through the roulette wheel, and the formula is as follows:
wherein, XiRepresenting the ith cosmic individual; NI (X)i) Is the normalized expansion ratio, r, of the ith cosmic individual1Is [0,1]Is determined by the random number of (1),
the jth parameter representing the kth universe selected by the roulette wheel selection mechanism.
The WEP is linearly increased in the iterative process, and the TDR is continuously reduced in the iterative process, so that more accurate local search can be carried out in the global optimal range; calculating the wormhole existence probability WEP and the travel distance rate TDR, wherein the formula is as follows:
wherein, WEPmaxIs the maximum value of WEP, WEPminIs the minimum value of WEP, L represents the current iteration number, L represents the maximum iteration number, p defines the search precision in the iteration process, and the higher p is, the faster and more accurate the local search is.
And finally, updating the position of the cosmic individual and finding out the optimal cosmic individual, wherein the formula is as follows:
wherein x isjThe j-th parameter, lb, representing the optimal universe of individuals formed so farjDenotes the lower limit, ub, of the j-th variablejDenotes the upper limit of the ith variable, r2，r3，r4Is [0,1]The random number of (2).
And S25, disturbing the universe population through an orthogonal learning strategy.
The disturbance also represents updating of the universe individual, however, the updating amplitude is small, several parameters in the individual are generally updated, large updating is not carried out, the small-amplitude updating is called disturbance, the disturbance function is to improve the diversity of the population so as to avoid the algorithm from falling into a local optimal trap, namely, the algorithm updating stagnation is prevented from not approaching the global optimal solution.
Orthogonal learning strategy (OL) is a classic strategy used by researchers to enhance the exploration and development of group intelligent optimization algorithm, and OL can reasonably utilize calculation cost to find better candidate solutions. In the OL mechanism, a guide vector T is requirediThe method can be an optimal solution, and can also be a construction vector used for guiding the search individual to move between a solution vector and a guide vector.
Specifically, a guide vector T in an orthogonal learning mechanism is acquiredi，TiRepresenting an optimal solution or construction vector for guiding the search individual to move between the solution vector and the guide vector, calculating a candidate solution ViThe formula is as follows:
wherein the content of the first and second substances,
representing orthogonal operation, MiRepresenting the location of the ith cosmic individual;
if ViRatio MiGood then MiQuilt ViSubstituted, otherwise, MiRemain unchanged.
The concepts of OL include Orthogonal Array (OA), factorial Analysis (FA), factorial Group (FG), horizontal partitioning (LD). The invention updates the cosmic position using only orthogonal operations.
Orthogonal learning mechanism based on individual TiWith the individual MiPerforming orthogonal operation to generate new individual ViAt this point, the individual update is completed. That is, the more the post-update individuals differ from the pre-update individuals, the higher the diversity of the search. The orthogonal learning mechanism realizes large update of individuals through orthogonal operation.
And S26, replacing the current optimal universe individual by a greedy selection method.
Specifically, the greedy selection algorithm is to select an individual with the best fitness value by comparing fitness values of the individuals in each loop, and is called a greedy algorithm because an optimal solution under the current loop is always selected. The optimal universe individual is multiple, and each loop, namely each iteration, can generate a current optimal universe individual (current optimal solution).
And S27, returning the optimal solution when the maximum iteration times are reached, and obtaining the improved multi-element universe optimization algorithm.
Specifically, the maximum iteration number is the maximum cycle number, and is positively correlated with the number of evaluating fitness values and the number of individual updating in the algorithm, the maximum iteration number is a numerical value, and is manually set, for example, generally 100 to 100000 times, and when the maximum iteration number is reached, an optimal solution is returned, so that an improved multivariate cosmic optimization algorithm (i.e., folmv, forming orthogonal multivariate optimization algorithm for Foraging) is obtained.
And S28, obtaining a preset engineering optimization model based on the improved multivariate cosmic optimization algorithm.
And S30, processing the engineering parameter data through the preset engineering optimization model to obtain and output the engineering cost data with the minimum manufacturing cost.
Specifically, after obtaining the improved multivariate cosmic optimization algorithm (FOLMVO), the invention obtains an objective function with the minimum manufacturing cost of the engineering optimization model based on the improved multivariate cosmic optimization algorithm (FOLMVO), namely, the FOLMVO is applied to the engineering optimization field, and a plurality of parameters of the engineering optimization model are optimized to obtain the objective function with the minimum manufacturing cost for realizing the engineering optimization model, so that the minimum manufacturing cost is realized, namely, the preset engineering optimization model is used for processing the engineering parameter data to obtain and output the engineering cost data with the minimum manufacturing cost.
Actually, to solve the optimization problem, an optimal solution of the problem needs to be found, and the solution optimization algorithms are divided into two types, namely a deterministic algorithm and a non-deterministic algorithm, the swarm intelligence optimization algorithm belongs to the non-deterministic algorithm, and only a feasible solution can be found through iterative search, and the feasible solution is put into the whole optimization problem, that is, in the global, the feasible solution is not generally a globally optimal solution, but is relatively an optimal solution which can be found at present, and is a locally optimal solution. The process of global optimization is as follows: the local optimal solution is infinitely close to the global optimal solution through the finite iteration updating of the group intelligent optimization algorithm, and the problem of efficiency must be considered in the finite global optimization process. For example, how to find a more excellent solution in a shorter time, and how to find a solution in the same time with higher precision, that is, closer to a global optimal solution; therefore, the improvement of the global optimization efficiency of the MVO algorithm is to improve the optimization precision of the algorithm in the same time, and the algorithm takes less time when the same problem is solved.
The general group intelligent optimization algorithm is divided into two stages in the solving process: the method comprises a searching stage and a developing stage, wherein the searching stage is carried out at the early stage of a solving process, the main purpose is to cover the whole situation as much as possible, and a limited number of searching individuals are enabled to carry out searching in the whole situation, searching individuals need to be introduced, names of different group intelligent searching individuals are different, but functions are the same, the searching individuals are universes in the method, and a whole group (group) is composed of a plurality of individuals (multiple universes); the number of individuals of the general algorithm is 30 which are manually set. Therefore, because the group intelligent algorithm has a limit on the number of searching individuals, the global can not be traversed like an exhaustion method, and the global can only be covered as much as possible; by combining the invention, the improvement of the diversity of the search stage is to improve the global search capability of individuals in the early stage, search the optimal solution in the global range as much as possible and avoid falling into the local optimal solution.
Therefore, the invention starts from the characteristics of a group intelligent algorithm, and provides a FOLMVO algorithm with stronger optimizing capability based on a classical MVO algorithm in combination with a dispersive foraging strategy and an orthogonal learning mechanism, wherein the dispersive foraging strategy is used for finely developing individual positions so as to improve the global optimization efficiency of the MVO algorithm; and the orthogonal learning mechanism disturbs the position of the population in the previous stage of the MVO algorithm so as to avoid the algorithm from falling into a local optimal trap.
Further, as shown in fig. 3, the whole process of the method for improving the multivariate cosmic optimization algorithm of the present invention is as follows (i.e. the process of obtaining the improved multivariate cosmic optimization algorithm):
step S101, start;
step S102, initializing a universe population and universe individuals;
step S103, calculating the fitness value of each cosmic individual;
s104, updating the position parameters of the universe individuals through a diet distribution strategy so as to improve the global optimization efficiency;
s105, updating the optimal universe individual based on a black hole and white hole transfer mechanism;
s106, disturbing the universe population through an orthogonal learning strategy, and improving the diversity of the population so as to avoid the algorithm from falling into a local optimal trap;
s107, replacing the current optimal universe individual by a greedy selection method;
step S108, judging whether the maximum iteration times is reached, if so, executing step S109, otherwise, executing step S103;
and step S109, returning to the optimal solution.
Further, to verify the effectiveness of the improved method, it was tested on a number of functions with other algorithms, such as the application of FOLMVO to the design of Pressure Vessels (PV) in the field of engineering optimization, FOLMVO versus modelOptimizing the four parameters to realize an objective function with the minimum manufacturing cost of the engineering optimization model; the unknown parameters in the pressure vessel design problem are: inner radius (R), head thickness (T)h) Shell thickness (T)s) And cross-sectional area minus seal head (l); meanwhile, when the PV model is optimized, constraint conditions required for implementing the pressure container model need to be met, and a linear programming equation of the PV model is as follows:
suppose that:
minimum value:
wherein the content of the first and second substances,
are respectively based on the boundary parameter x1、x2、x3、x4The constraint of (2).
In this experiment, the FOLMVO (forming orthogonal multiple universe Optimization Algorithm) Algorithm of the present invention was compared with other 5 algorithms (EWOA, enhanced wheel Optimization Algorithm; BA, bat Algorithm, bat Optimization Algorithm; HIS, nonlinear Branch and Bound Algorithm, nonlinear branching and bounding Algorithm; GA, genetic Algorithm; CPSO, co-evolution Particle Swarm Optimization) under the same experimental environment, as shown in the following table, the FOLMVO Algorithm had the smallest optimal solution result to the photovoltaic model with supercharger, and in this experiment, the FOLMVO Algorithm was compared with the other 5 algorithms under the same experimental environment, and finally, the FOLMVO Algorithm was better than the other FOLMVO Algorithm, and the pressure vessel Algorithm was designed more effectively.
Table: comparison of PV design problems
As can be seen from the comparison of the above tables, the method provided by the present invention not only can obtain better solution, but also has faster convergence rate.
Further, as shown in fig. 4, based on the above engineering optimization method based on the improved multivariate cosmic optimization algorithm, the present invention also provides an engineering optimization system based on the improved multivariate cosmic optimization algorithm, wherein the engineering optimization system based on the improved multivariate cosmic optimization algorithm includes:
a parameter obtaining and inputting module 51, configured to obtain engineering parameter data, and input the engineering parameter data into a preset engineering optimization model;
the engineering optimization model generation module 52 is configured to generate a preset engineering optimization model according to the improved multivariate cosmic optimization algorithm;
the preset engineering optimization model is generated through the following steps:
initializing a universe population and universe individuals;
calculating the fitness value of each cosmic individual;
updating the position parameters of the universe individuals through a diet distribution strategy;
updating the optimal universe individual based on a black hole and white hole transfer mechanism;
perturbing the universe population through an orthogonal learning strategy;
replacing the current optimal universe individual by a greedy selection method;
when the maximum iteration times are reached, returning the optimal solution to obtain an improved multivariate cosmic optimization algorithm;
obtaining a preset engineering optimization model based on the improved multivariate universe optimization algorithm;
and the data processing and outputting module 53 is configured to process the engineering parameter data through the preset engineering optimization model, obtain engineering cost data with the minimum manufacturing cost, and output the engineering cost data.
Further, as shown in fig. 5, based on the above engineering optimization method and system based on the improved multivariate cosmic optimization algorithm, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Fig. 5 shows only some of the components of the terminal, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
The memory 20 may in some embodiments be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 20 may also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal. Further, the memory 20 may also include both an internal storage unit and an external storage device of the terminal. The memory 20 is used for storing application software installed in the terminal and various types of data, such as program codes of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 20 stores an engineering optimization program 40 based on an improved multivariate cosmic optimization algorithm, and the engineering optimization program 40 based on the improved multivariate cosmic optimization algorithm can be executed by the processor 10, so as to implement the engineering optimization method based on the improved multivariate cosmic optimization algorithm in the application.
The processor 10 may be, in some embodiments, a Central Processing Unit (CPU), a microprocessor or other data Processing chip, which is used to run program codes stored in the memory 20 or process data, such as executing the engineering optimization method based on the improved multivariate cosmic optimization algorithm.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 30 is used for displaying information at the terminal and for displaying a visual user interface. The components 10-30 of the terminal communicate with each other via a system bus.
In an embodiment, the steps of the improved multivariate cosmic optimization algorithm-based engineering optimization method are implemented when processor 10 executes an engineering optimization program 40 in memory 20 based on the improved multivariate cosmic optimization algorithm.
The invention also provides a computer readable storage medium, wherein the computer readable storage medium stores an engineering optimization program based on an improved multivariate cosmic optimization algorithm, and the engineering optimization program based on the improved multivariate cosmic optimization algorithm realizes the steps of the engineering optimization method based on the improved multivariate cosmic optimization algorithm when being executed by a processor.
In summary, the present invention provides an engineering optimization method based on an improved multivariate cosmic optimization algorithm and a related device, where the method includes: acquiring engineering parameter data, and inputting the engineering parameter data into a preset engineering optimization model; obtaining a preset engineering optimization model based on the improved multivariate universe optimization algorithm; and processing the engineering parameter data through the preset engineering optimization model to obtain and output the engineering cost data with the minimum manufacturing cost. According to the invention, a scattered foraging strategy is adopted to finely develop the updated individual position of the multivariate cosmic optimization algorithm so as to obtain higher convergence precision, an orthogonal learning mechanism is adopted to disturb the population position of the multivariate cosmic optimization algorithm in the early stage so as to avoid the algorithm from falling into a local optimal trap, a plurality of parameters of the engineering optimization model are optimized based on the improved multivariate cosmic optimization algorithm, so that the manufacturing cost is minimum, the overall performance of the multivariate cosmic optimization algorithm is improved, the optimization problem in different fields is better applied, a preset engineering optimization model is obtained based on the improved multivariate cosmic optimization algorithm, and the engineering parameters are optimized through the preset engineering optimization model so as to obtain the engineering cost data with the minimum manufacturing cost.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or terminal that comprises the element.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. The computer readable storage medium may be a memory, a magnetic disk, an optical disk, etc.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (10)
1. An engineering optimization method based on an improved multivariate cosmic optimization algorithm is characterized by comprising the following steps:
acquiring engineering parameter data, and inputting the engineering parameter data into a preset engineering optimization model; the preset engineering optimization model is generated through the following steps:
initializing a universe population and universe individuals;
calculating the fitness value of each cosmic individual;
updating the position parameters of the universe individuals through a diet strategy;
updating the optimal universe individual based on a black hole and white hole transfer mechanism;
perturbing the universe population through an orthogonal learning strategy;
replacing the current optimal universe individual by a greedy selection method;
when the maximum iteration times are reached, returning the optimal solution to obtain an improved multivariate cosmic optimization algorithm;
obtaining a preset engineering optimization model based on the improved multi-element universe optimization algorithm;
and processing the engineering parameter data through the preset engineering optimization model to obtain and output the engineering cost data with the minimum manufacturing cost.
2. The engineering optimization method based on the improved multivariate cosmic optimization algorithm according to claim 1, wherein the updating of the cosmic individual location parameters through the diet strategy specifically comprises:
the position updating equation in the dispersing foraging process is as follows:
wherein the content of the first and second substances,
the j +1 th parameter representing the ith cosmic individual,
represents the jth parameter of the ith universe individual, mu is the migration coefficient of Harris eagle, mu-N (0.5,0.1)2) And N represents the number of universes,
and
it is shown that two individuals are searched for,
denotes the distance between any two searched individuals, n1And n2Denotes {1,2, ·, N } and N1≠n2Not equal to the random integer in i,
is a logical value used to determine whether harris hawk employs a decentralised foraging strategy, the formula is as follows:
wherein r is5Is [0,1]Denotes a dispersion factor, and a parameter that decreases non-linearly with iteration is defined as follows:
wherein epsilon0Is a constant and epsilon0T is the number of iterations and T is the maximum number of iterations.
3. The engineering optimization method based on the improved multivariate cosmic optimization algorithm according to claim 2, wherein the updating of the optimal cosmic individuals based on the black-hole and white-hole transfer mechanism specifically comprises:
the universe population consists of N universe individuals, is searched in a D-dimensional space, and is initialized, and the formula is as follows:
wherein X represents a cosmic population consisting of a plurality of cosmic individuals,
a line vector composed of a d parameter and a plurality of parameters representing the n cosmic individuals
A cosmic individual representing a cosmic population;
in each iteration, sorting is performed according to the expansion rate of the universe, and a white hole is selected through the roulette, wherein the formula is as follows:
wherein XiRepresenting the ith cosmic individual; NI (X)i) Is the normalized expansion ratio, r, of the ith cosmic individual1Is [0,1]Is determined by the random number of (1),
a jth parameter representing a kth universe selected by the roulette wheel selection mechanism;
calculating the wormhole existence probability WEP and the travel distance rate TDR, wherein the formula is as follows:
wherein, WEPmaxIs the maximum value of WEP, WEPminIs the minimum value of WEP, L represents the current iteration times, L represents the maximum iteration times, and p defines the search precision in the iteration process;
updating the position of the cosmic individuals and finding the optimal cosmic individuals, wherein the formula is as follows:
wherein x isjThe j-th parameter, lb, representing the optimal universe of individuals formed so farjDenotes the lower limit, ub, of the j-th variablejDenotes the upper limit of the ith variable, r2，r3，r4Is [0,1]The random number of (2).
4. The engineering optimization method based on the improved multivariate cosmic optimization algorithm according to claim 3, wherein the perturbing the cosmic population through the orthogonal learning strategy specifically comprises:
obtaining a guide vector T in an orthogonal learning mechanismi，TiRepresenting an optimal solution or construction vector for guiding the search individual to move between the solution vector and the guide vector, calculating a candidate solution ViThe formula is as follows:
wherein the content of the first and second substances,
representing orthogonal operation, MiRepresenting the location of the ith cosmic individual;
if ViRatio MiGood, then MiQuilt ViSubstituted, otherwise, MiRemain unchanged.
5. The method of claim 3, wherein the fitness value of the candidate solution is an expansion rate of the universe.
6. The improved multivariate cosmic optimization algorithm-based engineering optimization method according to claim 3, wherein the wormhole existence probability WEP is linearly increased in the iterative process, and the travel distance rate TDR is continuously decreased in the iterative process.
7. The method of claim 1, wherein the maximum number of iterations ranges from 100 to 100000.
8. An engineering optimization system based on an improved multivariate cosmic optimization algorithm, which is characterized by comprising:
the system comprises a parameter acquisition input module, a parameter optimization module and a parameter analysis module, wherein the parameter acquisition input module is used for acquiring engineering parameter data and inputting the engineering parameter data into a preset engineering optimization model;
the engineering optimization model generation module is used for generating a preset engineering optimization model according to the improved multivariate cosmic optimization algorithm;
the preset engineering optimization model is generated through the following steps:
initializing a universe population and universe individuals;
calculating the fitness value of each cosmic individual;
updating the position parameters of the universe individuals through a diet strategy;
updating the optimal universe individual based on a black hole and white hole transfer mechanism;
disturbing the universe population through an orthogonal learning strategy;
replacing the current optimal universe individual by a greedy selection method;
when the maximum iteration times are reached, returning the optimal solution to obtain an improved multivariate cosmic optimization algorithm;
obtaining a preset engineering optimization model based on the improved multivariate universe optimization algorithm;
and the data processing and outputting module is used for processing the engineering parameter data through the preset engineering optimization model to obtain and output the engineering cost data with the minimum manufacturing cost.
9. A terminal, characterized in that the terminal comprises: a memory, a processor and an engineering optimization program based on an improved multivariate cosmic optimization algorithm stored on the memory and executable on the processor, the engineering optimization program based on the improved multivariate cosmic optimization algorithm when executed by the processor implementing the steps of the engineering optimization method based on the improved multivariate cosmic optimization algorithm according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores an engineering optimization program based on an improved multivariate cosmic optimization algorithm, which when executed by a processor implements the steps of the engineering optimization method based on the improved multivariate cosmic optimization algorithm according to any one of claims 1 to 7.
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| CN117439190A (en) * | 2023-10-26 | 2024-01-23 | 华中科技大学 | Water, fire and wind system dispatching method, device, equipment and storage medium |
| CN117439190B (en) * | 2023-10-26 | 2024-06-11 | 华中科技大学 | Water, fire and wind system dispatching method, device, equipment and storage medium |
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