CN113408725A - Genetic algorithm parameter optimization method, system, equipment and medium based on composite entropy - Google Patents
Genetic algorithm parameter optimization method, system, equipment and medium based on composite entropy Download PDFInfo
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Abstract
The invention provides a genetic algorithm parameter optimization method, a system, equipment and a medium based on composite entropy, wherein the method comprises the following steps: constructing a network model based on a genetic algorithm; converting individuals in the population into network nodes by using a conversion rule, and converting crossover and variation in genetic operation into edge connection of a network; calculating measure indexes of the corresponding network models of the genetic algorithm under different iteration times, cross probabilities and variation probabilities, and calculating composite entropy according to the measure indexes; and judging the rationality of the genetic algorithm parameters according to the variation trend of the composite entropy so as to optimize the genetic algorithm parameters. The invention achieves the purpose of theoretically guiding the iteration times and the selection of the operation operator, avoids subjectivity in empirical selection of the operation operator in the existing research, provides certain theoretical guidance for the iteration times and the selection of the operation operator of the genetic algorithm, and also provides a brand-new visual angle for revealing the evolution process of the genetic algorithm.
Description
Technical Field
The invention relates to the field of computers and machine learning, in particular to a genetic algorithm parameter optimization method, a system, equipment and a medium based on composite entropy.
Background
Genetic Algorithm (GA) is a random search Algorithm based on the biological Genetic mechanism in nature, i.e. the phenomenon of biological evolution (natural elimination, crossing, mutation, etc.), and the Algorithm uses a computer to simulate the biological evolution process to search and evolve in a mathematical way, and finally seeks an optimal solution.
However, in practical application, the traditional standard genetic algorithm has the disadvantages of easy loss of population diversity and insufficient convergence speed and accuracy. In addition, in the process of setting parameters of the genetic algorithm, for example, the selection and design of a selection operator, a mutation operator and a crossover operator depend on user experience, so that the subjective influence of parameter setting is large, and the theoretical basis support is lacked. Therefore, a method capable of adaptively optimizing genetic algorithm parameters is needed to provide guidance for a large number of users.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide a method, a system, a device, and a medium for optimizing genetic algorithm parameters based on composite entropy, which are used to solve the problems of large subjective influence and lack of support of theoretical basis caused by artificial empirical determination during setting of genetic algorithm parameters in the prior art.
In order to achieve the above objects and other related objects, the present invention provides a method for optimizing genetic algorithm parameters based on composite entropy, comprising:
constructing a network model based on a genetic algorithm;
converting individuals in the population into network nodes by using a conversion rule, and converting crossover and variation in genetic operation into edge connection of a network;
calculating measure indexes of the corresponding network models of the genetic algorithm under different iteration times, cross probabilities and variation probabilities, and calculating composite entropy according to the measure indexes;
and judging the rationality of the genetic algorithm parameters according to the variation trend of the composite entropy so as to optimize the genetic algorithm parameters.
Another object of the present invention is to provide a system for optimizing genetic algorithm parameters based on composite entropy, comprising:
the network construction module is used for constructing a network model based on a genetic algorithm;
the rule conversion module is used for converting individuals in the population into network nodes by using a conversion rule and converting intersection and variation in genetic operation into network edge connection;
the composite entropy calculation module is used for calculating measure indexes of the corresponding network models of the genetic algorithm under different iteration times, cross probabilities and variation probabilities, and calculating composite entropy according to the measure indexes;
and the parameter optimization module is used for judging the rationality of the genetic algorithm parameters according to the variation trend of the composite entropy so as to optimize the genetic algorithm parameters.
Another object of the present invention is to provide an electronic device, comprising:
one or more processing devices;
a memory for storing one or more programs; when the one or more programs are executed by the one or more processing devices, causing the one or more processing devices to perform the composite entropy-based genetic algorithm parameter optimization method.
It is still another object of the present invention to provide a computer-readable storage medium having stored thereon a computer program for causing the computer to execute the composite entropy-based genetic algorithm parameter optimization method.
As mentioned above, the genetic algorithm parameter optimization method, system, device and medium based on composite entropy of the invention have the following beneficial effects:
establishing a network model based on a genetic algorithm, connecting genetic operation in the genetic algorithm with network establishment, and measuring important nodes in a network structure by considering a measurement index-composite entropy of the influence of nodes and links on network characteristic indexes and a topological structure, so that the influence of corresponding network models on the composite entropy under different iteration times, cross probabilities and variation probabilities is calculated, and whether the parameters of the genetic algorithm are correctly and reasonably set is judged according to the composite entropy; meanwhile, the invention achieves the purpose of theoretically guiding the iteration times and the selection of the operation operator, avoids subjectivity in empirical selection of the operation operator in the existing research, provides certain theoretical guidance for the iteration times of the genetic algorithm and the selection of the operation operator, and provides a brand new visual angle for revealing the evolution process of the genetic algorithm, so that the genetic algorithm can be more applied, and more practical engineering optimization problems are solved.
Drawings
FIG. 1 is a flow chart of a method for optimizing genetic algorithm parameters based on composite entropy provided by the invention;
FIG. 2 is a general flow chart of a method for optimizing genetic algorithm parameters based on composite entropy according to the present invention;
FIG. 3 is a schematic diagram of a composite entropy-based genetic algorithm selection method provided by the invention;
FIG. 4 is a schematic diagram of a composite entropy-based genetic algorithm crossover method provided by the invention;
FIG. 5 is a matrix optimization diagram of a genetic algorithm variation method based on composite entropy according to the present invention;
FIG. 6 is a diagram of an evolutionary network of a composite entropy-based genetic algorithm provided by the present invention;
FIG. 7 is a block diagram showing the structure of a composite entropy-based genetic algorithm parameter optimization device provided by the invention;
fig. 8 is a schematic structural diagram of an electronic device according to the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Referring to fig. 1, a flowchart of a genetic algorithm parameter optimization method based on composite entropy provided by the present invention includes:
step S1, constructing a network model based on a genetic algorithm;
carrying out networked modeling by utilizing a genetic algorithm, and depicting N individuals in the population as N nodes in the network; wherein, the transmission or genetic operation of gene information exists between any two nodes, and the two nodes are connected by a path;
for example, on the basis of the traditional genetic algorithm, a population is initialized and encoded; selecting a parent population by adopting a roulette selection method, and generating a new individual by adopting a double-point sequence intersection method; generating new individuals (variant individuals) by adopting a variant method of the priority matrix; and (3) performing self-replication on individuals which are not crossed and mutated in the selected filial generation, namely adding self-loops to the nodes in a complex network, so that the individuals are reserved, and the diversity of the population is improved.
Step S2, converting individuals in the population into network nodes by using a conversion rule, and converting crossover and variation in genetic operation into edge connection of a network;
specifically, the number of individuals in the population is converted into network nodes by using a conversion rule, and intersection and variation in genetic operation are converted into edge connection operation of a network to form a corresponding relation; for example, different composite entropies are obtained according to different generated network models, wherein the total number of nodes, the total number of connected edges, the number of connected edges of each joint and the weight in the network are changed by using the crossing rate and the variation rate of the genetic algorithm, so that the composite entropy under different iteration times, crossing probabilities and variation probabilities can be conveniently calculated in the subsequent process. For example, the number of nodes and links in the network increases with the change of the crossing rate and the variance rate, and the size of the crossing rate and the variance rate affects the trend of the increasing number of the nodes and the links in the network, that is, the increasing trend of the number of the nodes or the links is slowed down as the crossing rate or the variance rate is larger, and vice versa.
Step S3, calculating the measure indexes of the corresponding network models of the genetic algorithm under different iteration times, cross probabilities and variation probabilities, and calculating composite entropy according to the measure indexes;
specifically, a network model after genetic algorithm iteration is analyzed by using a Pajek tool to obtain a measure index of each node in the network; calculating the length change entropy of the network node degree entropy path according to the measure indexes of the network, calculating the weighted composite entropy of the network according to the length change entropy of the network node degree entropy path, and selecting the largest weighted composite entropy to perform weighted calculation to obtain the composite entropy.
And step S4, judging the rationality of the genetic algorithm parameters according to the variation trend of the composite entropy, thereby optimizing the genetic algorithm parameters.
Specifically, if the change of the composite entropy is in a decreasing trend, the ratio of the core node to the path in the network constructed by using the genetic algorithm parameters is reduced, which indicates that the network is stable and the network evolution is inert, the genetic algorithm parameters are judged to be reasonable, that is, the genetic algorithm parameters under the condition are suitable for optimizing the genetic algorithm parameters; if the change of the composite entropy presents an increasing trend, the ratio of the core nodes to the paths in the network constructed by using the genetic algorithm parameters is increased, which indicates that the network is unstable and the network evolution is positive, the genetic algorithm parameters are judged to be unreasonable, namely, the genetic algorithm parameters under the condition are not suitable for optimizing the genetic algorithm parameters.
Specifically, the genetic algorithm research based on the complex network theory mainly utilizes the characteristics of a complex network scale index, structural entropy and the like to measure the uniformity of the network, and solves the problems of unknown and uneven population structure and the like in the genetic algorithm evolution process. On the basis that the influence of the genetic algorithm parameter setting on the node degree entropy and the path length change entropy in the network is only considered by measuring the node degree entropy in the network uniformity, the genetic algorithm self-adaptive parameter setting method of the weighted composite entropy is provided by utilizing the influence of the genetic algorithm parameter setting on the node degree entropy and the path length change entropy in the network, and guidance is provided for the optimization setting of the genetic algorithm parameters.
In the embodiment, the invention provides a genetic algorithm parameter self-adaptive setting method based on composite entropy, and the method has the characteristic of genetic algorithm self-adaptive parameter setting. The method mainly comprises the steps of modeling a genetic algorithm by utilizing a complex network, and establishing a connection between the operation of the genetic algorithm and the construction of the complex network; then, a measurement index, namely composite entropy, which comprehensively considers the influence of the nodes and the links on the characteristic index and the topological structure of the complex network is provided, so that the influence of the important nodes of the network structure can be well measured; and finally, calculating the influence of three aspects of different iteration times, intersection rate and variation rate on the complex network entropy, and obtaining whether the self-adaptive setting of the genetic algorithm parameters is correct or not according to the influence on the complex network entropy, thereby providing a method for optimizing the genetic algorithm.
Referring to fig. 2, a general flowchart of a genetic algorithm parameter optimization method based on composite entropy provided by the present invention is detailed as follows:
setting individual as XiWith a fitness value of f (X)i) Then the individual fitness ratio, i.e. the individual selection probability P (X)i)；
Calculating cumulative probability q of each individualiI.e. of all individuals before each individualSelecting a sum of probabilities, corresponding to a probability distribution function f (x) in probability theory;
③ randomly generating r E [0, 1]If q isi>r, then selecting individual Xi。
In fig. 2, taking 10 individuals as an example, giving their fitness, calculating their selection probability and cumulative probability, and if the random number r generated in the selection is 0.58, the individual selected in this round is number 4; if the random number r generated in the next selection round is 0.96, the individual selected in the current selection round is number 9.
In other embodiments, please refer to fig. 3, which is a schematic diagram of a method for selecting a genetic algorithm based on composite entropy according to the present invention, including:
according to the cross probability PcSelecting P in a population of N individualscThe xn individuals are crossed, and if there are an odd number of individuals, one individual is randomly subtracted and put back into the population pool. Selecting two arbitrary parent chromosomes from the crossover pool, each having n genes, then randomly generating two in [1, n-1 ]]The positions of the intersections are determined by unequal numbers A1 and A2, so that a chromosome is divided into a front part, a middle part and a rear part, the front part and the rear part of two parent chromosomes are kept unchanged, and the middle part of the chromosome is regularly replaced.
In fig. 3, two intersections a1 ═ 2 and a2 ═ 7 are randomly generated, and the anterior, middle, and posterior three parts of the parent chromosome are divided, where the middle gene of parent chromosome 1 is: 43576, the sequence corresponding to the parent chromosome 2 is 34657, then 34657 is used as the middle part of the parent chromosome 1 to obtain the child chromosome 1; the intermediate genes of parent chromosome 2 are: 24658, the sequence of the parent chromosome 1 is 246968, and then 246968 is used as the middle part of the parent chromosome 2 to obtain the child chromosome 2.
In other embodiments, please refer to fig. 4, which is a schematic diagram of a genetic algorithm crossing method based on composite entropy according to the present invention, and the method includes the following steps:
according to the variation rate PmSelection of P in a population of N individualsmThe XN individuals were subjected to mutation.Selecting a parent chromosome from the mutation pool, wherein n genes are contained in the parent chromosome, randomly generating a gene in [1, n-1 ]]The position of the variation in the internal number a1, which divides a chromosome into two parts, keeps the front of the chromosome unchanged, and replaces the rear of the chromosome with the job priority matrix, for example, the priority matrix of fig. 4, which results in a 9 × 9 priority matrix.
M [ i ] [ j ] (1 ≦ i, j ≦ 9) ═ 1, indicating that i is the immediately preceding cell of j, M [ i ] [ j ] (1 ≦ i, j ≦ 9) ═ 0, indicating that i is the immediately preceding cell of j.
A parent chromosome is [1, 2, 4, 3, 5, 7, 6, 8, 9], and when a mutation point a1 is randomly generated as 4, 57689 of the chromosome is mutated according to the priority relationship of fig. 4, for example, it may be mutated as 65879, and a new child chromosome is generated [1, 2, 4, 3, 6, 5, 8, 7, 9 ].
In other embodiments, as shown in fig. 5, which is a flow chart of complex network modeling of the genetic algorithm of the present invention, a conventional genetic algorithm is modeled by using a complex network, population construction is performed, and then genetic algorithm operation is simulated in the complex network.
In this embodiment, a completely new complex network entropy is constructed herein, which is used to measure indexes and network topology characteristics in a complex networking process of a genetic algorithm, and a test function is used to analyze different iteration times, cross rates and mutation rates in Pajek (Pajek is a large-scale complex network analysis tool, and is a powerful tool for researching various complex nonlinear networks existing at present, Pajek runs in a Windows environment, and is used for analyzing and visualizing operations of a large network with thousands or even millions of nodes), so as to achieve the purpose of theoretically guiding iteration times and operator selection, avoid subjectivity in empirical selection of operators in existing research, provide a certain theoretical guidance for genetic algorithm iteration times and operator selection, and provide a completely new perspective for revealing a process of a genetic algorithm, the genetic algorithm can be applied more, and more practical engineering optimization problems can be solved.
In other embodiments, as shown in fig. 6, a composite entropy-based genetic algorithm evolutionary network diagram provided by the present invention includes:
for example, individuals are encoded as 123456789, 132465789, 132645879, 142365879, 124536789. After the first round of selection, 132465789 was selected twice and individual 124536789 was eliminated; then, individuals 123456789 and 132465789, 132465789 and 132645879 were crossed to obtain 123465789, 132456789, 132465879 and 132645789, respectively, while 142365879 was not operated to self-replicate and was retained; finally, individual 123465789 was mutated to 123465879.
The invention utilizes a brand-new composite entropy CE to evaluate the rationality of different parameters, and the formula of the CE is as follows:
in the formula, CE is composite entropy, alpha is weighting factor of nodes in the network, beta is weighting factor of number of connecting edges in the network, APL is average path length in the networkijFor node k in the networkiAnd kjLength of inter path, N is the number of nodes, k(i，j)Is node kiAnd kjThe distance between them.
The parameters of the genetic algorithm are then set to:
coding mode: one-dimensional data columns (mimicking the work unit work order).
② Population number (Population Size, PopSize): 80.
(xxiii) maximum algebra (MaxGen): when the crossing rate is 0.4 and the variation rate is 0.02, the iteration times are 100 times, 200 times, 300 times, 400 times and 500 times in sequence.
③ Crossover (Crossover Proavailability, Pc): when the number of iterations is 100 and the variation rate is 0.02 fixed, the crossing rate is 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85 and 0.9 in sequence.
Variation ratio (Pm): when the iteration times are 100 times and the crossing rate is 0.4 fixed, the variation rates are 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08 and 0.09 in sequence.
Compound entropy: and alpha is 0.5, and the log base number is 10.
In the embodiment, the composite entropies under different parameter settings are respectively calculated, and the small composite entropy indicates that the occupation ratio of important nodes and paths in the complex network is small, and simultaneously means that the network is stable and the network evolution is inert; the large composite entropy shows that the occupation ratio of important nodes and paths in the complex network is larger and larger, which means that the network is more and more unstable, and the network evolves actively. The rationality of the parameter setting is judged according to the above.
In summary, the invention provides a new genetic algorithm parameter selection method, namely the composite entropy, and after the genetic algorithm is modeled by using a complex network theory, the composite entropy can be used for comprehensively measuring the influence of the modeled nodes and paths on the network characteristics; the influence of the iteration times, the cross rate and the variation rate of the genetic algorithm on the composite entropy is analyzed by using Pajek large-scale complex network analysis software, and the reasonability of parameter setting is judged by using the size of the composite entropy.
Referring to fig. 7, a structural block diagram of a genetic algorithm parameter optimization device based on composite entropy according to the present invention includes:
the network construction module 1 is used for constructing a network model based on a genetic algorithm;
the rule conversion module 2 is used for converting individuals in the population into network nodes by using a conversion rule and converting intersection and variation in genetic operation into network edge connection;
the composite entropy calculation module 3 is used for calculating measure indexes of the corresponding network models of the genetic algorithm under different iteration times, cross probabilities and variation probabilities, and calculating composite entropy according to the measure indexes;
and the parameter optimization module 4 is used for judging the rationality of the genetic algorithm parameters according to the variation trend of the composite entropy so as to optimize the genetic algorithm parameters.
It should be further noted that the genetic algorithm parameter optimization device based on the composite entropy and the genetic algorithm parameter optimization method based on the composite entropy are in a one-to-one correspondence relationship, and here, technical details and technical effects related to each module/unit and the above process steps are the same, and are not described in detail here, please refer to the above genetic algorithm parameter optimization method based on the composite entropy.
Referring now to FIG. 8, an electronic device (e.g., a schematic structural diagram of an electronic device or server 500. an electronic device in an embodiment of the present disclosure may include, but is not limited to, a holder such as a cell phone, a tablet, a laptop, a desktop, a kiosk, a server, a workstation, a television, a set-top box, smart glasses, a smart watch, a digital camera, an MP4 player, an MP5 player, a learning machine, a point-reading machine, an electronic book, an electronic dictionary, a vehicle-mounted terminal, a Virtual Reality (VR) player, or an Augmented Reality (AR) player, etc. the electronic device shown in FIG. 8 is merely an example and should not impose any limitations on the functionality or scope of use of an embodiment of the present disclosure.
As shown in fig. 8, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 8 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. When executed by the processing device 501, performs the above-described functions defined in the methods of the embodiments of the present disclosure
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. 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 of the computer readable storage medium may include, but are not limited to: 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 present disclosure, 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. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to reference the composite entropy based genetic algorithm parameter optimization method.
Computer program code for carrying out operations for aspects of the present disclosure 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. 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 flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In summary, the invention constructs a network model based on a genetic algorithm, links genetic operation in the genetic algorithm with network construction, and is used for measuring important nodes in a network structure by considering a composite entropy which is a measurement index of influence of nodes and links on network characteristic indexes and topological structures, so as to calculate the influence of corresponding network models on the composite entropy under different iteration times, cross probabilities and variation probabilities, and judge whether parameters of the genetic algorithm are correctly and reasonably set according to the composite entropy; meanwhile, the invention achieves the purpose of theoretically guiding the iteration times and the selection of the operation operator, avoids subjectivity in empirical selection of the operation operator in the existing research, provides certain theoretical guidance for the iteration times of the genetic algorithm and the selection of the operation operator, and provides a brand new visual angle for revealing the evolution process of the genetic algorithm, so that the genetic algorithm can be more applied, and more practical engineering optimization problems are solved.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A genetic algorithm parameter optimization method based on composite entropy is characterized by comprising the following steps:
constructing a network model based on a genetic algorithm;
converting individuals in the population into network nodes by using a conversion rule, and converting crossover and variation in genetic operation into edge connection of a network;
calculating measure indexes of the corresponding network models of the genetic algorithm under different iteration times, cross probabilities and variation probabilities, and calculating composite entropy according to the measure indexes;
and judging the rationality of the genetic algorithm parameters according to the variation trend of the composite entropy so as to optimize the genetic algorithm parameters.
2. The method for optimizing genetic algorithm parameters based on composite entropy according to claim 1, further comprising: carrying out networked modeling by utilizing a genetic algorithm, and depicting N individuals in the population as N nodes in the network; wherein, the transmission of gene information or genetic operation exists between any two nodes, and the two nodes are connected by a path.
3. A method for composite entropy based genetic algorithm parameter optimization according to claim 1 or 2, wherein the expression of the composite entropy is:
where CE is the composite entropy, alpha is the weighting factor of the node in the network, beta is the weighting factor of the number of the connecting edges in the network, APL is the average path length in the network,
as a networkMiddle node kiAnd kjThe length of the path between.
4. The genetic algorithm parameter optimization method based on composite entropy as claimed in claim 1, characterized in that a Pajek tool is used to analyze a network model after genetic algorithm iteration to obtain a measure index of each node in the network; calculating the length change entropy of the network node degree entropy path according to the measure indexes of the network, calculating the weighted composite entropy of the network according to the length change entropy of the network node degree entropy path, and selecting the largest weighted composite entropy to perform weighted calculation to obtain the composite entropy.
5. The method for optimizing genetic algorithm parameters based on composite entropy according to claim 1 or 4, wherein different composite entropies are obtained according to different generated network models, wherein the total number of nodes, the total number of connected edges, the number of connected edges of each joint and the weight in the network are changed by using the cross rate and the variation rate of the genetic algorithm, so that the composite entropy under different iteration times, cross probabilities and variation probabilities is calculated.
6. The method for optimizing genetic algorithm parameters based on composite entropy according to claim 1, further comprising: if the change of the composite entropy is in a decreasing trend, the ratio of the core node to the path in the network constructed by using the genetic algorithm parameters is reduced, the network is stable, and the network evolution is inert, the genetic algorithm parameters are judged to be reasonable; if the change of the composite entropy presents an increasing trend, the ratio of the core nodes to the paths in the network constructed by using the genetic algorithm parameters is increased, the network is unstable, and the network evolution is positive, so that the genetic algorithm parameters are judged to be unreasonable.
7. A composite entropy based genetic algorithm parameter optimization method as claimed in claim 1, wherein the selection method in genetic manipulation adopts roulette selection method, the crossing method in genetic manipulation adopts two-point sequential crossing method, and the mutation method in genetic manipulation adopts mutation method based on priority matrix.
8. A genetic algorithm parameter optimization device based on composite entropy is characterized by comprising the following components:
the network construction module is used for constructing a network model based on a genetic algorithm;
the rule conversion module is used for converting individuals in the population into network nodes by using a conversion rule and converting intersection and variation in genetic operation into network edge connection;
the composite entropy calculation module is used for calculating measure indexes of the corresponding network models of the genetic algorithm under different iteration times, cross probabilities and variation probabilities, and calculating composite entropy according to the measure indexes;
and the parameter optimization module is used for judging the rationality of the genetic algorithm parameters according to the variation trend of the composite entropy so as to optimize the genetic algorithm parameters.
9. An electronic device, characterized in that: the method comprises the following steps:
one or more processing devices;
a memory for storing one or more programs; when executed by the one or more processing devices, cause the one or more processing devices to implement the composite entropy-based genetic algorithm parameter optimization method of any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program for causing a computer to execute the composite entropy-based genetic algorithm parameter optimization method according to any one of claims 1 to 7.
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| CN116933880B (en) * | 2023-06-14 | 2024-05-07 | 北京中科弧光量子软件技术有限公司 | Quantum circuit depth optimization method and system based on genetic algorithm and electronic equipment |
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