CN104504477B - A kind of method for optimizing route based on birds spore mechanism - Google Patents
A kind of method for optimizing route based on birds spore mechanism Download PDFInfo
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Abstract
The present invention discloses a kind of method for optimizing route based on birds spore mechanism, including：Generate some feasible paths at random, the chromosome of the corresponding birds of each paths, a gene on each node homologue, mrna length is path length, and gene order is path node order；It is determined that by the cost needed for feasible path as fitness function；The how many pairs of feasible paths according to being spent by every feasible path are ranked up, and it is classified；The number of the feasible path using more husband's polygyny classes is calculated, and generates a certain proportion of new feasible path at random again, substitutes the feasible path for belonging to more husband's polygyny classes；Each bar feasible path carries out breeding reconstruct according to its affiliated birds spore mode, after completing breeding reconstruct, compares parent individuality and the path length of offspring individual, retains and spend few feasible path；Iteration reaches the iterations threshold value of setting, obtains some feasible paths；Minimum path is spent as preferred path to being chosen in the feasible path of acquisition.
Description
Technical Field
The invention relates to the field of network path optimization, in particular to a path optimization method based on an avian species evolution mechanism.
Background
The shortest path problem is the most basic problem in network optimization, the routing distribution of a multi-hop network and the application of the routing distribution in the industries such as accident rush repair, traffic guidance, GPS navigation and the like are very wide, and a rapid path optimizing algorithm can enable a system to fully utilize network resources and meet customer requirements.
(1) The most classical method is Dijkstra algorithm, which is also called single-source shortest path, and can solve the shortest path from one vertex to all reachable vertices. The method is mainly characterized in that the expansion is carried out layer by layer towards the outer part by taking the starting point as the center until the end point is reached. The Dijkstra algorithm has the advantage that 100% can obtain the optimal solution of the shortest path, but has the defect of low efficiency because the nodes for traversing calculation are many.
(2) Chang Wook Ahn et al propose to solve the shortest path problem by using a genetic algorithm, the method decomposes a feasible path into a plurality of sections, under the premise of ensuring the topological connectivity of the sections, uses a crossover operator to interact with variable sections, uses a mutation operator to introduce new sections, and continuously iterates until the algorithm converges to obtain the shortest path. The main disadvantages of this method are two: firstly, the algorithm has limited exploration capacity for a new space and is easy to converge to a local optimal solution. The algorithm belongs to a random algorithm, needs multiple operations, has poor reliability of results and cannot obtain a solution stably.
Birds are the biggest tetrapod vertebrates in the world, the breeding and evolution process and optimization problems of the birds have a lot of commonness, and the birds have 5 breeding modes including parthenogenesis, single preparation, one-man-wife system, one-wife-multi-husband system and one-wife-multi-wife system, and each bird can breed offspring according to the mode of the bird.
Disclosure of Invention
In order to overcome the defects of the existing path searching method, the invention provides a path optimization method based on an avian species evolution mechanism, the shortest path is searched, the method simulates a specific derived offspring mode of avian species, the shortest path problem in graph theory is solved by simulating a breeding and evolution mode of avian, and the optimizing efficiency and the convergence speed can be improved.
In order to solve the above disadvantages, the technical scheme of the invention is as follows:
a path optimization method based on an avian species evolution mechanism comprises the following steps:
s1, randomly generating a plurality of feasible paths, wherein each path corresponds to a chromosome of a bird, each node corresponds to a gene on the chromosome, the length of the gene is the path length, and the sequence of the genes is the sequence of the path nodes;
s2, determining cost required by passing through a feasible path as an adaptive function;
s3, sorting the feasible paths according to the cost of passing through each feasible path, and classifying the feasible paths, wherein the specific classification mode is as follows:
1) Dividing the feasible paths into a female type and a male type according to the cost, wherein when the cost is less than or equal to a threshold value A, the corresponding feasible paths belong to the female type, otherwise, the feasible paths belong to the male type;
2) Classifying the feasible paths belonging to the female category, and classifying the feasible paths into a parthenogenesis category and a wife-more-time-making category according to the cost, wherein when the cost is less than or equal to a threshold B, the corresponding feasible paths belong to the parthenogenesis category, otherwise, the corresponding feasible paths belong to the wife-more-time-making category;
classifying feasible paths belonging to a male class, and classifying the feasible paths into a single preparation class, a one-man-wife class and a multi-man-wife class according to cost, wherein when the cost is less than or equal to a threshold value C, the corresponding feasible paths belong to the single preparation class, when the cost is more than the threshold value C and less than or equal to a threshold value D, the feasible paths belong to the one-man-wife class, and when the cost is more than the threshold value D, the feasible paths belong to the multi-man-wife class;
wherein A > B, D > C > A;
s4, calculating the number of feasible paths belonging to the multi-husband multi-wife system to be E, and generating alpha E new feasible paths at random again, wherein alpha is more than 0 and less than 1 to replace the alpha E feasible paths belonging to the multi-husband multi-wife system;
s5, propagating and reconstructing each feasible path according to the bird species evolution mode to which the feasible path belongs, comparing the path lengths of the parent individuals and the offspring individuals after the propagation reconstruction is completed, and reserving feasible paths with low cost;
s6, repeating the steps S3 to S5 until a set iteration number threshold is reached, and obtaining a plurality of feasible paths;
and S7, selecting the path with the least cost from the feasible paths obtained in the step S6 as the preferred path.
Preferably, the specific way of breeding and reconstructing each feasible path in step S5 according to the bird species evolution way to which it belongs is as follows:
when the feasible path belongs to the parthenogenesis class, the reproduction reconstruction mode is as follows:
101 For each node) a node variation probability rn is generated i When n is i When the node variation probability threshold is larger than the node variation probability threshold, the node is varied, and the gene segment between two variation nodes is the gene segment to be varied;
102 For each gene fragment to be mutated, a gene mutation probability rpv is generated j When rpv j When the probability of the gene segment variation is larger than the threshold value of the gene segment variation probability, the gene segment is varied, and a feasible path is regenerated according to the head node and the tail node of the segment of the gene to replace the corresponding gene segment to be varied;
when the feasible path belongs to the single preparation class, the feasible path and the feasible path belonging to the single sexual reproduction class or the one wife multi-husband class are propagated and reconstructed to obtain the filial generation individuals, and the specific mode is as follows:
201 Searching the same node between two feasible paths, and taking a part of road section sets with the same head node and tail node as gene segments to be exchanged;
202 When the gene fragment to be exchanged has an exchange probability of rpc j If the gene fragment is larger than the exchange threshold, the gene fragment is exchanged;
when the feasible path belongs to a husband and wife system, breeding and reconstructing the feasible path with the female system to obtain the offspring individual, wherein the specific mode is as follows:
301 Searching for gene fragments belonging to the same feasible path in the male class as all other feasible paths belonging to the female class;
302 Finding out the gene segments with the threshold value larger than the gene exchange probability, and then exchanging the allele segments with the maximum mutation probability;
when the feasible path belongs to a wife-dof system, breeding and reconstructing the feasible path and the feasible path belonging to a male system to obtain the offspring individual, wherein the specific mode is as follows:
401 Search for gene fragments that belong to the same feasible path in the female class as all other feasible paths in the male class;
402 Finding out the gene segments with the threshold value larger than the gene exchange probability, and then exchanging the allele segments with the maximum mutation probability;
when the feasible path belongs to the multi-husband and multi-wife system class, the feasible path and the feasible path belonging to the female class are propagated and reconstructed to obtain the offspring individual, and the specific mode is as follows:
501 All gene fragments belonging to the same female class feasible pathway as all other feasible pathways belonging to the male class are searched;
502 Find out the gene fragment with threshold value larger than the gene exchange probability, and then use the allele fragment with maximum mutation probability to exchange.
Preferably, the determined fitness function is path length, time, delay, cost or emissions,
when the adaptive function is the path length, the feasible paths are classified according to the length of the paths, the cost is more when the paths are long, and the cost is less when the paths are short;
when the adaptive function is time, the feasible paths are classified according to the time of passing the feasible paths, the time is long, and the cost is high, and the time is short, and the cost is low;
when the adaptive function is delayed, classifying the feasible paths according to the time delay of the feasible paths, wherein the time delay is long, the cost is high, and the time delay is short, the cost is low;
when the adaptive function is cost, classifying the feasible paths according to the cost required by the feasible paths, wherein the cost is high if the cost is high, and the cost is low if the cost is low;
when the adaptive function is discharging, that is, the feasible paths are classified according to the discharge amount required by the feasible paths, more discharging costs more, and less discharging costs less.
Preferably, the specific calculation method of the path length of the feasible path is as follows:
C ij for the value coefficient between the node i and the node j, S, D respectively represents the head node and the tail node of the target path; i is ij Indicating whether the link from node i to node j belongs to the target path,
preferably, after the plurality of feasible paths are obtained in step S6, the repeated segments in the feasible paths are also eliminated, that is, the segments in the repeated nodes are removed.
Compared with the prior art, the invention has the beneficial effects that:
(1) Compared with the traditional Dijkstra algorithm, the method realizes a more efficient shortest path selection mode by utilizing the bird evolution algorithm and the path gene coding.
(2) The optimization strategy simulating bird breeding evolution enables the algorithm to have higher convergence speed and solving capability compared with other heuristic search algorithms.
Drawings
Fig. 1 is a schematic diagram of feasible path coding.
Fig. 2 is a schematic diagram of the manner in which birds parthenogenesis generates new pathways.
Fig. 3 is a schematic of the manner in which birds are individually formulated to create a new pathway.
FIG. 4 is a flow chart of the algorithm of the present invention.
Fig. 5 is a schematic diagram of experimental road network (guangzhou university city).
Fig. 6 is a schematic diagram of the shortest path found in fig. 5.
Fig. 7 is a graph of the convergence of an optimization algorithm using the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, but the embodiments of the present invention are not limited thereto.
The shortest path optimizing is a basic problem in network optimization and graph theory, and means that from a certain vertex, a path with the minimum sum of weights on each edge is obtained from paths which are passed from the edge of a graph to another vertex, namely the shortest path.
The mathematical expression of the shortest path optimization problem is as follows: the network topology can be expressed by a directed graph G (N, a). Wherein N represents a set of nodes in the network, A represents a set of directed edges in the network, and each directed edge corresponds to a value coefficient C ij The attributes (such as time, length, etc.) of the directed edge are represented, and subscripts i and j are head and tail nodes of the directed edge, and the head and tail nodes of the path are respectively S and D. I is ij And indicating whether the link from the node i to the node j belongs to the target path.
I ij The following definitions can be made:
the shortest path network optimization algorithm is described as follows
Minimize
Subject to
And
I ij ∈{0,1},for all i.
The optimization aims to select a path so that the cost of the passed path is minimum (when the value coefficient is the parameters of road section length, time, delay, cost, emission and the like, the optimal solution is the shortest path). Wherein a first constraint in the optimization model ensures that paths are connected between start and end points and a second constraint ensures that no duplicate paths occur.
Expression of viable pathways using avian genes
In the bird breeding evolution optimization algorithm, each bird individual represents a feasible solution, namely a feasible path in the embodiment, before the evolution algorithm is applied, the feasible solution needs to be subjected to gene coding, for a feasible path containing N nodes, a chromosome with the length of N can be used for representation, N is smaller than the maximum value of the nodes of the road network, and the sequence of the nodes on the path corresponds to the sequence of genes on the chromosome.
FIG. 1 shows an example of encoding a feasible path into a chromosome, where the first gene and the last gene correspond to the start and end points of the path. The feasible path should satisfy two conditions of connectivity and non-repetition, and it is noted that it is relatively simple to ensure the connectivity of the path when searching for the initial feasible path, but repeated road segments often appear in the path, and at this time, only the nodes between the same nodes need to be removed.
Classifying feasible paths according to bird breeding modes
Birds are divided into males and females, each individual can reproduce and evolve according to different modes, and the following feasible path classification method is designed according to the species evolution principle and the law in nature.
Firstly, randomly generating a plurality of feasible paths, wherein each path is a chromosome of a bird, each node corresponds to a gene on the chromosome, the length of the gene is the path length, and the sequence of the genes is the sequence of the path nodes. An adaptive function is then determined, which in this embodiment is set to path length, and birds are then ranked according to the path length of each feasible path. And then, intercepting corresponding intervals according to the sequence for classification, which is specifically as follows.
Individuals with the better genes were first divided by gender into the female population and the remainder into the male population. And secondly, dividing the female population into two groups according to the reproduction mode, dividing individuals with better genes into a first group, reproducing in a parthenogenesis mode, and dividing the rest into a second group, and reproducing by a wife husband system. The female group can be divided into three groups, the individuals with better genes are divided into the first group and bred by single preparation, the individuals with next-best genes are divided into the second group and bred by one-man and many-wife, and the rest are divided into the third group and bred by many-man and many-wife.
The classification of the feasible paths is as follows:
1) Dividing the feasible paths into a female type and a male type according to the length of the paths, wherein when the path length is less than or equal to a threshold value A, the corresponding feasible paths belong to the female type, otherwise, the feasible paths belong to the male type;
2) Classifying the feasible paths belonging to the female category, and classifying the feasible paths into a parthenogenesis category and a wife-Do-Shu category according to the path length, wherein when the path length is less than or equal to a threshold value B, the corresponding feasible paths belong to the parthenogenesis category, otherwise, the feasible paths belong to the wife-Do-Shu category;
classifying feasible paths belonging to a male class, and classifying the feasible paths into a single preparation class, a one-man-wife class and a multi-man-wife class according to path lengths, wherein when the path lengths are less than or equal to a threshold value C, the corresponding feasible paths belong to the single preparation class, when the path lengths are greater than the threshold value C and less than or equal to a threshold value D, the feasible paths belong to the one-man-wife class, and when the path lengths are greater than the threshold value D, the feasible paths belong to the multi-man-wife class;
wherein A > B, D > C > A.
Reproduction reconstruction of feasible paths according to bird evolution mode
Birds are divided into males and females, with females possessing the best genes in the population. Birds have 5 kinds of breeding modes including sexual reproduction, simple preparation, one-man-wife system, one-wife system and multi-wife system. In the algorithm, new solution data is generated in a corresponding manner
(1) Parthenogenesis, namely female birds can independently finish the propagation process without the help of male birds, some gene segments of female birds self chromosomes can be randomly changed in the propagation process corresponding to the variation of female birds self genes, and a new path generation method corresponding to a parthenogenesis mode is as follows: the process of simulating the new path generated by parthenogenesis of birds is divided into two steps, wherein the first step randomly generates gene fragments needing variation, and the second step regenerates a feasible path at a corresponding gene position (road section node position) to complete variation, as shown in figure 2.
The first step is as follows: generating a node variation probability rn for each node i When an rn i When the mutation probability is larger than the node mutation probability threshold, the node is mutated, and the gene segment between two mutated nodes is the gene segment to be mutated.
The second step is that: generating a gene variation probability rpv for each gene fragment to be varied j When rpv j When the probability of the gene fragment mutation is larger than the threshold value of the gene fragment mutation probability, the gene fragment is mutated, and a feasible path is regenerated according to the head node and the tail node of the gene fragment to replace the original corresponding part.
(2) And (4) single preparation, namely, mating one male bird with one female bird to complete the breeding process. In this process, the "allelic fragments" of two birds (referring to the sets of partial segments of a feasible path with the same head-to-tail nodes, as shown in fig. 3) are randomly exchanged, and the corresponding single-formulation new path generation method is as follows:
the process of mimicking the new pathway generated by avian haplotypes is divided into two steps, the first step searching for the same gene fragment between the two chromosomes. The second step is random exchange of the corresponding gene fragments. Rpc in FIG. 3 j For randomly generated gene fragment exchange probabilities, gene fragments are exchanged when the probability is greater than an exchange threshold.
(3) It is prepared by one man and two wives, one wife and two wives.
a) One man and many wives make that one male bird mates with many female birds to complete the breeding process. In this process, the "allelic fragments" (referring to the partial road segment sets with the same head and tail nodes in a feasible path) of a male bird and a plurality of female birds are randomly exchanged, and the generation method of the corresponding new path of a couple system is as follows:
the process of mimicking the new pathway produced by avian couples is divided into two steps, the first step of searching for the same gene fragments between the male chromosomes as between all other female chromosomes. In the second step, the gene segments with the threshold value larger than the gene exchange probability are found out, and then the allele segments with the maximum mutation probability are used for exchange.
b) One wife is a husband system, namely, a female bird is mated with a plurality of male birds to complete the breeding process. The new path generation method is similar to that of a couple system and is not described in detail.
c) Many husbandry and wife make the male birds mate with the female birds to complete the breeding process. In the BMO algorithm, the new path generation method is similar to a couple system, and is not described in detail.
In a new path generated by the algorithm, repeated road sections appear, and at the moment, the repeated road sections need to be eliminated, and only the road sections in the repeated nodes need to be removed when the repeated road sections are eliminated.
The flow of the path optimization algorithm simulating the bird evolution mode is shown in FIG. 4, and comprises the following steps:
s1, randomly generating a plurality of feasible paths, wherein each path corresponds to a chromosome of a bird, each node corresponds to a gene on the chromosome, the gene length is the path length, and the gene sequence is the path node sequence;
s2, determining the path length as an adaptive function;
s3, sorting the feasible paths according to the path length of each feasible path, and classifying the feasible paths, wherein the specific classification mode is as follows:
1) Dividing the feasible paths into a female type and a male type according to the length of the paths, wherein when the path length is less than or equal to a threshold value A, the corresponding feasible paths belong to the female type, otherwise, the feasible paths belong to the male type;
2) Classifying the feasible paths belonging to the female category, and classifying the feasible paths into a parthenogenesis category and a wife-Do-Shu category according to the path length, wherein when the path length is less than or equal to a threshold value B, the corresponding feasible paths belong to the parthenogenesis category, otherwise, the feasible paths belong to the wife-Do-Shu category;
classifying feasible paths belonging to a male class, and classifying the feasible paths into a single preparation class, a one-man-wife class and a multi-man-wife class according to path lengths, wherein when the path lengths are less than or equal to a threshold value C, the corresponding feasible paths belong to the single preparation class, when the path lengths are greater than the threshold value C and less than or equal to a threshold value D, the feasible paths belong to the one-man-wife class, and when the path lengths are greater than the threshold value D, the feasible paths belong to the multi-man-wife class;
wherein A > B, D > C > A;
s4, calculating the number of feasible paths belonging to the multi-husband multi-wife system to be E, and generating alpha E new feasible paths at random again, wherein alpha is more than 0 and less than 1 to replace the alpha E feasible paths belonging to the multi-husband multi-wife system; this can increase the heuristics of the algorithm to avoid falling into local optima;
s5, breeding and reconstructing each feasible path according to the bird species evolution mode to which the feasible path belongs, comparing the path lengths of the parent individuals and the offspring individuals after the breeding and reconstruction are completed, and reserving the feasible path with short path length;
s6, repeating the steps S3 to S5 until a set iteration number threshold is reached, obtaining a plurality of feasible paths, and eliminating repeated road sections in the feasible paths, namely removing the road sections in the repeated nodes;
and S7, selecting the path with the minimum path length from the feasible paths obtained in the step S6 as a preferred path.
And aiming at different road network scales and road network complexity, the optimal setting value of the feasible path gene classification parameter is not used. Referring to the general rules and related experiments of specific biological behaviors in nature, in this embodiment, the general rules set in step S3 regarding feasible path gene classification parameters are as follows:
the next first class of gene ranks is parthenogenetic females (5%)
The second category of the gene ranking was a female (5%) with one male and one female (mated with the first 10 males of the gene ranking)
The third gene rank is a single-prepared male bird (50%)
The next fourth gene line is a male bird (30%)
The fifth gene rank is the male bird of Dufu wife (10%)
Wherein the third, fourth and fifth gene ranks are male birds, and the mating objects are female birds with the first and second gene ranks.
Example 1
The road network of the experiment of the embodiment is a homemade road network of Guangzhou university cities, the number of road network nodes is 335, and the number of arc segments is 408, as shown in fig. 5.
The values of the parameters in this embodiment are as follows: the initial feasible path is 100, namely the bird population size is 100; 5 birds which are bred by one sex, 5 birds which are bred by one wife, 50 birds which are bred by one preparation, 30 birds which are bred by one wife and 10 birds which are bred by one wife are adopted. The node mutation probability is 10, the cross probability and the mutation probability of the allele fragments are 0.5, and the upper limit value of the evolution algebra is defined as 10 generations. The start and end nodes of the path are identified in fig. 6. Fig. 6 shows the shortest path (chain line) found by the algorithm of the present embodiment. Fig. 7 is a convergence curve, where the ordinate is the path length (m) and the abscissa is the number of iterations, and the algorithm starts to converge in the fourth generation in this experiment, and the shortest path length is 4908 m.
The above-described embodiments of the present invention do not limit the scope of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and scope of the present invention shall be included in the protection scope of the claims of the present invention.
Claims (5)
1. A path optimization method based on an avian species evolution mechanism is applied to the traffic field and used for solving the path optimization of traffic paths, and the process comprises the following steps:
s1, randomly generating a plurality of feasible paths, wherein each path corresponds to a chromosome of a bird, each node corresponds to a gene on the chromosome, the length of the gene is the path length, and the sequence of the genes is the sequence of the path nodes;
s2, determining cost required by passing through a feasible path as an adaptive function;
s3, sorting the feasible paths according to the cost of passing through each feasible path, and classifying the feasible paths, wherein the specific classification mode is as follows:
1) Dividing the feasible paths into a female type and a male type according to the cost, wherein when the cost is less than or equal to a threshold value A, the corresponding feasible paths belong to the female type, otherwise, the feasible paths belong to the male type;
2) Classifying the feasible paths belonging to the female category, and classifying the feasible paths into a parthenogenesis category and a wife-more-time-making category according to the cost, wherein when the cost is less than or equal to a threshold B, the corresponding feasible paths belong to the parthenogenesis category, otherwise, the corresponding feasible paths belong to the wife-more-time-making category;
classifying feasible paths belonging to a male class, and classifying the feasible paths into a single preparation class, a one-man-wife class and a multi-man-wife class according to cost, wherein when the cost is less than or equal to a threshold value C, the corresponding feasible paths belong to the single preparation class, when the cost is more than the threshold value C and less than or equal to a threshold value D, the feasible paths belong to the one-man-wife class, and when the cost is more than the threshold value D, the feasible paths belong to the multi-man-wife class;
wherein A > B, D > C > A;
s4, calculating the number of feasible paths belonging to the multi-husband multi-wife system to be E, and generating alpha E new feasible paths at random again, wherein alpha is more than 0 and less than 1 to replace the alpha E feasible paths belonging to the multi-husband multi-wife system;
s5, propagating and reconstructing each feasible path according to the bird species evolution mode to which the feasible path belongs, comparing the path lengths of the parent individuals and the offspring individuals after the propagation reconstruction is completed, and reserving feasible paths with low cost;
s6, repeating the steps S3 to S5 until a set iteration number threshold is reached, and obtaining a plurality of feasible paths;
and S7, selecting the path with the least cost from the feasible paths obtained in the step S6 as the preferred path.
2. The method for optimizing a path based on an avian species evolution mechanism according to claim 1, wherein the reproduction reconstruction of each feasible path in step S5 according to the avian species evolution method to which it belongs is specifically as follows:
when the feasible path belongs to the parthenogenesis class, the reproduction reconstruction mode is as follows:
101 For each node) a node variation probability rn is generated i When n is i When the mutation probability of the node is greater than the threshold value of the mutation probability of the node, the node is mutated, and the gene segment between two mutated nodes is the gene segment to be mutated;
102 For each gene fragment to be mutated, a gene mutation probability rpv is generated j When rpv j When the probability of the gene segment variation is larger than the threshold value of the gene segment variation probability, the gene segment is varied, and a feasible path is regenerated according to the head node and the tail node of the segment of the gene to replace the corresponding gene segment to be varied;
when the feasible path belongs to the single preparation class, the feasible path and the feasible path belonging to the single sexual reproduction class or the wife-doff class are propagated and reconstructed to obtain the offspring individuals, and the concrete mode is as follows:
201 Searching the same node between two feasible paths, and taking a part of road section sets with the same head node and tail node as gene segments to be exchanged;
202 When the gene fragment to be exchanged has an exchange probability of rpc j If the gene fragment is larger than the exchange threshold, the gene fragment is exchanged;
when the feasible path belongs to a husband and wife system, the feasible path and the feasible path of the female class are propagated and reconstructed to obtain offspring individuals, and the specific mode is as follows:
301 Searching for gene fragments belonging to the same feasible path in the male class as all other feasible paths belonging to the female class;
302 Finding out the gene segments with the threshold value larger than the gene exchange probability, and then exchanging the allele segments with the maximum mutation probability;
when the feasible path belongs to a wife-dof system, breeding and reconstructing the feasible path and the feasible path belonging to a male system to obtain the offspring individual, wherein the specific mode is as follows:
401 Searching for gene fragments belonging to the same feasible path in the female class as all other feasible paths belonging to the male class;
402 Finding out the gene segments with the threshold value larger than the gene exchange probability, and then exchanging the allele segments with the maximum mutation probability;
when the feasible path belongs to the multi-husband and multi-wife system class, the feasible path and the feasible path belonging to the female class are propagated and reconstructed to obtain the offspring individual, and the specific mode is as follows:
501 Searching for gene fragments having all feasible paths belonging to the female class identical to all feasible paths belonging to the male class;
502 Find out the gene fragment with threshold value larger than the gene exchange probability, and then use the allele fragment with maximum mutation probability to exchange.
3. The method of claim 1 or 2, wherein the determined fitness function is path length, time, delay, cost, or emissions,
when the adaptive function is the path length, classifying the feasible paths according to the length of the paths, wherein the path length is more cost, and the path length is less cost;
when the adaptive function is time, classifying the feasible paths according to the time of the feasible paths, wherein the time is long, the cost is high, and the time is short, the cost is low;
when the adaptive function is delayed, classifying the feasible paths according to the time delay of the feasible paths, wherein the time delay is long, the cost is high, and the time delay is short, the cost is low;
when the adaptive function is cost, classifying the feasible paths according to the cost required by the feasible paths, wherein the cost is high if the cost is high, and the cost is low if the cost is low;
when the adaptive function is discharging, that is, the feasible paths are classified according to the discharge amount required by the feasible paths, more discharging costs more, and less discharging costs less.
4. The method for optimizing a path based on an avian species evolution mechanism according to claim 3, wherein the path length of the feasible path is calculated as follows:
C ij for the value coefficient between the node i and the node j, S, D respectively represents the head node and the tail node of the target path; i is ij Indicating whether the link from node i to node j belongs to the target path,
5. the method for optimizing a route based on an avian species evolution mechanism according to claim 1, wherein after the step S6 of obtaining a plurality of feasible routes, the elimination of the repeated sections is performed, i.e. the sections in the repeated nodes are removed.
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