CN111083577A - Power communication topological structure optimization method - Google Patents

Power communication topological structure optimization method Download PDF

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CN111083577A
CN111083577A CN201910994615.3A CN201910994615A CN111083577A CN 111083577 A CN111083577 A CN 111083577A CN 201910994615 A CN201910994615 A CN 201910994615A CN 111083577 A CN111083577 A CN 111083577A
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CN111083577B (en
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方景辉
孙一凡
汤东升
洪晓燕
卢奇
高梅鹃
钟伟东
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Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention relates to the field of power communication, and discloses a power communication topological structure optimization method, which comprises the following steps: A) the method comprises the steps of counting optical cable line data in the power communication network; B) establishing a topological structure chart by using optical cable line data; C) establishing a reliability and failure rate constraint condition, and constructing a target function which integrates the economy and the influence degree of the topological structure; D) solving the objective function by using a genetic algorithm; E) and obtaining the network line after the topological structure of the power communication network is optimized. According to the invention, the optical cable is optimized by establishing the topological structure diagram, an objective function integrating the economy and the influence degree of the topological structure is constructed under the constraint condition of meeting the reliability of the power communication network and the failure rate of the communication station, and the objective function is solved by utilizing a genetic algorithm, so that the power communication network topological structure with good economy and small fault occurrence influence degree is obtained.

Description

Power communication topological structure optimization method
Technical Field
The invention relates to the field of power communication, in particular to a power communication topological structure optimization method.
Background
The power communication network is used as a special communication network of the power system, plays an important role in ensuring the safe, stable and reliable operation of the power system, and is a communication basis for the transition from the traditional power grid to the smart power grid. At present, with the rapid development of smart power grids, the network structure of the power grid is more and more complex, the network scale is large, the types of various electric power communication services carried by the power grid are gradually increased, the data volume of information is more and more, the safety and reliability of the electric power communication network directly influence the safety and stability of the operation of the power grid, and the working part of the power communication network can still maintain better connectivity under the condition of local damage of the power communication network, so the survivability of the topological structure is an important aspect of the reliability of the power communication network, and the survivability of the topological structure of the communication network refers to the capability of maintaining or recovering the performance of the network to an acceptable degree when a deterministic or random fault occurs in the network.
For example, a chinese patent document discloses "an EPON optical cable network structure applied to a 10kV grid", which is published under the number CN 204316504U, and the utility model includes: a trunk optical cable line that is an optical cable line connected to a plurality of branch trunks or branches; the optical cable system comprises a branch main optical cable line, a branch optical cable line and a branch optical cable line, wherein the branch main optical cable line is used for connecting a plurality of branch optical cables in a junction manner; the optical cable line branch comprises an optical cable line branch, wherein the optical cable line branch is a line which is only connected with 1 distribution transformer; the optical cable cross-connecting box is arranged at a position where a plurality of optical cables are collected and optical fiber scheduling/light splitting is required; the optical distributor is arranged in the optical cable cross-connecting box; the optical cable joint box is used for fixedly connecting the branch optical cable and the branch main optical cable or fixedly connecting the middle of the main optical cable; the trunk optical cable and the branch optical cable are divided by taking the optical splitter as a boundary. Although the utility model discloses an optical cable network structure has been improved from the aspect of mechanical structure, but does not optimize the optical cable from whole electric power communication network, can not solve the problem that optical cable laid economic planning.
Disclosure of Invention
The invention aims to solve the problems of poor reliability, poor economy and large influence degree when the traditional power communication network is subjected to later-stage expansion planning of an optical cable line, and provides a power communication topological structure optimization method.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power communication topological structure optimization method comprises the following steps:
A) the method comprises the steps of counting optical cable line data in the power communication network;
B) establishing a topological structure chart by using optical cable line data;
C) establishing a reliability and failure rate constraint condition, and constructing a target function which integrates the economy and the influence degree of the topological structure;
D) solving the objective function by using a genetic algorithm;
E) and obtaining the network line after the topological structure of the power communication network is optimized.
The method comprises the steps of establishing a topological structure diagram by simulating an actual electric power communication network construction state and a network structure, forming a model facing the whole electric power communication network planning, then establishing constraint conditions, and solving an objective function by using a genetic algorithm under the condition that the constraint conditions of optical cable reliability and communication station failure rate are met, so that the electric power communication network topological structure with good economy and small fault occurrence influence degree is obtained.
Further, in step a), the optical cable line data includes: n communication sites, k existing optical cable lines, m optical cable lines to be established, the cost of the optical cable lines and the reliability of the optical cable lines.
Further, step B) further comprises: the communication sites are represented by nodes, the existing optical cable lines are represented by solid lines, the optical cable lines to be constructed are represented by dotted lines, and each optical cable line is numbered.
Further, step C) establishes constraints, including the steps of:
C1) obtaining the network looping rate a of the ith optical cable line without fault by using a network connection undirected graphiAnd network connectivity rate biCalculating the reliability c of the ith optical cable linei＝ai+bi；
C2) Obtaining the network ring forming rate when the ith optical cable fails
Figure BDA0002239350810000021
And rate of network connectivity
Figure BDA0002239350810000022
Calculating a reliability difference
Figure BDA0002239350810000023
Setting a reliability threshold
Figure BDA0002239350810000024
Establishing optical cable reliability constraint conditions
Figure BDA0002239350810000025
Wherein the content of the first and second substances,
Figure BDA0002239350810000026
C3) counting the number of optical cables connected with each communication station to obtain a station optical cable number set { A }1,A2,...,AnGet the failure rate of a single communication station when it fails
Figure BDA0002239350810000027
C4) Obtaining failure of any two communication stationsHas a failure rate of
Figure BDA0002239350810000028
i ≠ j, wherein AijThe number of optical cables connected between the ith communication station and the jth communication station is represented;
C5) the failure rate of any three communication stations is obtained as
Figure BDA0002239350810000029
C6) D is obtained as failure rates of any 4, 5, … and n communication stations which fail respectively4、d5、...、dnSetting a failure rate threshold value mu, and establishing a constraint condition d < mu of the failure rate of the communication station, wherein
Figure BDA0002239350810000031
The reliability of the optical cable line is defined from two aspects of ring forming rate and network communication rate, when the optical cable line has a fault, the connectivity of a communication network is deteriorated, an optical cable reliability constraint condition is established by utilizing the reliability difference of the optical cable line when no fault occurs and the optical cable line has the fault, if one communication station has the fault, the optical cable connected with the fault communication station cannot carry out normal communication, and the failure rate constraint condition of the communication station is established by fully considering various combination conditions of the faults of the communication station.
Further, the network ring forming rate of the ith optical cable line
Figure BDA0002239350810000032
qiIndicating the number of communication stations constituting the ring structure of a communication network, and the network connection rate
Figure BDA0002239350810000033
riIndicating the number of communication stations capable of communicating normally.
The ring structure of the communication network can ensure that the failure of one optical cable does not affect the whole communication network area, and the communication can be continued through the other side of the ring, so the ring forming rate needs to be considered when the constraint condition is established. The invention defines the network communication rate as the ratio of the number of communication stations capable of normally communicating to the number of communication stations of the whole communication network.
Further, in step C), constructing the objective function includes the steps of:
C11) calculating power communication network efficiency
Figure BDA0002239350810000034
dkjThe shortest optical cable length between the kth communication station and the jth communication station;
C12) calculating the influence degree of the ith optical cable when the ith optical cable fails
Figure BDA0002239350810000035
Indicating the efficiency of the power communication network in case of failure of the ith optical cable, ηiIndicating the efficiency of the power communication network before the i-th cable fails, c* iIndicating reliability of the ith cable in the event of failure, ciRepresenting the reliability of the ith optical cable before the fault occurs;
C13) obtaining an influence weight rho when the ith optical cable failsi；
C14) Calculating the final influence degree of the ith optical cable when the ith optical cable fails
Figure BDA0002239350810000036
C16) Obtaining a cost set of fiber optic lines { e }1,e2,...,ei,...,ek+mIn which eiRepresenting the construction cost of the ith optical cable line;
C17) when the constraint condition is satisfied
Figure BDA0002239350810000037
Next, an objective function is constructed
Figure BDA0002239350810000038
Wherein z isiA value of 1 or0, when xiA value of 1 indicates that the ith optical cable has been constructed or is predicted to be constructed, when xiA value of 0 indicates that the ith cable is not considered for construction.
Influence degree weight rho when fault occurs to ith optical cableiThe method comprises the steps of comprehensively considering relevant factors such as optical cable types, geographic environments and optical cable lengths, and then distributing different influence weights to different optical cable lines. From the perspective of cost, different cable lines have different costs due to length, operation, and the like. In addition, if one line fails, the influence degree of the fault is larger, the influence on the communication capacity of the whole network is larger after the fault is shown, and the optimal network topology structure meeting the constraint condition is obtained by establishing a target function integrating the economy and the influence degree.
Further, the solving of the objective function by using the genetic algorithm in the step D) comprises the steps of:
D1) initializing a population, setting cross probability and variation probability, setting a base factor on each chromosome as (k + m), setting the population scale as N, and recording the population of the 1 st generation as
Figure BDA0002239350810000041
Wherein the content of the first and second substances,
Figure BDA0002239350810000042
represents the nth chromosome of the population of the t generation,
Figure BDA0002239350810000043
a first gene representing an Nth chromosome of the population of the t generation, the position of the gene representing the number of the optical cable line;
D2) coding is carried out, each chromosome represents a group of solutions of the objective function, the genes represent optical cable lines, the genes are 1 and 0 respectively represent that the optical cable lines are built or predicted to be built, and the genes represent that the optical cable lines are not built;
D3) evaluating each group of solutions in the population, calculating the reliability and failure rate of each group of solutions, obtaining H chromosomes meeting constraint conditions, wherein H is less than or equal to N, calculating objective function values, and sequencing the chromosomes from small to large according to the objective function values;
D4) setting a stopping condition, if the stopping condition is met, ending the algorithm to obtain an optimal solution, and if the stopping condition is not met, entering the step D5);
D5) selecting a chromosome to obtain a selected population;
D6) carrying out cross operation on the selected chromosomes according to the cross probability, taking two chromosomes in the population as parents, carrying out cross recombination on partial genes of the chromosomes to form new chromosomes, and obtaining a crossed population;
D7) setting a variation position in the crossed population, changing the gene value at the variation position, carrying out individual gene variation according to variation probability, mutating the gene with the value of 0 to 1, and mutating the gene with the value of 1 to 0;
D8) generating a new generation of population, repeating step D3).
The genetic algorithm is a search type heuristic algorithm and is used for solving the optimization problem of solving the function maximum value. The solution of the solving problem is changed into a group of codes according to whether the line is built or not, the genes corresponding to the built optical cable lines are set to be 1 in the codes, the genes corresponding to the optical cable lines representing the predicted building are also set to be 1, the genes corresponding to the optical cable lines representing the non-building are set to be 0, the initialization is firstly carried out, a population is built, then, a process of elimination is carried out by imitating the phenomena of heredity, intersection, variation and natural selection in the biological world, and the solution which enables the objective function to be minimum is found by optimizing the solution generation by generation under the condition that the constraint condition is met, namely, the optical cable line planning combination with the best economical efficiency is found.
Further, the chromosome is selected in the step D5), and the method comprises the following steps:
D51) determining the number H of chromosomes selected each time, wherein H is less than H;
D52) randomly selecting H chromosomes from the H chromosomes, sequencing the objective function values of all the selected chromosomes, and selecting the individual with the minimum objective function value to enter a filial generation population;
D53) and D52) repeating the step until the new population size reaches the original population size N to obtain a new generation of population.
By randomly selecting H chromosomes from H chromosomes, the probability of each chromosome being selected is the same, and then finding the best chromosome to be put into a new generation of population, the problem of local convergence of the algorithm can be avoided. By means of preferential selection, the problem that the population stops evolving due to small difference between individuals in the population at the early stage and the population at the later stage due to rapid occupation of better individuals is solved.
Further, in the step D6), the chromosome is crossed in a mode that every two chromosome are not repeated, so as to obtain chromosome combination
(X1,X2),(X3,X4),...,(XN-1,XN) For each chromosome combination, one [0,1 ] is generated]And judging whether the random number is smaller than the crossover probability, if so, performing crossover operation on the chromosome combination, and if not, not performing crossover operation on the chromosome combination.
Further, in step D4), an average objective function value of the g-th generation population is calculated
Figure BDA0002239350810000051
Setting a stop threshold β, the stop condition being
Figure BDA0002239350810000052
Or the number of iterations is reached,
Figure BDA0002239350810000053
is the average objective function value of the g-1 generation population,
Figure BDA0002239350810000054
is the average objective function value of the g-2 generation population.
Whether the objective function value of the current population is not changed much and tends to be stable or not is judged by calculating the average objective function value of the 3 generations of populations, and on the other hand, the algorithm is stopped when the iteration times are reached in consideration of the iteration times.
The invention has the following beneficial effects: the optical cable line is optimized by establishing a topological structure diagram, an objective function integrating the economy and the influence degree of the topological structure is constructed under the constraint condition that the reliability of the power communication network and the failure rate of a communication station are met, the objective function is solved by utilizing a genetic algorithm, and the power communication network topological structure with good economy and small fault occurrence influence degree is obtained.
Drawings
FIG. 1 is a block flow diagram of the present invention.
Fig. 2 is a network connectivity undirected graph in an embodiment of the invention.
Detailed Description
The invention is further described with reference to the following detailed description and accompanying drawings.
As shown in fig. 1 and fig. 2, a method for optimizing a power communication topology includes the steps of:
A) optical cable line data in the power communication network are counted, wherein the optical cable line data comprise: 13 communication sites, 14 existing optical cable lines, 7 optical cable lines to be constructed, the cost of the optical cable lines and the reliability of the optical cable lines.
B) And establishing a topological structure diagram by using the optical cable line data, wherein the communication station is represented by nodes, the existing optical cable line is represented by a solid line, and the optical cable line to be established is represented by a dotted line.
C) Establishing a constraint condition, comprising the following steps:
C1) obtaining the network looping rate a of the ith optical cable line without fault by using a network connection undirected graphiAnd network connectivity rate biWherein, in the step (A),
Figure BDA0002239350810000061
qirepresenting the number of communication stations that make up the ring structure of the communication network,
Figure BDA0002239350810000062
rithe number of communication stations capable of normally communicating is shown, and the reliability c of the ith optical cable line is calculatedi＝ai+bi；
C2) To obtainNetwork ring forming rate when ith optical cable fails
Figure BDA0002239350810000063
And rate of network connectivity
Figure BDA0002239350810000064
Calculating a reliability difference
Figure BDA0002239350810000065
Setting a reliability threshold
Figure BDA0002239350810000066
Establishing optical cable reliability constraint conditions
Figure BDA0002239350810000067
Wherein the content of the first and second substances,
Figure BDA0002239350810000068
C3) counting the number of optical cables connected with each communication station to obtain a station optical cable number set { A }1,A2,...,A13Get the failure rate of a single communication station when it fails
Figure BDA0002239350810000069
C4) The failure rate of any two communication stations which fail is obtained as
Figure BDA00022393508100000610
i ≠ j, wherein AijThe number of optical cables connected between the ith communication station and the jth communication station is represented;
C5) the failure rate of any three communication stations is obtained as
Figure BDA00022393508100000611
C6) D is obtained as failure rates of any 4, 5, … and 13 communication stations which fail respectively4、d5、...、d13Setting a failure rate threshold value mu, and establishing a constraint condition d < mu of the failure rate of the communication station, wherein
Figure BDA00022393508100000612
Constructing the objective function includes the steps of:
C11) calculating power communication network efficiency
Figure BDA0002239350810000071
dkjThe length of the shortest optical cable between the kth communication station and the jth communication station is n, and the number of the communication stations of the power communication network is n;
C12) calculating the influence degree of the ith optical cable when the ith optical cable fails
Figure BDA0002239350810000072
Indicating the efficiency of the power communication network in case of failure of the ith optical cable, ηiIndicating the efficiency of the power communication network before the i-th cable fails, c* iIndicating reliability of the ith cable in the event of failure, ciRepresenting the reliability of the ith optical cable before the fault occurs;
C13) obtaining an influence weight rho when the ith optical cable failsi；
C14) Calculating the final influence degree of the ith optical cable when the ith optical cable fails
Figure BDA0002239350810000073
C16) Obtaining a cost set of fiber optic lines { e }1,e2,...,ei,...,ek+mIn which eiRepresenting the construction cost of the ith optical cable line;
C17) when the constraint condition is satisfied
Figure BDA0002239350810000074
Next, an objective function is constructed
Figure BDA0002239350810000075
Wherein z isiThe value is 1 or 0, and the value is,when x isiA value of 1 indicates that the ith optical cable has been constructed or is predicted to be constructed, when xiA value of 0 indicates that the ith cable is not considered for construction.
D) Solving the objective function by using a genetic algorithm;
the method comprises the following steps:
D1) initializing a population, setting cross probability and variation probability, setting the gene factor on each chromosome as 17, the population scale as N, and recording the population of the 1 st generation as
Figure BDA0002239350810000076
Wherein the content of the first and second substances,
Figure BDA0002239350810000077
represents the nth chromosome of the population of the t generation,
Figure BDA0002239350810000078
a first gene representing an Nth chromosome of the population of the t generation, the position of the gene representing the number of the optical cable line;
D2) coding is carried out, each chromosome represents a group of solutions of the objective function, the genes represent optical cable lines, the genes are 1 and 0 respectively represent that the optical cable lines are built or predicted to be built, and the genes represent that the optical cable lines are not built;
D3) evaluating each group of solutions in the population, calculating the reliability and failure rate of each group of solutions, obtaining H chromosomes meeting constraint conditions, wherein H is less than or equal to N, calculating objective function values, and sequencing the chromosomes from small to large according to the objective function values;
D4) calculating the average objective function value of the g generation population
Figure BDA0002239350810000079
Setting a stop threshold β, the stop condition being
Figure BDA0002239350810000081
Or the number of iterations is reached,
Figure BDA0002239350810000082
is the average of the g-1 generation populationThe values of the objective functions are averaged,
Figure BDA0002239350810000083
if the average objective function value of the g-2 th generation population meets the stop condition, ending the algorithm to obtain the optimal solution, and if the average objective function value of the g-2 nd generation population cannot meet the stop condition, entering the step D5);
D5) selecting a chromosome comprising the steps of:
D51) determining the number H of chromosomes selected each time, wherein H is less than H;
D52) randomly selecting H chromosomes from the H chromosomes, sequencing the objective function values of all the selected chromosomes, and selecting the individual with the minimum objective function value to enter a filial generation population;
D53) and D52) repeating the step until the new population size reaches the original population size N to obtain the selected population.
D6) Performing cross operation on chromosome by using pairwise non-repetitive mode to obtain chromosome combination (X)1,X2),(X3,X4),...,(XN-1,XN) For each chromosome combination, one [0,1 ] is generated]Judging whether the random number is less than the crossing probability, if so, performing crossing operation on the chromosome combination, and if not, not performing crossing operation on the chromosome combination, taking two chromosomes in the population as parents, and performing crossing recombination on partial genes of the chromosomes to form new chromosomes and obtain a crossed population;
D7) setting a variation position in the crossed population, changing the gene value at the variation position, carrying out individual gene variation according to variation probability, mutating the gene with the value of 0 to 1, and mutating the gene with the value of 1 to 0;
D8) generating a new generation of population, repeating step D3).
E) And obtaining the network line after the topological structure of the power communication network is optimized.
The invention utilizes the optical cable line data to establish a topological structure chart, optimizes the optical cable line, constructs an objective function integrating the economy and the influence degree of the topological structure under the constraint condition of meeting the reliability of the electric power communication network and the failure rate of the communication station, and utilizes a genetic algorithm to solve the objective function to obtain the electric power communication network topological structure with good economy and small influence degree of faults.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (9)

1. A power communication topological structure optimization method is characterized by comprising the following steps:
A) the method comprises the steps of counting optical cable line data in the power communication network;
B) establishing a topological structure chart by using optical cable line data;
C) establishing a reliability and failure rate constraint condition, and constructing a target function which integrates the economy and the influence degree of the topological structure;
D) solving the objective function by using a genetic algorithm;
E) and obtaining the network line after the topological structure of the power communication network is optimized.
2. The method for optimizing the power communication topology according to claim 1, wherein in the step a), the optical cable line data comprises: n communication sites, k existing optical cable lines, m optical cable lines to be established, cost of the optical cable lines and reliability of the optical cable lines.
3. The method for optimizing the power communication topology according to claim 1, wherein the step B) further comprises: the communication sites are represented by nodes, the existing cable lines by solid lines and the cable lines to be constructed by broken lines.
4. The method for optimizing the power communication topological structure according to claim 1, wherein the step C) of establishing the constraint condition comprises the steps of:
C1) obtaining the network looping rate a of the ith optical cable line without fault by using a network connection undirected graphiAnd network connectivity rate biCalculating the reliability c of the ith optical cable linei＝ai+bi；
C2) Obtaining the network ring forming rate when the ith optical cable fails
Figure FDA0002239350800000011
And rate of network connectivity
Figure FDA0002239350800000012
Calculating a reliability difference
Figure FDA0002239350800000013
Setting a reliability threshold
Figure FDA0002239350800000014
Establishing optical cable reliability constraint conditions
Figure FDA0002239350800000015
Wherein the content of the first and second substances,
Figure FDA0002239350800000016
C3) counting the number of optical cables connected with each communication station to obtain a station optical cable number set { A }1,A2,...,AnGet the failure rate of a single communication station when it fails
Figure FDA0002239350800000017
C4) The failure rate of any two communication stations which fail is obtained as
Figure FDA0002239350800000018
Wherein A isijLight representing a connection between an ith communication station and a jth communication stationThe number of cables;
C5) the failure rate of any three communication stations is obtained as
Figure FDA0002239350800000021
C6) D is obtained as failure rates of any 4, 5, … and n communication stations which fail respectively4、d5、...、dnSetting a failure rate threshold value mu, and establishing a constraint condition d < mu of the failure rate of the communication station, wherein
Figure FDA0002239350800000022
5. The method as claimed in claim 4, wherein the network looping rate of the ith optical cable line is higher than that of the ith optical cable line
Figure FDA0002239350800000023
qiIndicating the number of communication stations constituting the ring structure of a communication network, and the network connection rate
Figure FDA0002239350800000024
riIndicating the number of communication stations capable of communicating normally.
6. The method for optimizing the power communication topology according to claim 1 or 4, wherein in the step C), constructing the objective function comprises the steps of:
C11) calculating power communication network efficiency
Figure FDA0002239350800000025
dkjThe shortest optical cable length between the kth communication station and the jth communication station;
C12) calculating the influence degree of the ith optical cable when the ith optical cable fails
Figure FDA0002239350800000026
Figure FDA0002239350800000027
Indicating the efficiency of the power communication network in case of failure of the ith optical cable, ηiIndicating the efficiency of the power communication network before the i-th cable fails, c* iIndicating reliability of the ith cable in the event of failure, ciRepresenting the reliability of the ith optical cable before the fault occurs;
C13) obtaining an influence weight rho when the ith optical cable failsi；
C14) Calculating the final influence degree of the ith optical cable when the ith optical cable fails
Figure FDA0002239350800000028
C16) Obtaining a cost set of fiber optic lines { e }1,e2,...,ei,...,ek+mIn which eiRepresenting the construction cost of the ith optical cable line;
C17) when the constraint condition is satisfied
Figure FDA0002239350800000029
Next, an objective function is constructed
Figure FDA00022393508000000210
Wherein z isiTaking a value of 1 or 0 when xiA value of 1 indicates that the ith optical cable has been constructed or is predicted to be constructed, when xiA value of 0 indicates that the ith cable is not considered for construction.
7. The method for optimizing the power communication topological structure according to claim 1, wherein the step D) of solving the objective function by using a genetic algorithm comprises the steps of:
D1) initializing a population, setting cross probability and variation probability, setting a base factor on each chromosome as (k + m), setting the population scale as N, and recording the population of the 1 st generation as
Figure FDA0002239350800000031
Wherein the content of the first and second substances,
Figure FDA0002239350800000032
represents the nth chromosome of the population of the t generation,
Figure FDA0002239350800000033
Figure FDA0002239350800000034
a first gene representing an Nth chromosome of the population of the t generation, the position of the gene representing the number of the optical cable line;
D2) coding is carried out, each chromosome represents a group of solutions of the objective function, the genes represent optical cable lines, the genes are 1 and 0 respectively represent that the optical cable lines are built or predicted to be built, and the genes represent that the optical cable lines are not built;
D3) evaluating each group of solutions in the population, calculating the reliability and failure rate of each group of solutions, obtaining H chromosomes meeting constraint conditions, wherein H is less than or equal to N, calculating objective function values, and sequencing the chromosomes from small to large according to the objective function values;
D4) setting a stopping condition, if the stopping condition is met, ending the algorithm to obtain an optimal solution, and if the stopping condition is not met, entering the step D5);
D5) selecting a chromosome to obtain a selected population;
D6) carrying out cross operation on the selected chromosomes according to the cross probability, taking two chromosomes in the population as parents, carrying out cross recombination on partial genes of the chromosomes to form new chromosomes, and obtaining a crossed population;
D7) setting a variation position in the crossed population, changing the gene value at the variation position, carrying out individual gene variation according to variation probability, mutating the gene with the value of 0 to 1, and mutating the gene with the value of 1 to 0;
D8) generating a new generation of population, repeating step D3).
8. The method for optimizing the power communication topology according to claim 7, wherein the step of selecting the chromosome in the step D5) comprises the steps of:
D51) determining the number H of chromosomes selected each time, wherein H is less than H;
D52) randomly selecting H chromosomes from the H chromosomes, sequencing the objective function values of all the selected chromosomes, and selecting the individual with the minimum objective function value to enter a filial generation population;
D53) and D52) repeating the step until the new population size reaches the original population size N to obtain a new generation of population.
9. The method as claimed in claim 8, wherein the chromosome is crossed in step D6 in a non-repetitive manner to obtain chromosome combination (X)1,X2),(X3,X4),...,(XN-1,XN) For each chromosome combination, one [0,1 ] is generated]And judging whether the random number is smaller than the crossover probability, if so, performing crossover operation on the chromosome combination, and if not, not performing crossover operation on the chromosome combination.
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