多目標(biāo)遺傳算法代碼

1、% function nsga_2(pro)%% Main Function% Main program to run the NSGA-II MOEA.% Read the corresponding documentation to learn more about multiobjective% optimization using evolutionary algorithms.% initialize_variables ha

2、s two arguments; First being the population size% and the second the problem number. '1' corresponds to MOP1 and '2'% corresponds to MOP2.%inp_para_definition=input_parameters_definition;%% Initialize the

3、 variables% Declare the variables and initialize their values% pop - population% gen - generations% pro - problem number%clear;clc;tic;pop = 100; % 每一代的種群數(shù)gen = 100; % 總共的代數(shù)pro = 2; % 問題選擇 1 或者 2，見 switchswitch procase 1

4、% M is the number of objectives.M = 2;% V is the number of decision variables. In this case it is% difficult to visualize the decision variables space while the% objective space is just two dimensional.V = 6;case 2M = 3;

5、V = 12;case 3 % case 1 和 case 2 用來對整個(gè)算法進(jìn)行常規(guī)驗(yàn)證， 作為調(diào)試之用； case 3 為本工程所需；M = 2; %(output parameters 個(gè)數(shù))V = 8; %(input parameters 個(gè)數(shù))K = 10;end% Initialize the populationchromosome = initialize_variables(pop,pro);%% Sort the

6、initialized population% Sort the population using non-domination-sort. This returns two columns% for each individual which are the rank and the crowding distance% corresponding to their position in the front they belong.

7、 真是牛 X 了。chromosome = non_domination_sort_mod(chromosome,pro);%% Start the evolution processintermediate_chromosome(main_pop+1:main_pop+offspring_pop,1:M+V)=offspring_chromosome;%intermediate_chromosome=inter_chromo(chro

8、mosome,offspring_chromosome,pro);% Non-domination-sort of intermediate population% The intermediate population is sorted again based on non-domination sort% before the replacement operator is performed on the intermediat

9、e% population.intermediate_chromosome = ...non_domination_sort_mod(intermediate_chromosome,pro);% Perform Selection% Once the intermediate population is sorted only the best solution is% selected based on it rank and cro

10、wding distance. Each front is filled in% ascending order until the addition of population size is reached. The% last front is included in the population based on the individuals with% least crowding distancechromosome =

11、replace_chromosome(intermediate_chromosome,pro,pop);if ~mod(i,10)fprintf('%d\n',i);endend%% Result% Save the result in ASCII text format.save solution.txt chromosome -ASCII%% Visualize% The following is used to v

12、isualize the result for the given problem.switch procase 1plot(chromosome(:,V + 1),chromosome(:,V + 2),'y+');title('MOP1 using NSGA-II');xlabel('f(x_1)');ylabel('f(x_2)');case 2plot3(chrom

13、osome(:,V + 1),chromosome(:,V + 2),chromosome(:,V + 3),'*');title('MOP2 using NSGA-II');xlabel('f(x_1)');ylabel('f(x_2)');zlabel('f(x_3)');end%disp('run time is:')%toc;%%%%