非支配性排序遗传算法 III---NSGA-III matlab代码.zip

% K. Deb and H. Jain, "An Evolutionary Many-Objective Optimization Algorithm % Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving % Problems With Box Constraints," % IEEE Transactions on Evolutionary Computation, % vol. 18, no. 4, pp. 577-601, Aug. 2014. clc; clear; close all; %% Problem Definition CostFunction = @(x) MOP2(x); % Cost Function nVar = 5; % Number of Decision Variables VarSize = [1 nVar]; % Size of Decision Variables Matrix VarMin = -1; % Lower Bound of Variables VarMax = 1; % Upper Bound of Variables % Number of Objective Functions nObj = numel(CostFunction(unifrnd(VarMin, VarMax, VarSize))); %% NSGA-II Parameters % Generating Reference Points nDivision = 10; Zr = GenerateReferencePoints(nObj, nDivision); MaxIt = 50; % Maximum Number of Iterations nPop = 80; % Population Size pCrossover = 0.5; % Crossover Percentage nCrossover = 2*round(pCrossover*nPop/2); % Number of Parnets (Offsprings) pMutation = 0.5; % Mutation Percentage nMutation = round(pMutation*nPop); % Number of Mutants mu = 0.02; % Mutation Rate sigma = 0.1*(VarMax-VarMin); % Mutation Step Size %% Colect Parameters params.nPop = nPop; params.Zr = Zr; params.nZr = size(Zr,2); params.zmin = []; params.zmax = []; params.smin = []; %% Initialization disp('Staring NSGA-III ...'); empty_individual.Position = []; empty_individual.Cost = []; empty_individual.Rank = []; empty_individual.DominationSet = []; empty_individual.DominatedCount = []; empty_individual.NormalizedCost = []; empty_individual.AssociatedRef = []; empty_individual.DistanceToAssociatedRef = []; pop = repmat(empty_individual, nPop, 1); for i = 1:nPop pop(i).Position = unifrnd(VarMin, VarMax, VarSize); pop(i).Cost = CostFunction(pop(i).Position); end % Sort Population and Perform Selection [pop, F, params] = SortAndSelectPopulation(pop, params); %% NSGA-II Main Loop for it = 1:MaxIt % Crossover popc = repmat(empty_individual, nCrossover/2, 2); for k = 1:nCrossover/2 i1 = randi([1 nPop]); p1 = pop(i1); i2 = randi([1 nPop]); p2 = pop(i2); [popc(k, 1).Position, popc(k, 2).Position] = Crossover(p1.Position, p2.Position); popc(k, 1).Cost = CostFunction(popc(k, 1).Position); popc(k, 2).Cost = CostFunction(popc(k, 2).Position); end popc = popc(:); % Mutation popm = repmat(empty_individual, nMutation, 1); for k = 1:nMutation i = randi([1 nPop]); p = pop(i); popm(k).Position = Mutate(p.Position, mu, sigma); popm(k).Cost = CostFunction(popm(k).Position); end % Merge pop = [pop popc popm]; %#ok % Sort Population and Perform Selection [pop, F, params] = SortAndSelectPopulation(pop, params); % Store F1 F1 = pop(F{1}); % Show Iteration Information disp(['Iteration ' num2str(it) ': Number of F1 Members = ' num2str(numel(F1))]); % Plot F1 Costs figure(1); PlotCosts(F1); pause(0.01); end %% Results disp(['Final Iteration: Number of F1 Members = ' num2str(numel(F1))]); disp('Optimization Terminated.');