matlab-基于matlab的杂草优化算法IWO仿真-源码

clc; clear; close all; warning off; addpath(genpath(pwd)); CostFunction = @(x) Sphere(x); % Objective Function nVar = 5; % Number of Decision Variables VarSize = [1 nVar]; % Decision Variables Matrix Size VarMin = -10; % Lower Bound of Decision Variables VarMax = 10; % Upper Bound of Decision Variables MaxIt = 200; % Maximum Number of Iterations nPop0 = 10; % Initial Population Size nPop = 25; % Maximum Population Size Smin = 0; % Minimum Number of Seeds Smax = 5; % Maximum Number of Seeds Exponent = 2; % Variance Reduction Exponent sigma_initial = 0.5; % Initial Value of Standard Deviation sigma_final = 0.001; % Final Value of Standard Deviation empty_plant.Position = []; empty_plant.Cost = []; pop = repmat(empty_plant, nPop0, 1); % Initial Population Array for i = 1:numel(pop) % Initialize Position pop(i).Position = unifrnd(VarMin, VarMax, VarSize); % Evaluation pop(i).Cost = CostFunction(pop(i).Position); end % Initialize Best Cost History BestCosts = zeros(MaxIt, 1); for it = 1:MaxIt % Update Standard Deviation sigma = ((MaxIt - it)/(MaxIt - 1))^Exponent * (sigma_initial - sigma_final) + sigma_final; % Get Best and Worst Cost Values Costs = [pop.Cost]; BestCost = min(Costs); WorstCost = max(Costs); % Initialize Offsprings Population newpop = []; % Reproduction for i = 1:numel(pop) ratio = (pop(i).Cost - WorstCost)/(BestCost - WorstCost); S = floor(Smin + (Smax - Smin)*ratio); for j = 1:S % Initialize Offspring newsol = empty_plant; % Generate Random Location newsol.Position = pop(i).Position + sigma * randn(VarSize); % Apply Lower/Upper Bounds newsol.Position = max(newsol.Position, VarMin); newsol.Position = min(newsol.Position, VarMax); % Evaluate Offsring newsol.Cost = CostFunction(newsol.Position); % Add Offpsring to the Population newpop = [newpop newsol]; %#ok end end % Merge Populations pop = [pop newpop]; % Sort Population [~, SortOrder] = sort([pop.Cost]); pop = pop(SortOrder); % Competitive Exclusion (Delete Extra Members) if numel(pop)>nPop pop = pop(1:nPop); end % Store Best Solution Ever Found BestSol = pop(1); % Store Best Cost History BestCosts(it) = BestSol.Cost; % Display Iteration Information disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCosts(it))]); end figure; plot(BestCosts, 'LineWidth', 2); xlabel('Iteration'); ylabel('Best Cost'); grid on;