ABC，人工蜂群优化算法，MATLAB代码

% % Copyright (c) 2015, Mostapha Kalami Heris & Yarpiz (www.yarpiz.com) % All rights reserved. Please read the "LICENSE" file for license terms. % % Project Code: YPEA114 % Project Title: Implementation of Artificial Bee Colony in MATLAB % Publisher: Yarpiz (www.yarpiz.com) % % Developer: Mostapha Kalami Heris (Member of Yarpiz Team) % % Cite as: % Mostapha Kalami Heris, Artificial Bee Colony in MATLAB (URL: https://yarpiz.com/297/ypea114-artificial-bee-colony), Yarpiz, 2015. % % Contact Info: sm.kalami@gmail.com, info@yarpiz.com % clc; clear; close all; %% Problem Definition CostFunction = @(x) Sphere(x); % Cost Function nVar = 5; % Number of Decision Variables VarSize = [1 nVar]; % Decision Variables Matrix Size VarMin = -10; % Decision Variables Lower Bound VarMax = 10; % Decision Variables Upper Bound %% ABC Settings MaxIt = 1000; % Maximum Number of Iterations nPop = 100; % Population Size (Colony Size) nOnlooker = nPop; % Number of Onlooker Bees L = round(0.6*nVar*nPop); % Abandonment Limit Parameter (Trial Limit) a = 1; % Acceleration Coefficient Upper Bound %% Initialization % Empty Bee Structure empty_bee.Position = []; empty_bee.Cost = []; % Initialize Population Array pop = repmat(empty_bee, nPop, 1); % Initialize Best Solution Ever Found BestSol.Cost = inf; % Create Initial Population for i = 1:nPop pop(i).Position = unifrnd(VarMin, VarMax, VarSize); pop(i).Cost = CostFunction(pop(i).Position); if pop(i).Cost <= BestSol.Cost BestSol = pop(i); end end % Abandonment Counter C = zeros(nPop, 1); % Array to Hold Best Cost Values BestCost = zeros(MaxIt, 1); %% ABC Main Loop for it = 1:MaxIt % Recruited Bees for i = 1:nPop % Choose k randomly, not equal to i K = [1:i-1 i+1:nPop]; k = K(randi([1 numel(K)])); % Define Acceleration Coeff. phi = a*unifrnd(-1, +1, VarSize); % New Bee Position newbee.Position = pop(i).Position+phi.*(pop(i).Position-pop(k).Position); % Apply Bounds newbee.Position = max(newbee.Position, VarMin); newbee.Position = min(newbee.Position, VarMax); % Evaluation newbee.Cost = CostFunction(newbee.Position); % Comparision if newbee.Cost <= pop(i).Cost pop(i) = newbee; else C(i) = C(i)+1; end end % Calculate Fitness Values and Selection Probabilities F = zeros(nPop, 1); MeanCost = mean([pop.Cost]); for i = 1:nPop F(i) = exp(-pop(i).Cost/MeanCost); % Convert Cost to Fitness end P = F/sum(F); % Onlooker Bees for m = 1:nOnlooker % Select Source Site i = RouletteWheelSelection(P); % Choose k randomly, not equal to i K = [1:i-1 i+1:nPop]; k = K(randi([1 numel(K)])); % Define Acceleration Coeff. phi = a*unifrnd(-1, +1, VarSize); % New Bee Position newbee.Position = pop(i).Position+phi.*(pop(i).Position-pop(k).Position); % Apply Bounds newbee.Position = max(newbee.Position, VarMin); newbee.Position = min(newbee.Position, VarMax); % Evaluation newbee.Cost = CostFunction(newbee.Position); % Comparision if newbee.Cost <= pop(i).Cost pop(i) = newbee; else C(i) = C(i) + 1; end end % Scout Bees for i = 1:nPop if C(i) >= L pop(i).Position = unifrnd(VarMin, VarMax, VarSize); pop(i).Cost = CostFunction(pop(i).Position); C(i) = 0; end end % Update Best Solution Ever Found for i = 1:nPop if pop(i).Cost <= BestSol.Cost BestSol = pop(i); end end % Store Best Cost Ever Found BestCost(it) = BestSol.Cost; % Display Iteration Information disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); end %% Results figure; % plot(BestCost, 'LineWidth', 2); semilogy(BestCost, 'LineWidth', 1); xlabel('Iteration'); ylabel('Best Cost'); grid on;