智能优化算法-生物地理学优化算法（BBO）（附源码）

%% 生物地理学优化算法 clc; clear; close all; %% 问题定义 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 %% BBO参数设置 MaxIt=1000; % Maximum Number of Iterations nPop=50; % Number of Habitats (Population Size) KeepRate=0.2; % Keep Rate nKeep=round(KeepRate*nPop); % Number of Kept Habitats nNew=nPop-nKeep; % Number of New Habitats % Migration Rates mu=linspace(1,0,nPop); % Emmigration Rates lambda=1-mu; % Immigration Rates alpha=0.9; pMutation=0.1; sigma=0.02*(VarMax-VarMin); %% 初始化 % Empty Habitat habitat.Position=[]; habitat.Cost=[]; % Create Habitats Array pop=repmat(habitat,nPop,1); % Initialize Habitats for i=1:nPop pop(i).Position=unifrnd(VarMin,VarMax,VarSize); pop(i).Cost=CostFunction(pop(i).Position); end % Sort Population [~, SortOrder]=sort([pop.Cost]); pop=pop(SortOrder); % Best Solution Ever Found BestSol=pop(1); % Array to Hold Best Costs BestCost=zeros(MaxIt,1); %% BBO主循环 for it=1:MaxIt newpop=pop; for i=1:nPop for k=1:nVar % Migration if rand<=lambda(i) % Emmigration Probabilities EP=mu; EP(i)=0; EP=EP/sum(EP); % Select Source Habitat j=RouletteWheelSelection(EP); % Migration newpop(i).Position(k)=pop(i).Position(k) ... +alpha*(pop(j).Position(k)-pop(i).Position(k)); end % Mutation if rand<=pMutation newpop(i).Position(k)=newpop(i).Position(k)+sigma*randn; end end % Apply Lower and Upper Bound Limits newpop(i).Position = max(newpop(i).Position, VarMin); newpop(i).Position = min(newpop(i).Position, VarMax); % Evaluation newpop(i).Cost=CostFunction(newpop(i).Position); end % Sort New Population [~, SortOrder]=sort([newpop.Cost]); newpop=newpop(SortOrder); % Select Next Iteration Population pop=[pop(1:nKeep) newpop(1:nNew)]; % Sort Population [~, SortOrder]=sort([pop.Cost]); pop=pop(SortOrder); % Update Best Solution Ever Found BestSol=pop(1); % Store Best Cost Ever Found BestCost(it)=BestSol.Cost; % Show Iteration Information disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); end %% 结果输出 figure; semilogy(BestCost,'LineWidth',2); xlabel('Iteration'); ylabel('Best Cost'); grid on; legend('BBO')