萤火虫算法(FA)优化BP神经网络分类预测，FA-BP分类预测，多特征输入模型 多特征输入单输出的二分类及多分类模型 程序内

% % Copyright (c) 2015, Yarpiz (www.yarpiz.com) % All rights reserved. Please read the "license.txt" for license terms. % % Project Code: YOEA112 % Project Title: Implementation of Firefly Algorithm (FA) in MATLAB % Publisher: Yarpiz (www.yarpiz.com) % % Developer: S. Mostapha Kalami Heris (Member of Yarpiz Team) % % Contact Info: sm.kalami@gmail.com, info@yarpiz.com % function [bestFitValue,bestSolution,cg_curve] = FA(nPop,MaxIteration,low,up,dims,fobj) %% Problem Definition gamma=1; % Light Absorption Coefficient beta0=2; % Attraction Coefficient Base Value alpha=0.2; % Mutation Coefficient alpha_damp=0.98; % Mutation Coefficient Damping Ratio CostFunction=fobj; % Cost Function nVar=dims; % Number of Decision Variables VarSize=[1 nVar]; % Decision Variables Matrix Size VarMin=low; % Decision Variables Lower Bound VarMax= up; % Decision Variables Upper Bound %% Firefly Algorithm Parameters MaxIt=MaxIteration; % Maximum Number of Iterations % nPop=25; % Number of Fireflies (Swarm Size) % gamma=1; % Light Absorption Coefficient % % beta0=2; % Attraction Coefficient Base Value % % alpha=0.2; % Mutation Coefficient % % alpha_damp=0.98; % Mutation Coefficient Damping Ratio delta=0.05*(VarMax-VarMin); % Uniform Mutation Range m=2; if isscalar(VarMin) && isscalar(VarMax) dmax = (VarMax-VarMin)*sqrt(nVar); else dmax = norm(VarMax-VarMin); end %% Initialization % Empty Firefly Structure firefly.Position=[]; firefly.Cost=[]; % Initialize Population Array pop=repmat(firefly,nPop,1); % Initialize Best Solution Ever Found BestSol.Cost=inf; % Create Initial Fireflies 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 % Array to Hold Best Cost Values BestCost=zeros(MaxIt,1); %% Firefly Algorithm Main Loop for it=1:MaxIt disp(['第',num2str(it),'次迭代']) newpop=repmat(firefly,nPop,1); for i=1:nPop newpop(i).Cost = inf; for j=1:nPop if pop(j).Cost < pop(i).Cost rij=norm(pop(i).Position-pop(j).Position)/dmax; beta=beta0*exp(-gamma*rij^m); % e=delta*unifrnd(-1,+1,VarSize); e=delta.*randn(VarSize); newsol.Position = pop(i).Position ... + beta*rand(VarSize).*(pop(j).Position-pop(i).Position) ... + alpha*e; newsol.Position=max(newsol.Position,VarMin); newsol.Position=min(newsol.Position,VarMax); newsol.Cost=CostFunction(newsol.Position); if newsol.Cost <= newpop(i).Cost newpop(i) = newsol; if newpop(i).Cost<=BestSol.Cost BestSol=newpop(i); end end end end end % Merge pop=[pop newpop]; %#ok % Sort [~, SortOrder]=sort([pop.Cost]); pop=pop(SortOrder); % Truncate pop=pop(1:nPop); % Store Best Cost Ever Found BestCost(it)=BestSol.Cost; % Show Iteration Information disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); % Damp Mutation Coefficient alpha = alpha*alpha_damp; end %% Results bestFitValue = BestSol.Cost; bestSolution = BestSol.Position; cg_curve = BestCost; end