基于蜉蝣优化算法实现聚类附matlab代码.zip

% Project Title: A mayfly optimization algorithm (MA) for clustering in MATLAB % % https://www.sciencedirect.com/science/article/abs/pii/S036083522030293X?via%3Dihub % https://link.springer.com/article/10.1007/s10639-023-11645-4 % % Developers: K. Zervoudakis & S. Tsafarakis % % Contact Info: [email protected] % School of Production Engineering and Management, % Technical University of Crete, Chania, Greece % https://scholar.google.com/citations?user=QZ6WGDoAAAAJ&hl=en % https://www.researchgate.net/profile/Konstantinos-Zervoudakis % % Researchers are allowed to use this code in their research projects, as % long as they cite as: % % Zervoudakis, K., & Tsafarakis, S. (2020). A mayfly optimization algorithm. % Computers & Industrial Engineering, 145, 106559. % https://doi.org/10.1016/j.cie.2020.106559 % % and % % Mastrothanasis, K., Zervoudakis, K., Kladaki, M. & Tsafarakis, S. (2023). % A bio-inspired computational classifier system for the evaluation of children痴 % theatrical anxiety at school. Education and Information Technologies. % https://doi.org/10.1007/s10639-023-11645-4 %% clc; clear; close all; %% Problem Definition % Objective Function funcname='Clustering'; Index = 'DB'; % DB or CS X = xlsread('mydata.xlsx'); % Replace the data in this files with your data % Remove outliers %X = rmoutliers(X,"mean"); % Normilize Data %X=normalize(X,"range"); k = 10; ObjectiveFunction=@(s) ClusteringCost(s, X, Index); % Cost Function ProblemSize=[k size(X,2)+1]; % Decision Variables Matrix Size LowerBound= repmat([min(X) 0],k,1); % Lower Bound of Variables UpperBound= repmat([max(X) 1],k,1); % Upper Bound of Variables %% Mayfly Parameters methname='Mayfly Algorithm'; MaxIt=500; % Maximum Number of Iterations nPop=20; nPopf=20; % Population Size (males and females) g=0.8; % Inertia Weight gdamp=1; % Inertia Weight Damping Ratio a1=1.0; % Personal Learning Coefficient a2=1.5; a3=1.5; % Global Learning Coefficient beta=2; % Distance sight Coefficient dance=5; % Nuptial Dance fl=1; % Random flight dance_damp=0.8; % Damping Ratio fl_damp=0.99; % Mating Parameters nc=20; % Number of Offsprings (also Parnets) nm=round(0.05*nPop); % Number of Mutants mu=0.01; % Mutation Rate % Velocity Limits VelMax=0.1*(UpperBound-LowerBound); VelMin=-VelMax; %% Initialization empty_mayfly.Position=[]; empty_mayfly.Cost=[]; empty_mayfly.Velocity=[]; empty_mayfly.Out=[]; empty_mayfly.Best.Position=[]; empty_mayfly.Best.Cost=[]; empty_mayfly.Best.Out=[]; Mayfly=repmat(empty_mayfly,nPop,1); % Males Mayflyf=repmat(empty_mayfly,nPopf,1); % Females GlobalBest.Cost=inf; funccount=0; for i=1:nPop % Initialize Position of Males Mayfly(i).Position=unifrnd(LowerBound,UpperBound,ProblemSize); % Initialize Velocity Mayfly(i).Velocity=zeros(ProblemSize); % Evaluation [Mayfly(i).Cost, Mayfly(i).Out]=ObjectiveFunction(Mayfly(i).Position); % Update Personal Best Mayfly(i).Best.Position=Mayfly(i).Position; Mayfly(i).Best.Cost=Mayfly(i).Cost; Mayfly(i).Best.Out=Mayfly(i).Out; funccount=funccount+1; % Update Global Best if Mayfly(i).Best.Cost<GlobalBest.Cost GlobalBest=Mayfly(i).Best; end end for i=1:nPopf % Initialize Position of Females Mayflyf(i).Position=unifrnd(LowerBound,UpperBound,ProblemSize); Mayflyf(i).Velocity=zeros(ProblemSize); [Mayflyf(i).Cost, Mayflyf(i).Out]=ObjectiveFunction(Mayflyf(i).Position); funccount=funccount+1; % Update Global Best (Uncomment if you use the PGB-IMA version) if Mayflyf(i).Best.Cost<GlobalBest.Cost GlobalBest=Mayflyf(i).Best; end end BestSolution=zeros(MaxIt,1); %% Mayfly Main Loop for it=1:MaxIt for i=1:nPopf % Update Females e=unifrnd(-1,+1,ProblemSize); rmf=(Mayfly(i).Position-Mayflyf(i).Position); if Mayflyf(i).Cost>Mayfly(i).Cost Mayflyf(i).Velocity = g*Mayflyf(i).Velocity ... +a3*exp(-beta.*rmf.^2).*(Mayfly(i).Position-Mayflyf(i).Position); else Mayflyf(i).Velocity = g*Mayflyf(i).Velocity+fl*(e); end % Apply Velocity Limits Mayflyf(i).Velocity = max(Mayflyf(i).Velocity,VelMin); Mayflyf(i).Velocity = min(Mayflyf(i).Velocity,VelMax); % Update Position Mayflyf(i).Position = Mayflyf(i).Position + Mayflyf(i).Velocity; % Velocity Mirror Effect %IsOutside=(Mayflyf(i).Position<LowerBound | Mayflyf(i).Position>UpperBound); %Mayflyf(i).Velocity(IsOutside)=-Mayflyf(i).Velocity(IsOutside); % Position Limits Mayflyf(i).Position = max(Mayflyf(i).Position,LowerBound); Mayflyf(i).Position = min(Mayflyf(i).Position,UpperBound); % Evaluation [Mayflyf(i).Cost, Mayflyf(i).Out] = ObjectiveFunction(Mayflyf(i).Position); funccount=funccount+1; % Update Global Best (Uncomment if you use the PGB-IMA version) if Mayflyf(i).Best.Cost<GlobalBest.Cost GlobalBest=Mayflyf(i).Best; end end for i=1:nPop % Update Males rpbest=(Mayfly(i).Best.Position-Mayfly(i).Position); rgbest=(GlobalBest.Position-Mayfly(i).Position); e=unifrnd(-1,+1,ProblemSize); % Update Velocity if Mayfly(i).Cost>GlobalBest.Cost Mayfly(i).Velocity = g*Mayfly(i).Velocity ... +a1*exp(-beta.*rpbest.^2).*(Mayfly(i).Best.Position-Mayfly(i).Position) ... +a2*exp(-beta.*rgbest.^2).*(GlobalBest.Position-Mayfly(i).Position); else Mayfly(i).Velocity = g*Mayfly(i).Velocity+dance*(e); end % Apply Velocity Limits Mayfly(i).Velocity = max(Mayfly(i).Velocity,VelMin); Mayfly(i).Velocity = min(Mayfly(i).Velocity,VelMax); % Update Position Mayfly(i).Position = Mayfly(i).Position + Mayfly(i).Velocity; % Velocity Mirror Effect %IsOutside=(Mayfly(i).Position<LowerBound | Mayfly(i).Position>UpperBound); %Mayfly(i).Velocity(IsOutside)=-Mayfly(i).Velocity(IsOutside); % Position Limits Mayfly(i).Position = max(Mayfly(i).Position,LowerBound); Mayfly(i).Position = min(Mayfly(i).Position,UpperBound); % Evaluation [Mayfly(i).Cost, Mayfly(i).Out] = ObjectiveFunction(Mayfly(i).Position); funccount=funccount+1; % Update Personal Best if Mayfly(i).Cost<Mayfly(i).Best.Cost Mayfly(i).Best.Position=Mayfly(i).Position; Mayfly(i).Best.Cost=Mayfly(i).Cost; Mayfly(i).Best.Out=Mayfly(i).Out; % Update Global Best if Mayfly(i).Best.Cost<GlobalBest.Cost GlobalBest=Mayfly(i).Best; end end end [~, SortMayflies]=sort([Mayfly.Cost]); Mayfly=Mayfly(SortMayflies); [~, SortMayflies]=sort([Mayflyf.Cost]); Mayflyf=Mayflyf(SortMayflies); % MATE MayflyOffspring=repmat(empty_mayfly,nc/2,2); for k=1:nc/2 % Select Parents i1=k; i2=k; p1=Mayfly(i1); p2=Mayflyf(i2); % Apply Crossover [MayflyOffspring(k,1).Position, MayflyOffspring(k,2).Position]=Crossover(p1.Position,p2.Position,LowerBound,UpperBound); % Evaluate Offsprings [MayflyOffspring(k,1).Cost, MayflyOffspring(k,1).Out]=ObjectiveFunction(MayflyOffspring(k,1).Position); if MayflyOffspring(k,1).Cost<GlobalBest.Cost GlobalBest=MayflyOffspring(k,1); end funccount=funccount+1; [MayflyOffspring(k,2).Cost, MayflyOffsprin