PSOFunc.m

function GlobalBest = PSOFunc()
    
    global minx;
    global maxx;
    global c;
    global s;

    CostFunction=@(x) FitnessFunction(x);        % Cost Function

    VarMin= minx;         % Lower Bound of Variables
    VarMax= maxx;         % Upper Bound of Variables

    VarSize=[1 c*s];    

    %% PSO Parameters

    MaxIt=150;      % Maximum Number of Iterations

    nPop=50;        % Population Size (Swarm Size)

    % w=1;            % Inertia Weight
    % wdamp=0.99;     % Inertia Weight Damping Ratio
    % c1=2;           % Personal Learning Coefficient
    % c2=2;           % Global Learning Coefficient

    % Constriction Coefficients
    phi1=2.05;
    phi2=2.05;
    phi=phi1+phi2;
    chi=2/(phi-2+sqrt(phi^2-4*phi));
    w=chi;          % Inertia Weight
    wdamp=1;        % Inertia Weight Damping Ratio
    c1=chi*phi1;    % Personal Learning Coefficient
    c2=chi*phi2;    % Global Learning Coefficient

    % Velocity Limits
    VelMax=0.1*(VarMax-VarMin);
    VelMin=-VelMax;
    
    wMin = .4;
    wMax = .9;

    %% Initialization

    empty_particle.Position=[];
    empty_particle.Cost=[];
    empty_particle.Velocity=[];
    empty_particle.Best.Position=[];
    empty_particle.Best.Cost=[];

    particle=repmat(empty_particle,nPop,1);

    GlobalBest.Cost=inf;

    for i=1:nPop

        % Initialize Position
        particle(i).Position=unifrnd(VarMin,VarMax,VarSize);

        % Initialize Velocity
        particle(i).Velocity=zeros(VarSize);

        % Evaluation
        particle(i).Cost=CostFunction(particle(i).Position);

        % Update Personal Best
        particle(i).Best.Position=particle(i).Position;
        particle(i).Best.Cost=particle(i).Cost;

        % Update Global Best
        if particle(i).Best.Cost<GlobalBest.Cost
                        
            GlobalBest=particle(i).Best;

        end

    end

    BestCost=zeros(MaxIt,1);
        
    w=zeros(1,MaxIt);
    
    %% PSO Main Loop

    for it=1:MaxIt

        w(it)=wMax - ((wMax-wMin)*(it/MaxIt));
        
        for i=1:nPop
    
            % Update Velocity
            particle(i).Velocity = w(it)*particle(i).Velocity ...
                +c1*rand(VarSize).*(particle(i).Best.Position-particle(i).Position) ...
                +c2*rand(VarSize).*(GlobalBest.Position-particle(i).Position);

            % Apply Velocity Limits
            particle(i).Velocity = max(particle(i).Velocity,VelMin);
            particle(i).Velocity = min(particle(i).Velocity,VelMax);

            % Update Position
            particle(i).Position = particle(i).Position + particle(i).Velocity;

            % Velocity Mirror Effect
            IsOutside=(particle(i).Position<VarMin | particle(i).Position>VarMax);
            particle(i).Velocity(IsOutside)=-particle(i).Velocity(IsOutside);

            % Apply Position Limits
            particle(i).Position = max(particle(i).Position,VarMin);
            particle(i).Position = min(particle(i).Position,VarMax);

            % Evaluation
            particle(i).Cost = CostFunction(particle(i).Position);

            % Update Personal Best
            if particle(i).Cost<particle(i).Best.Cost

                particle(i).Best.Position=particle(i).Position;
                particle(i).Best.Cost=particle(i).Cost;

                % Update Global Best
                if particle(i).Best.Cost<GlobalBest.Cost

                    GlobalBest=particle(i).Best;

                end

            end

        end

        BestCost(it)=GlobalBest.Cost;


        disp(['Iteration ' num2str(it) ', Best Cost = ' num2str(BestCost(it))]);

        w=w*wdamp;

    end



end