Particle Swarm Optimization (PSO) MATLAB Code |Step-by-Step|~xRay Pixy

The Particle Swarm Optimization algorithm is inspired by the Social Behavior of Birds flocking. PSO is a Population-based stochastic search algorithm.

What is PSO? PSO is a computational method that Optimizes a problem.

How PSO will optimize? By Improving a Candidate Solution.

Particle Swarm Optimization (PSO) MATLAB Code

clear;

close all;

%% Fitness Function Calling

FitnessFunction=@(x) Sphere(x);        % Fitness Function Calling

% Total Number of Decision Variables Used

nVar=10;                 

% Size of Decision Variables Matrix             

VarSize=[1 nVar];                     

% Lower Bound 

LowerBound =-10;        

% Upper Bound                

UpperBound = 10;                     

%% Parameters Initialization Phase

% Maximum Number of Iterations used.

MaxT=100;      

          

% Total Number of Search Agents used. 

PopulationSize = 10;       

% Initialize PSO Parameters

% Inertia Weight

w=1;            

% Inertia Weight Damping Ratio            

wdamp=0.99;      

% Personal Learning Coefficient           

c1=1.5;    

% Global Learning Coefficient                

c2=2.0;                     

% Velocity Limits

VelMax=0.1*(UpperBound-LowerBound);

VelMin=-VelMax;

%% Initialization Position, Cost, Velocity, Best_Position, Best_Cost

empty_particle.Position=[];

empty_particle.Cost=[];

empty_particle.Velocity=[];

empty_particle.Best.Position=[];

empty_particle.Best.Cost=[];

particle=repmat(empty_particle,PopulationSize ,1);

GlobalBest.Cost=inf;

for i=1:PopulationSize 

    

    % Initialize Position for each search Agent in the search space

    particle(i).Position=unifrnd(LowerBound,UpperBound,VarSize);

    

    % Initialize Velocity for each search Agent in the search space

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

    

    % Fitness Values Calculation for each search Agent in the search space

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

    

    % Update Personal Best Position for the particles

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

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

    

    % Update Global Best Position for each search Agent in the search space

    if particle(i).Best.Cost<GlobalBest.Cost   

        GlobalBest=particle(i).Best;   

    end

    

end

BestCost=zeros(MaxT,1);

%% PSO Main Loop

for CurrentIteration=1:MaxT

    

    for i=1:PopulationSize

        

        % Update Velocity for each search Agent in the search space

        particle(i).Velocity = w*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 for Each Particle

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

        

        %  % Check Boundries [-10, 10]

        Outside=(particle(i).Position<LowerBound | particle(i).Position>UpperBound);

        particle(i).Velocity(Outside)=-particle(i).Velocity(Outside);

        

       

        particle(i).Position = max(particle(i).Position,LowerBound);

        particle(i).Position = min(particle(i).Position,UpperBound);

        

        % Fitness Values Calculation

        particle(i).Cost = FitnessFunction(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(CurrentIteration)=GlobalBest.Cost;

    

    disp(['Current Iteration Number = ' num2str(CurrentIteration) ': Best Cost Found = ' num2str(BestCost(CurrentIteration))]);

    

    w=w*wdamp;

    

end

BestSol = GlobalBest;

%% Results

figure;

%plot(BestCost,'LineWidth',2);

semilogy(BestCost,'LineWidth',2);

xlabel('Iteration Numbers');

ylabel('Best Cost Found');

grid on;

Particle Swarm Optimization Algorithm for Solving Economic Load Dispatch Problem

Video Timestamp's:

Introduction: 00:00

Economic Load dispatch Cost Calculation: 00:37

Particle Swarm Optimization Parameter: 01:50

PSO Initialization: 02:19

PSO Main Loop: 03:25

Output : 06:06

Conclusion: 06:33

Economic Load Dispatch Problem (EDP)