Particle Swarm Optimization (PSO) |Part - 2| with Numerical Example and ...

Particle Swarm Optimization (PSO) Algorithm

Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. 🌞 Particle Swarm Optimization (PSO) Algorithm Matlab code.

Particle Swarm Optimization Main File: main.m

pso;

Particle Swarm Optimization Function File: Sphere(x)

function F1 = Sphere(x) F1 = sum(x.^2); end

Particle Swarm Optimization File Name Save as: pso.m

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;

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