pso.m

%% PSO on Wave Fucntion
clc;
clear;
close all;

%% Cost 
CostFunction = @(x) wave(x);        % Cost Function
nVar = 10;            % Number of Decision Variables
VarSize = [1 nVar];   % Size of Decision Variables Matrix
VarMin = -10;         % Lower Bound of Variables
VarMax = 10;         % Upper Bound of Variables

%% PSO Parameters
tic
MaxIt = 1000;      % Maximum Number of Iterations
nPop = 10;        % Population Size (Swarm Size)
% PSO Parameters
w = 1;            % Inertia Weight
wdamp = 0.99;     % Inertia Weight Damping Ratio
c1 = 1.5;         % Personal Learning Coefficient
c2 = 2.0;         % Global Learning Coefficient
% If you would like to use Constriction Coefficients for PSO, 
% uncomment the following block and comment the above set of parameters.

% % 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;

%% Start

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);

%% PSO Body

for it = 1:MaxIt
for i = 1:nPop
% Update Velocity
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
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
BestSol = GlobalBest;
toc
%% ITR
figure;
plot(BestCost, 'LineWidth', 3);
xlabel('Iteration');
ylabel('Best Cost');
ax = gca; 
ax.FontSize = 14; 
ax.FontWeight='bold';
set(gca,'Color','y')
grid on;