%
% Copyright (c) 2015, Yarpiz (www.yarpiz.com)
% All rights reserved. Please read the "license.txt" for license terms.
%
% Project Code: YPAP113
% Project Title: Solving Economic Dispatching using PSO in MATLAB
% Publisher: Yarpiz (www.yarpiz.com)
%
% Developer: S. Mostapha Kalami Heris (Member of Yarpiz Team)
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% Contact Info: [email protected], [email protected]
%
clc;
clear;
close all;
%% Problem Definition
model=CreateModel();
CostFunction=@(x) MyCost(x,model); % Cost Function
nVar=model.nPlant; % Number of Decision Variables
VarSize=[1 nVar]; % Size of Decision Variables Matrix
VarMin=0; % Lower Bound of Variables
VarMax=1; % Upper Bound of Variables
%% PSO Parameters
MaxIt=200; % Maximum Number of Iterations
nPop=100; % 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;
%% Initialization
empty_particle.Position=[];
empty_particle.Cost=[];
empty_particle.Out=[];
empty_particle.Velocity=[];
empty_particle.Best.Position=[];
empty_particle.Best.Cost=[];
empty_particle.Best.Out=[];
particle=repmat(empty_particle,nPop,1);
BestSol.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, particle(i).Out]=CostFunction(particle(i).Position);
% Update Personal Best
particle(i).Best.Position=particle(i).Position;
particle(i).Best.Cost=particle(i).Cost;
particle(i).Best.Out=particle(i).Out;
% Update Global Best
if particle(i).Best.Cost<BestSol.Cost
BestSol=particle(i).Best;
end
end
BestCost=zeros(MaxIt,1);
%% PSO Main Loop
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).*(BestSol.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, particle(i).Out] = 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;
particle(i).Best.Out=particle(i).Out;
% Update Global Best
if particle(i).Best.Cost<BestSol.Cost
BestSol=particle(i).Best;
end
end
end
BestCost(it)=BestSol.Cost;
disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
w=w*wdamp;
end
%% Results
figure;
plot(BestCost,'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');
