function [Network2 BestCost] = TrainUsing_PSO_Fcn(Network,Xtr,Ytr)
%% Problem Statement
IW = Network.IW{1,1}; IW_Num = numel(IW);
LW = Network.LW{2,1}; LW_Num = numel(LW);
b1 = Network.b{1,1}; b1_Num = numel(b1);
b2 = Network.b{2,1}; b2_Num = numel(b2);
TotalNum = IW_Num + LW_Num + b1_Num + b2_Num;
NPar = TotalNum;
VarMin = -1*ones(1,TotalNum);
VarMax = +1*ones(1,TotalNum);
CostFuncName = 'Cost_ANN_EA';
%% Algorithm's Parameters
SwarmSize = 300;
MaxIteration = 40;
C1 = 2; % Cognition Coefficient;
C2 = 4 - C1; % Social Coefficient;
%% Initial Population
GBest.Cost = inf;
GBest.Position = [];
GBest.CostMAT = [];
for p = 1:SwarmSize
Particle(p).Position = rand(1,NPar) .* (VarMax - VarMin) + VarMin;
Particle(p).Cost = feval(CostFuncName,Particle(p).Position,Xtr,Ytr,Network);
Particle(p).Velocity = [];
Particle(p).LBest.Position = Particle(p).Position;
Particle(p).LBest.Cost = Particle(p).Cost;
if Particle(p).LBest.Cost < GBest.Cost
GBest.Cost = Particle(p).LBest.Cost;
GBest.Position = Particle(p).LBest.Position;
end
end
%% Start of Optimization
for Iter = 1:MaxIteration
%% Velocity update
for p = 1:SwarmSize
Particle(p).Velocity = C1 * rand * (Particle(p).LBest.Position - Particle(p).Position) + C2 * rand * (GBest.Position - Particle(p).Position);
Particle(p).Position = Particle(p).Position + Particle(p).Velocity;
Particle(p).Position = max(Particle(p).Position , VarMin);
Particle(p).Position = min(Particle(p).Position , VarMax);
Particle(p).Cost = feval(CostFuncName,Particle(p).Position,Xtr,Ytr,Network);
if Particle(p).Cost < Particle(p).LBest.Cost
Particle(p).LBest.Position = Particle(p).Position;
Particle(p).LBest.Cost = Particle(p).Cost;
if Particle(p).LBest.Cost < GBest.Cost
GBest.Cost = Particle(p).LBest.Cost;
GBest.Position = Particle(p).LBest.Position;
end
end
end
%% Display
disp(['Itretion = ' num2str(Iter) '; Best Cost = ' num2str(GBest.Cost) ';'])
GBest.CostMAT = [GBest.CostMAT GBest.Cost];
end
GBest.Position
plot(GBest.CostMAT)
Network2 = ConsNet_Fcn(Network,GBest.Position);
BestCost = GBest.Cost;
end
