PSO algorithm non-existent field

%% this file is pso1.m ; the driver program
%% Problem Definiton
CostFunction = @(x) Sphere (x); % Cost Function
problem.nVar = 5;                        %Number of Unknown (Decision) Variables
problem.VarMin = -10;                   % Lower Bound of Decision Variables
problem.VarMax = 10;                    % Upper Bound of Decision Variables

%% Parameters of PSO
params.MaxIt = 1000;             % Maximum Number of Iterations
params.nPop = 50;                % Population Size (Swarm Size)
params.W = 1;                      %Intertia Coefficient
params.wdamp = 0.99;               % Damping Ratio of Inertia Coefficient
params.c1 = 2;                     % Personal Acceleration Coefficient
params.c2 = 2;                     %Social Acceleration Coefficient
params.ShowIterInfo = true;         %Flag for Showing Iteration Information

%%calling pso 
out = PSO(problem,params);
BestSol = out.BestSol;
BestCosts = out.BestCosts;

%%Result
figure;
%plot(BestCosts, 'LineWidth',2);
semilogy(BestCosts, 'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;
function out  = PSO(problem,params)

%%this is the PSO.m file, with the function PSO
%% Problem Definiton
CostFunction = problem.Costfunction; % Cost Function
nVar = problem.nVar;                        %Number of Unknown (Decision) Variables
VarSize = [1 nVar];              %Matrix Size of Decision Variables
VarMin = problem.VarMin;         % Lower Bound of Decision Variables

VarMax = problem.VarMax;         % Upper Bound of Decision Variables

%% Parameters of PSO

MaxIt = params.MaxIt;             % Maximum Number of Iterations

nPop = params.nPop;                % Population Size (Swarm Size)

w = params.w;                     %Intertia Coefficient

wdamp = params.wdamp;               % Damping Ratio of Inertia Coefficient

c1 = params.c1;                   % Personal Acceleration Coefficient

c2 = params.c2;                    %Social Acceleration Coefficient

%The Flag for Showing Iteration Information
ShowIterInfo = params.ShowIterInfo;

MaxVelocity = (VarMax - VarMin)*0.2;
MinVelocity = -MaxVelocity;


%%Initialization

% The Particle Template

empty_particle.Position = [];

empty_particle.Velocity = [];

empty_particle.Cost = [];

empty_particle.Best.Position = [];

empty_particle.Best.Cost = [];

% Create Population Array
particle = repmat(empty_particle, nPop, 1);

% Initialize Global Best

GlobalBest.Cost = inf;

% Initialize Population Members

for i=1:nPop
    
    %Generate Random Solution
    particle (i). Position = unifrnd (VarMin, VarMax, VarSize);
    
    % Initialize Velocity
    particle (i). Velocity = zeros (VarSize);
    
    % Evaluation
    
    particle(i).CostFunction(particle (i).Position);
    
    % Update the 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

% Array to Hold Best Cost Value on Each Iteration

BestCosts = zeros(MaxIt,1);

%%Main Loop of PSO

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,MinVelocity);
        particle(i).Velocity = min(particle(i).Velocity,MaxVelocity);
        
        %Update Position
        particle (i). Position = particle (i). Position + particle (i). Velocity;
        %apply lower and upper bound 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
    
    % Store the Best Cost Value
    BestCosts (it) = GlobalBest.Cost;
    
    % Display Iteration Information
    if ShowIterInfo
        
        disp (['Iteration' num2str(it) ': Best Cost ='  num2str(BestCosts (it))]);
    end
    
    %Damping Inertia Coefficient
    w = w * wdamp;
end



out.pop=particle;
out.Bestsol=GlobalBest;
out.BestCosts=BestCosts;


%Result

figure;

%plot(BestCosts, 'LineWidth',2);
semilogy(BestCosts, 'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;

this is the error i am getting

Answer 1

[Sanjana]

Hi Madhav,

I understand that you are trying to access the “Costfunction” defines in pso1.m file, by calling “PSO” function defined in PSO.m file,

Upon analyzing the code, you provided, the “problem” struct variable doesn’t seem to contain “Costfunction” field,

To solve the above issue, add the below line to the code in “pso1.m” file, in order to add “Costfunction” field with value as the defined anonymous “CostFunction” in your code ,

CostFunction = @(x) Sphere (x); % Cost Function
problem.nVar = 5;     %Number of Unknown (Decision) Variables
problem.VarMin = -10; % Lower Bound of Decision Variables
problem.VarMax = 10;
problem.CostFunction = CostFunction;

Hope this helps!

[Walter Roberson]

You can improve performance a bit if you use

CostFunction = @Sphere;