How to implement importing data from csv file in optimization algorithm?

Hi,
I have attached the code used for PSO algorithm for 3D box packing. I am finding difficulty to modify the code to integrate with importing data from ‘presents.csv’ and also provide best solution for arrangements of the boxes on the grid, for example as ’50 – GA (10-Apr-2024 03.48.47).csv’.
please do share some ideas on this. thanks.

pso.m
%
% Copyright (c) 2015, Yarpiz (www.yarpiz.com)
% All rights reserved. Please read the "license.txt" for license terms.
%
% Project Code: YPAP105
% Project Title: Solving Bin Packing Problem using PSO, FA and IWO
% Publisher: Yarpiz (www.yarpiz.com)
%
% Developer: S. Mostapha Kalami Heris (Member of Yarpiz Team)
%
% Contact Info: sm.kalami@gmail.com, info@yarpiz.com
%

clc;
clear;
close all;

%% Problem Definition

model = CreateModel(); % Create Bin Packing Model

CostFunction = @(x) BinPackingCost(x, model); % Objective Function

nVar = 2*model.n-1; % Number of Decision Variables
VarSize = [1 nVar]; % Decision Variables Matrix Size

VarMin = 0; % Lower Bound of Decision Variables
VarMax = 1; % Upper Bound of Decision Variables

%% PSO Parameters

MaxIt=1000; % Maximum Number of Iterations

nPop=50; % 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;

nParticleMutation = 2; % Number of Mutations Performed on Each Particle
nGlobalBestMutation = 5; % Number of Mutations Performed on Global Best

%% Initialization

empty_particle.Position=[];
empty_particle.Cost=[];
empty_particle.Sol=[];
empty_particle.Velocity=[];
empty_particle.Best.Position=[];
empty_particle.Best.Cost=[];
empty_particle.Best.Sol=[];

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, particle(i).Sol]=CostFunction(particle(i).Position);

% Update Personal Best
particle(i).Best.Position=particle(i).Position;
particle(i).Best.Cost=particle(i).Cost;
particle(i).Best.Sol=particle(i).Sol;

% Update Global Best
if particle(i).Best.Cost<GlobalBest.Cost
GlobalBest=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).*(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, particle(i).Sol] = CostFunction(particle(i).Position);

% Perform Mutation
for j=1:nParticleMutation
NewParticle = particle(i);
NewParticle.Position = Mutate(particle(i).Position);
[NewParticle.Cost, NewParticle.Sol] = CostFunction(NewParticle.Position);
if NewParticle.Cost <= particle(i).Cost
particle(i) = NewParticle;
end
end

% 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.Sol=particle(i).Sol;

% Update Global Best
if particle(i).Best.Cost<GlobalBest.Cost
GlobalBest=particle(i).Best;
end

end

% Perform Mutation on Global Best
for i=1:nGlobalBestMutation
NewParticle = GlobalBest;
NewParticle.Position = Mutate(GlobalBest.Position);
[NewParticle.Cost, NewParticle.Sol] = CostFunction(NewParticle.Position);
if NewParticle.Cost <= GlobalBest.Cost
GlobalBest = NewParticle;
end
end

BestCost(it)=GlobalBest.Cost;

disp([‘Iteration ‘ num2str(it) ‘: Best Cost = ‘ num2str(BestCost(it))]);

w=w*wdamp;

end

BestSol = GlobalBest;

%% Results

figure;
plot(BestCost,’LineWidth’,2);
xlabel(‘Iteration’);
ylabel(‘Best Cost’);
grid on;Hi,
I have attached the code used for PSO algorithm for 3D box packing. I am finding difficulty to modify the code to integrate with importing data from ‘presents.csv’ and also provide best solution for arrangements of the boxes on the grid, for example as ’50 – GA (10-Apr-2024 03.48.47).csv’.
please do share some ideas on this. thanks.