TLBOBPP.m

clc;
clear;
close all;

%% Problem 
tic
Items = [6 3 4 6 8 7 4 7 7 5 5 6 7 7 6 4 8 7 8 8 2 1 4 9 6];
BinSize = 35;

model = CreateModel(Items,BinSize);  % 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

%% TLBO Parameters
MaxIt = 200;         % Maximum Number of Iterations
nPop = 20;           % Population Size

%% Start 
% Empty Individuals
empty_individual.Position = [];
empty_individual.Cost = [];
empty_individual.Sol = [];

% Population Array
pop = repmat(empty_individual, nPop, 1);
% Initialize Best Solution
BestSol.Cost = inf;
% Population Members
for i = 1:nPop
pop(i).Position = unifrnd(VarMin, VarMax, VarSize);
[pop(i).Cost pop(i).Sol] = CostFunction(pop(i).Position);
if pop(i).Cost < BestSol.Cost
BestSol = pop(i);
end
end
% Best Cost Record
BestCost = zeros(MaxIt, 1);

%% TLBO 
for it = 1:MaxIt
% Calculate Population Mean
Mean = 0;
for i = 1:nPop
Mean = Mean + pop(i).Position;
end
Mean = Mean/nPop;
% Select Teacher
Teacher = pop(1);
for i = 2:nPop
if pop(i).Cost < Teacher.Cost
Teacher = pop(i);
end
end

% Teacher Phase
for i = 1:nPop
% Create Empty Solution
newsol = empty_individual;
% Teaching Factor
TF = randi([1 2]);
% Teaching (moving towards teacher)
newsol.Position = pop(i).Position ...
+ rand(VarSize).*(Teacher.Position - TF*Mean);
% Clipping
newsol.Position = max(newsol.Position, VarMin);
newsol.Position = min(newsol.Position, VarMax);
% Evaluation
[newsol.Cost newsol.Sol] = CostFunction(newsol.Position);
% Comparision
if newsol.Cost<pop(i).Cost
pop(i) = newsol;
if pop(i).Cost < BestSol.Cost
BestSol = pop(i);
end
end
end
% Learner Phase
for i = 1:nPop
A = 1:nPop;
A(i) = [];
j = A(randi(nPop-1));
Step = pop(i).Position - pop(j).Position;
if pop(j).Cost < pop(i).Cost
Step = -Step;
end
% Create Empty Solution
newsol = empty_individual;
% Teaching (moving towards teacher)
newsol.Position = pop(i).Position + rand(VarSize).*Step;
% Clipping
newsol.Position = max(newsol.Position, VarMin);
newsol.Position = min(newsol.Position, VarMax);
% Evaluation
[newsol.Cost newsol.Sol] = CostFunction(newsol.Position);
% Comparision
if newsol.Cost<pop(i).Cost
pop(i) = newsol;
if pop(i).Cost < BestSol.Cost
BestSol = pop(i);
end
end
end
BestCost(it) = BestSol.Cost;
% Iteration 
disp(['In ITR ' num2str(it) ': TLBO Cost Value Is = ' num2str(BestCost(it))]);
end

%% Results
figure;
plot(BestCost,'k', 'LineWidth', 2);
xlabel('ITR');
ylabel('Cost Value');
ax = gca; 
ax.FontSize = 14; 
ax.FontWeight='bold';
set(gca,'Color','c')
grid on;

%% Statistics
items=model.v;
itemindex=BestSol.Sol.B;
sii=size(itemindex);sii=sii(1,1);
for iii=1:sii
ii(iii)=string(num2str(itemindex{iii}));
end;

itemst=model.v;
for i=1: sii
itemvalue(i)=string(num2str(itemst(itemindex{i})));
end;
itemvalue=itemvalue';
%
disp(['Number of Items is ' num2str(model.n)]);
disp(['Items are ' num2str(items)]);
disp(['Bins size is ' num2str(model.Vmax)]);
disp(['Selected bins is ' num2str(BestCost(end))]);
disp(['Selected bins indexes are ']);
itemindex
disp(['Selected bins values are ']);
itemvalue

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