%
% Copyright (c) 2015, Mostapha Kalami Heris & Yarpiz (www.yarpiz.com)
% All rights reserved. Please read the "LICENSE" file for license terms.
%
% Project Code: YPEA114
% Project Title: Implementation of Artificial Bee Colony in MATLAB
% Publisher: Yarpiz (www.yarpiz.com)
%
% Developer: Mostapha Kalami Heris (Member of Yarpiz Team)
%
% Cite as:
% Mostapha Kalami Heris, Artificial Bee Colony in MATLAB (URL: https://yarpiz.com/297/ypea114-artificial-bee-colony), Yarpiz, 2015.
%
% Contact Info: sm.kalami@gmail.com, info@yarpiz.com
%
clc;
clear;
close all;
%% Problem Definition
CostFunction = @(x) Sphere(x); % Cost Function
nVar = 5; % Number of Decision Variables
VarSize = [1 nVar]; % Decision Variables Matrix Size
VarMin = -10; % Decision Variables Lower Bound
VarMax = 10; % Decision Variables Upper Bound
%% ABC Settings
MaxIt = 1000; % Maximum Number of Iterations
nPop = 100; % Population Size (Colony Size)
nOnlooker = nPop; % Number of Onlooker Bees
L = round(0.6*nVar*nPop); % Abandonment Limit Parameter (Trial Limit)
a = 1; % Acceleration Coefficient Upper Bound
%% Initialization
% Empty Bee Structure
empty_bee.Position = [];
empty_bee.Cost = [];
% Initialize Population Array
pop = repmat(empty_bee, nPop, 1);
% Initialize Best Solution Ever Found
BestSol.Cost = inf;
% Create Initial Population
for i = 1:nPop
pop(i).Position = unifrnd(VarMin, VarMax, VarSize);
pop(i).Cost = CostFunction(pop(i).Position);
if pop(i).Cost <= BestSol.Cost
BestSol = pop(i);
end
end
% Abandonment Counter
C = zeros(nPop, 1);
% Array to Hold Best Cost Values
BestCost = zeros(MaxIt, 1);
%% ABC Main Loop
for it = 1:MaxIt
% Recruited Bees
for i = 1:nPop
% Choose k randomly, not equal to i
K = [1:i-1 i+1:nPop];
k = K(randi([1 numel(K)]));
% Define Acceleration Coeff.
phi = a*unifrnd(-1, +1, VarSize);
% New Bee Position
newbee.Position = pop(i).Position+phi.*(pop(i).Position-pop(k).Position);
% Apply Bounds
newbee.Position = max(newbee.Position, VarMin);
newbee.Position = min(newbee.Position, VarMax);
% Evaluation
newbee.Cost = CostFunction(newbee.Position);
% Comparision
if newbee.Cost <= pop(i).Cost
pop(i) = newbee;
else
C(i) = C(i)+1;
end
end
% Calculate Fitness Values and Selection Probabilities
F = zeros(nPop, 1);
MeanCost = mean([pop.Cost]);
for i = 1:nPop
F(i) = exp(-pop(i).Cost/MeanCost); % Convert Cost to Fitness
end
P = F/sum(F);
% Onlooker Bees
for m = 1:nOnlooker
% Select Source Site
i = RouletteWheelSelection(P);
% Choose k randomly, not equal to i
K = [1:i-1 i+1:nPop];
k = K(randi([1 numel(K)]));
% Define Acceleration Coeff.
phi = a*unifrnd(-1, +1, VarSize);
% New Bee Position
newbee.Position = pop(i).Position+phi.*(pop(i).Position-pop(k).Position);
% Apply Bounds
newbee.Position = max(newbee.Position, VarMin);
newbee.Position = min(newbee.Position, VarMax);
% Evaluation
newbee.Cost = CostFunction(newbee.Position);
% Comparision
if newbee.Cost <= pop(i).Cost
pop(i) = newbee;
else
C(i) = C(i) + 1;
end
end
% Scout Bees
for i = 1:nPop
if C(i) >= L
pop(i).Position = unifrnd(VarMin, VarMax, VarSize);
pop(i).Cost = CostFunction(pop(i).Position);
C(i) = 0;
end
end
% Update Best Solution Ever Found
for i = 1:nPop
if pop(i).Cost <= BestSol.Cost
BestSol = pop(i);
end
end
% Store Best Cost Ever Found
BestCost(it) = BestSol.Cost;
% Display Iteration Information
disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
end
%% Results
figure;
% plot(BestCost, 'LineWidth', 2);
semilogy(BestCost, 'LineWidth', 1);
xlabel('Iteration');
ylabel('Best Cost');
grid on;
