clc;
clear;
close all;
warning off;
addpath(genpath(pwd));
CostFunction = @(x) Sphere(x); % Objective Function
nVar = 5; % Number of Decision Variables
VarSize = [1 nVar]; % Decision Variables Matrix Size
VarMin = -10; % Lower Bound of Decision Variables
VarMax = 10; % Upper Bound of Decision Variables
MaxIt = 200; % Maximum Number of Iterations
nPop0 = 10; % Initial Population Size
nPop = 25; % Maximum Population Size
Smin = 0; % Minimum Number of Seeds
Smax = 5; % Maximum Number of Seeds
Exponent = 2; % Variance Reduction Exponent
sigma_initial = 0.5; % Initial Value of Standard Deviation
sigma_final = 0.001; % Final Value of Standard Deviation
empty_plant.Position = [];
empty_plant.Cost = [];
pop = repmat(empty_plant, nPop0, 1); % Initial Population Array
for i = 1:numel(pop)
% Initialize Position
pop(i).Position = unifrnd(VarMin, VarMax, VarSize);
% Evaluation
pop(i).Cost = CostFunction(pop(i).Position);
end
% Initialize Best Cost History
BestCosts = zeros(MaxIt, 1);
for it = 1:MaxIt
% Update Standard Deviation
sigma = ((MaxIt - it)/(MaxIt - 1))^Exponent * (sigma_initial - sigma_final) + sigma_final;
% Get Best and Worst Cost Values
Costs = [pop.Cost];
BestCost = min(Costs);
WorstCost = max(Costs);
% Initialize Offsprings Population
newpop = [];
% Reproduction
for i = 1:numel(pop)
ratio = (pop(i).Cost - WorstCost)/(BestCost - WorstCost);
S = floor(Smin + (Smax - Smin)*ratio);
for j = 1:S
% Initialize Offspring
newsol = empty_plant;
% Generate Random Location
newsol.Position = pop(i).Position + sigma * randn(VarSize);
% Apply Lower/Upper Bounds
newsol.Position = max(newsol.Position, VarMin);
newsol.Position = min(newsol.Position, VarMax);
% Evaluate Offsring
newsol.Cost = CostFunction(newsol.Position);
% Add Offpsring to the Population
newpop = [newpop
newsol]; %#ok
end
end
% Merge Populations
pop = [pop
newpop];
% Sort Population
[~, SortOrder] = sort([pop.Cost]);
pop = pop(SortOrder);
% Competitive Exclusion (Delete Extra Members)
if numel(pop)>nPop
pop = pop(1:nPop);
end
% Store Best Solution Ever Found
BestSol = pop(1);
% Store Best Cost History
BestCosts(it) = BestSol.Cost;
% Display Iteration Information
disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCosts(it))]);
end
figure;
plot(BestCosts, 'LineWidth', 2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;
