de.m

function [x,err,BestCost]=de(CostFunction,nVar,MaxIt,nPop)

%% Variables 
VarSize = [1 nVar];   % Decision Variables Matrix Size
VarMin = -5;         % Decision Variables Lower Bound
VarMax = 5;         % Decision Variables Upper Bound

% DE Parameters
MaxIt = MaxIt;      % Maximum Number of Iterations
nPop = nPop;        % Population Size
beta_min = 0.2;   % Lower Bound of Scaling Factor
beta_max = 0.8;   % Upper Bound of Scaling Factor
pCR = 0.2;        % Crossover Probability

%% Start

empty_individual.Position = [];
empty_individual.Cost = [];
BestSol.Cost = inf;
pop = repmat(empty_individual, nPop, 1);
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
BestCost = zeros(MaxIt, 1);

%% DE Body

for it = 1:MaxIt
for i = 1:nPop
x = pop(i).Position;
A = randperm(nPop);
A(A == i) = [];
a = A(1);
b = A(2);
c = A(3);
% Mutation
%beta = unifrnd(beta_min, beta_max);
beta = unifrnd(beta_min, beta_max, VarSize);
y = pop(a).Position+beta.*(pop(b).Position-pop(c).Position);
y = max(y, VarMin);
y = min(y, VarMax);
% Crossover
z = zeros(size(x));
j0 = randi([1 numel(x)]);
for j = 1:numel(x)
if j == j0 || rand <= pCR
z(j) = y(j);
else
z(j) = x(j);
end
end
NewSol.Position = z;
NewSol.Cost= CostFunction(NewSol.Position);
if NewSol.Cost<pop(i).Cost
pop(i) = NewSol;
if pop(i).Cost<BestSol.Cost
BestSol = pop(i);
end
end
end
% Update Best Cost
BestCost(it) = BestSol.Cost;
% Show Iteration Information
disp(['In Iteration ' num2str(it) ': DE Cost Is = ' num2str(BestCost(it))]);
end

x=BestSol.Position';
err=BestSol.Cost;