教与学算法（Teaching Learning Based Optimization，TLBO）
教与学算法（Teaching Learning Based Optimization，TLBO）function = ModifiedTLBO(FITNESSFCN,lb,ub,T,NPop)
% Teaching Learning Based optimization (TLBO)
% ModifiedTLBO attempts to solve problems of the following forms:
% min F(X)subject to: lb <= X <= ub
% X
%
% = ModifiedTLBO(FITNESSFCN,lb,ub,T,NPop)
%FITNESSFCN - function handle of the fitness function
%lb - lower bounds on X
%ub - upper bounds on X
%T - number of iterations
%NPop - size of the population (class size)
%X - minimum of the fitness function determined by ModifiedTLBO
%FVAL - value of the fitness function at the minima (X)
%BestFVALIter - the best fintess function value in each iteration
%pop - the population at the end of the specified number of iterations
% preallocation to store the best objective function of every iteration
% and the objective function value of every student
BestFVALIter = NaN(T,1);
obj = NaN(NPop,1);
% Determining the size of the problem
D = length(lb);
% Generation of initial population
pop = repmat(lb, NPop, 1) + repmat((ub-lb),NPop,1).*rand(NPop,D);
%Evaluation of objective function
%Can be vectorized
for p = 1:NPop
obj(p) = FITNESSFCN(pop(p,:));
end
for gen = 1: T
% Partner selection for all students
% Note that randperm has been used to speedup the partner selection.
Partner = randperm(NPop);
% There is a remote possibility that the ith student will have itself as its partner
% No experiment is available in literature on the disadvantages of
% a solution having itself as partner solution.
for i = 1:NPop
% ----------------Begining of the Teacher Phase for ith student-------------- %
mean_stud = mean(pop);
% Determination of teacher
[~,ind] = min(obj);
best_stud = pop(ind,:);
% Determination of the teaching factor
TF = randi(,1,1);
% Generation of a new solution
NewSol = pop(i,:) + rand(1,D).*(best_stud - TF*mean_stud);
% Bounding of the solution
NewSol = max(min(ub, NewSol),lb);
% Evaluation of objective function
NewSolObj = FITNESSFCN(NewSol);
% Greedy selection
if (NewSolObj < obj(i))
pop(i,:) = NewSol;
obj(i) = NewSolObj;
end
% ----------------Ending of the Teacher Phase for ith student-------------- %
% ----------------Begining of the Learner Phase for ith student-------------- %
% Generation of a new solution
if (obj(i)< obj(Partner(i)))
NewSol = pop(i,:) + rand(1, D).*(pop(i,:)- pop(Partner(i),:));
else
NewSol = pop(i,:) + rand(1, D).*(pop(Partner(i),:)- pop(i,:));
end
% Bounding of the solution
NewSol = max(min(ub, NewSol),lb);
% Evaluation of objective function
NewSolObj =FITNESSFCN(NewSol);
% Greedy selection
if(NewSolObj< obj(i))
pop(i,:) = NewSol;
obj(i) = NewSolObj;
end
% ----------------Ending of the Learner Phase for ith student-------------- %
end
% This is not part of the algorithm but is used to keep track of the
% best solution determined till the current iteration
= min(obj);
end
% Extracting the best solution
X = pop(ind,:);
FVAL = BestFVALIter(gen);适应度函数：function F = Rastrigin(X)
= size(X);
F = zeros(ros, 1);
for k = 1: ros
x = X(k,:);
F(k,1) = sum((x.^2 - 10.*cos(2.*pi.*x) + 10));
end主函数：
rng(2,'twister')
FITNESSFCN = @Rastrigin;
lb = -5.12*ones(1,2);
ub = 5.12*ones(1,2);
NPop = 50;
T = 90;
= ModifiedTLBO(FITNESSFCN,lb,ub,T,NPop);
display(['The minimum point is ', num2str(X)])
display(['The fitness function value at the mimimum point is ', num2str(FVAL)])
D = length(lb);
display(['The number of fitness function evaluation is ', num2str(NPop+2*NPop*T)])
plot(1:T,BestFVALIter,'r*')
xlabel('Iteration Number')
ylabel('Value of Fitness function')
grid on
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