【优化调度】基于与学算法实现库存优化控制问题附matlab代码

 1 内容介绍

简要分析了群智能优化算法的研究现状,重点对"教与学"优化算法作了详细的描述,并分析了"教与学"算法的性能及其优缺点;随后介绍了几种改进的"教与学"优化算法,对"教与学"优化算法的应用研究情况进行了论述。最后,说明了目前"教与学"优化算法中存在的问题,并指出"教与学"优化算法未来的研究方向。

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2 仿真代码

% TLBO Optimal Inventory Control

%------------------------------------------
clc;
clear;
close all;
warning('off');
%%-----------------------------------------
model=CreateM();                        % Create Model
model.Umax=100;
CostFunction=@(xhat) MyCost(xhat,model);    % Cost Function
VarSize=[model.K model.H];   % Size of Decision Variables Matrix
nVar=prod(VarSize);    % Number of Decision Variables
VarMin=0;         % Lower Bound of Variables
VarMax=1;         % Upper Bound of Variables
%% TLBO Parameters
MaxIt = 250;        % Maximum Number of Iterations
nPop = 150;           % Population Size
%% Start
% Empty Structure for Individuals
empty_individual.Position = [];
empty_individual.Cost = [];
empty_individual.Sol=[];
% Initialize Population Array
pop = repmat(empty_individual, nPop, 1);
% Initialize Best Solution
BestSol.Cost = inf;
% Initialize 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
% Initialize Best Cost Record
BestCosts = zeros(MaxIt, 1);
%% TLBO Body
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) = 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) = newsol;
if pop(i).Cost < BestSol.Cost
BestSol = pop(i);
end
end
end
% Store Record for Current Iteration
BestCosts(it) = BestSol.Cost;
% Show Iteration Information
disp(['In Iteration ' num2str(it) ': TLBO Best Cost Is = ' num2str(BestCosts(it))]);
figure(1);
PlotSol(BestSol.Sol,model);
pause(0.01);
end
title('TLBO Optimal Inventory Control');
%% Plot
figure;
semilogy(BestCosts,'k', 'LineWidth', 2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;

function PlotSol(sol,model)
K=model.K;
H=model.H;
I0=model.I0;
X0=zeros(K,1);
u=model.u;
Umax=model.Umax;
UC0=sum(u.*I0);

X=sol.X;
I=sol.I;
UC=sol.UC;

subplot(3,1,1);
stairs(0:H,[X X0]',':','LineWidth',3);
xlabel('Time');
ylabel('Order Amount');

subplot(3,1,2);
stairs(0:H,[I0 I]','LineWidth',1);
xlabel('Time');
ylabel('Inventory ');

subplot(3,1,3);
stairs(0:H,[UC0 UC],'LineWidth',1);
hold on;
plot([0 H],[Umax Umax],'c:','LineWidth',3);
hold off;
xlabel('Time');
ylabel('Used Capacity');

end

3 运行结果

4 参考文献

[1]程亚维. 基于教与学优化算法的模糊柔性作业车间的调度问题[J]. 新乡学院学报, 2021, 38(9):6.

[2]杨文明, 顾幸生. 基于混沌优化算法的连续生产过程重调度与库存优化[J]. 华东理工大学学报：自然科学版, 2006, 32(7):4.

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