matlab退火算法求解车间调度

clc;
clear;
close all;
%% Problem Definition
model=CreateModel();        % Create Model of the Problem
CostFunction=@(q) MyCost(q,model);       % Cost Function
nVar=model.nVar;        % Number of Decision Variables
VarSize=[1 nVar];       % Size of Decision Variables Matrix
%% SA Parameters
MaxIt=100;      % Maximum Number of Iterations
MaxIt2=25;      % Maximum Number of Inner Iterations
T0=10;          % Initial Temperature
alpha=0.97;     % Temperature Damping Rate
%% Initialization
% Create Initial Solution
x.Position=CreateRandomSolution(model);
[x.Cost, x.Sol]=CostFunction(x.Position);
% Update Best Solution Ever Found
BestSol=x;
% Array to Hold Best Cost Values
BestCost=zeros(MaxIt,1);
% Set Initial Temperature
T=T0;
%% SA Main Loop
for it=1:MaxIt
    for it2=1:MaxIt2
        % Create Neighbor
        xnew.Position=CreateNeighbor(x.Position);
        [xnew.Cost, xnew.Sol]=CostFunction(xnew.Position);
        if xnew.Cost<=x.Cost
            % xnew is better, so it is accepted
            x=xnew;
        else
            % xnew is not better, so it is accepted conditionally
            delta=xnew.Cost-x.Cost;
            p=exp(-delta/T);
            
            if rand<=p
                x=xnew;
            end
        end
        % Update Best Solution
        if x.Cost<=BestSol.Cost
            BestSol=x;
        end
    end
    % Store Best Cost
    BestCost(it)=BestSol.Cost;
    % Display Iteration Information
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
    % Reduce Temperature
    T=alpha*T;
    % Plot Solution
    figure(1);
    PlotSolution(BestSol.Sol,model);
    pause(0.01);
end
%% Results
figure;
plot(BestCost,'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;