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一、简介

模拟退火算法介绍

3 模拟退火算法的参数 模拟退火是一种优化算法，它本身是不能独立存在的，需要有一个应用场合，其中温度就是模拟退火需要优化的参数，如果它应用到了聚类分析中，那么就是说聚类分析中有某个或者某几个参数需要优化，而这个参数，或者参数集就是温度所代表的。它可以是某项指标，某项关联度，某个距离等等。

二、源代码

%%clc;clear;close all;%% Problem Definitionmodel=SelectModel();        % Select Model of the Problemmodel.eta=0.1;CostFunction=@(q) MyCost(q,model);       % Cost Function%% SA ParametersMaxIt=500;     % Maximum Number of IterationsMaxIt2=80;      % Maximum Number of Inner IterationsT0=100;         % Initial Temperaturealpha=0.98;     % Temperature Damping Rate%% Initialization% Create Initial Solutionx.Position=CreateRandomSolution(model);[x.Cost, x.Sol]=CostFunction(x.Position);% Update Best Solution Ever FoundBestSol=x;% Array to Hold Best Cost ValuesBestCost=zeros(MaxIt,1);% Set Initial TemperatureT=T0;%% SA Main Loopfor 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    if BestSol.Sol.IsFeasible        FLAG=' *';    else        FLAG='';    end    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it)) FLAG]);        % Reduce Temperature    T=alpha*T;        % Plot Solution    figure(1);    PlotSolution(BestSol.Sol,model);    pause(0.01);    end% function model=CreateRandomModel(I,J)    rmin=10;    rmax=25;    r=randi([rmin rmax],1,I);        TotalDemand=sum(r);    cmean=TotalDemand/J;    cmin=round(cmean);    cmax=round(1.25*cmean);    c=randi([cmin cmax],1,J);        xmin=0;    xmax=200;        ymin=0;    ymax=100;        x=randi([xmin xmax],1,I);    y=randi([ymin ymax],1,I);        alpha_x=0.1;    xm=(xmin+xmax)/2;    dx=xmax-xmin;    x0min=round(xm-alpha_x*dx);    x0max=round(xm+alpha_x*dx);        alpha_y=0.1;    ym=(ymin+ymax)/2;    dy=ymax-ymin;    y0min=round(ym-alpha_y*dy);    y0max=round(ym+alpha_y*dy);        x0=randi([x0min x0max]);    y0=randi([y0min y0max]);        d=zeros(I,I);    d0=zeros(1,I);    for i=1:I        for i2=i+1:I            d(i,i2)=sqrt((x(i)-x(i2))^2+(y(i)-y(i2))^2);            d(i2,i)=d(i,i2);        end                d0(i)=sqrt((x(i)-x0)^2+(y(i)-y0)^2);    end        eta=0.5;        model.I=I;    model.J=J;    model.r=r;    model.c=c;    model.xmin=xmin;    model.xmax=xmax;    model.ymin=ymin;    model.ymax=ymax;    model.x=x;    model.y=y;    model.x0=x0;    model.y0=y0;    model.d=d;    model.d0=d0;    model.eta=eta;end%function qnew=CreateNeighbor(q)    m=randi([1 3]);        switch m        case 1            % Do Swap            qnew=Swap(q);                    case 2            % Do Reversion            qnew=Reversion(q);                    case 3            % Do Insertion            qnew=Insertion(q);    endendfunction qnew=Swap(q)    n=numel(q);        i=randsample(n,2);    i1=i(1);    i2=i(2);        qnew=q;    qnew([i1 i2])=q([i2 i1]);    endfunction qnew=Reversion(q)    n=numel(q);        i=randsample(n,2);    i1=min(i(1),i(2));    i2=max(i(1),i(2));        qnew=q;    qnew(i1:i2)=q(i2:-1:i1);endfunction qnew=Insertion(q)    n=numel(q);        i=randsample(n,2);    i1=i(1);    i2=i(2);        if i1

三、运行结果

四、备注

版本：2014a

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