【优化调度】基于帝国企鹅算法求解多扇区航空调度问题附matlab代码

1 简介

基于帝国企鹅算法求解多扇区航空调度问题​

2 部分代码

function [bestY,bestX,recording]=AFO(x,y,option,data)%% Authority%Author: Zhe Yang% E-mail: 454170989@qq.com% School:University of Manchester%% Input% x----positions of initialized populaiton% y----fitnesses of initialized populaiton% option-----parameters set of the algorithm% data------Pre-defined parameters% This parameter is used for solving complex problems is passing case data%% outPut% bestY ----fitness of best individual% bestX ----position of best individual% recording ---- somme data was recorded in this variable%% initializationpe=option.pe;L=option.L;gap0=option.gap0;gap=gap0;dim=option.dim;recording.bestFit=zeros(option.maxIteration+1,1);recording.meanFit=zeros(option.maxIteration+1,1);At=randn(option.numAgent,option.dim);count=1;%% center of population[y_c,position]=min(y);x_c=x(position(1),:);At_c=At(position(1),:);%% memory of populationy_m=y;x_m=x;%% update recordingrecording.bestFit(1)=y_c;recording.meanFit(1)=mean(y_m);%% main loopfor iter=1:option.maxIteration    %Dmp(['AFO,iter:',num2str(iter),',minFit:',num2str(y_c)])    %% Moving Strategy I for center of population    if rem(iter, gap)==0        c0=exp(-30*(iter-gap0)/option.maxIteration); % EQ.2-11        Dx=ones(1,dim);        Dx=c0*Dx/norm(Dx)*norm(option.v_ub-option.v_lb)/2; %EQ.2-12        % +△x        for j=1:dim            tempX(j,:)=x_c;             tempX(j,j)=x_c(1,j)+Dx(j);            if tempX(j,j)>option.ub(j)                tempX(j,j)=option.ub(j);                Dx(1,j)=tempX(j,j)-x_c(1,j);            end            if tempX(j,j)<option.lb(j)                tempX(j,j)=option.lb(j);                Dx(1,j)=tempX(j,j)-x_c(1,j);            end            tempY(j,:)=option.fobj(tempX(j,:),option,data);            if tempY(j)*y_c<0                g0(1,j)=(log(tempY(j))-log(y_c))./Dx(j); %EQ.2-18            else                temp=[tempY(j),y_c];                temp=temp+min(temp)+eps;                g0(1,j)=(log(temp(1))-log(temp(2)))./Dx(j);            end                 g0(isnan(g0))=0;        end        G0=-g0(1,:)*norm(option.v_ub-option.v_lb)/2/norm(g0(1,:)); % part of Eq 2-18         G0(1,G0(1,:)>option.v_ub)=G0(1,G0(1,:)>option.v_ub)/max(G0(1,G0(1,:)>option.v_ub))*max(option.v_ub(G0(1,:)>option.v_ub));        G0(1,G0(1,:)<option.v_lb)=G0(1,G0(1,:)<option.v_lb)/min(G0(1,G0(1,:)<option.v_lb))*min(option.v_lb(G0(1,:)<option.v_lb));        G01=G0;        % -△x        Dx=-ones(1,dim);        Dx=c0*Dx/norm(Dx)*norm(option.v_ub-option.v_lb)/2;        for j=1:dim            tempX(j+dim,:)=x_c;            tempX(j+dim,j)=x_c(1,j)+Dx(j);            if tempX(j+dim,j)>option.ub(j)                tempX(j+dim,j)=option.ub(j);                Dx(1,j)=tempX(j,j)-x_c(1,j);            end            if tempX(j+dim,j)<option.lb(j)                tempX(j+dim,j)=option.lb(j);                Dx(1,j)=tempX(j,j)-x_c(1,j);            end​        if minY<y_c            y_c=tempY(no);            x_c=tempX(no,:);        end        if rand>(no-dim*2)/(option.numAgent-dim*2)*(option.maxIteration-iter)/option.maxIteration            gap=max(option.gapMin,gap-option.dec); %EQ.2-15        end    else        for i=1:option.numAgent            p =tanh(abs(y(i)-y_c)); %EQ.2-30              if rand<p*(option.maxIteration-iter)/option.maxIteration                % EQ 2-28                At(i,:)=option.w2*At(i,:)+option.w4*rand(size(x(1,:))).*(x_c-x(i,:))+option.w5*rand(size(x(1,:))).*(x_m(i,:)-x(i,:));                x(i,:)=x(i,:)+At(i,:); %EQ 2-29                x(i,:)=checkX(x(i,:),option,data);                tempY(i,:)=y(i);                y(i)=option.fobj(x(i,:),option,data);                if tempY(i,:)<y(i)                    for j=1:dim                        r1=randi(option.numAgent);                        r2=randi(option.numAgent);                        v(i,j)=rand.*(x_m(r1,j)-x_m(r2,j))*-sign(y_m(r1)-y_m(r2));                        if std(x_m(:,j))<=exp(-20*iter/option.maxIteration)*(option.v_ub(j)-option.v_lb(j))/2                            if v(i,j)==0                                v(i,j)=randn*(option.v_ub(j)-option.v_lb(j))/2;                            else                                v(i,j)=rand.*sign(x_m(r1,j)-x_m(r2,j))*-sign(y_m(r1)-y_m(r2))*(option.v_ub(j)-option.v_lb(j))/2;                            end                        end                    end                end            else                g0=randn(size(At_c));                r1=randi(option.numAgent);                r2=randi(option.numAgent);​    end    if count>L        for i=1:option.numAgent            x(i,:)=(option.ub-option.lb)*rand+option.lb;            y(i)=option.fobj(x(i,:),option,data);            if y(i)<y_m(i)                y_m(i)=y(i);                x_m(i,:)=x(i,:);                if y_m(i)<y_c                    y_c=y_m(i);                    x_c=x_m(i,:);                    At_c=At(i,:);                end            end        end        count=0;    end    %% 更新记录    recording.bestFit(1+iter)=y_c;    recording.meanFit(1+iter)=mean(y_m);%     recording.std(1+iter)=mean(std(x_m));%     recording.DC(1+iter)=norm(x_m-repmat(x_c,option.numAgent,1));%     recording.x1(1+iter,:)=x(1,:);endbestY=y_c;bestX=x_c;end%%​

3 仿真结果

4 参考文献

[1]芮钧, 陈守伦. MATLAB粒子群算法工具箱求解水电站优化调度问题[J]. 中国农村水利水电, 2009(1):3.

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