clc clear close all %% 输入数据 G=[ 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0]; % G=[ 0 1 1 1 0 % 1 0 0 0 0 % 0 0 0 0 1 % 0 0 0 0 1 % 0 1 1 0 1]; G = zeros(10,10); d = randperm(95,21)+1; d=sort(d); G(d) = 1; %% 栅格绘制 drawShanGe(G,0) title('栅格地图') %% S = [1 1]; % 起点 E = [10 10]; % 终点 G0 = G; G = G0(S(1):E(1),S(2):E(2)); % 该方式是为了方便更改起点与终点 [Xmax,dim] = size(G); % 栅格地图列数为粒子维数，行数为粒子的变化范围 dim = dim - 2; % 减2是去掉起点与终点 %% 参数设置 maxgen = 50; % 最大迭代次数 NP = 30; % 种群数量 %%%%%%%%%%%%%%%%%%%%%%%%%%% rPercent = 0.2; %%%%%%%%%%%%%%%%%%%%%%%%%%% pNum = round( NP * rPercent ); % %发现者 Xmin = 1; % 变量下界 %% 初始化 X = zeros(NP,dim); for i = 1:NP for j = 1:dim col = G(:,j+1); % 地图的一列 id = find(col == 0); % 该列*栅格的位置 X(i,j) = id(randi(length(id))); % 随机选择一个*栅格 id = []; end fit( i ) = fitness(X( i, : ),G); end fpbest = fit; % 个体最优适应度 pX = X; % 个体最优位置 XX=pX; [fgbest, bestIndex] = min( fit ); % 全局最优适应度 bestX = X(bestIndex, : ); % 全局最优位置 [fmax,B]=max(fit); worse= X(B,:); %% for gen = 1 : maxgen gen [ ans, sortIndex ] = sort( fit );% Sort. [fmax,B]=max( fit ); worse= X(B,:); [~, Idx] = sort( fpbest ); r2=rand(1); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = 1 : pNum if(r2<0.9) r1=rand(1); a=rand(1,1); if (a>0.1) a=1; else a=-1; end X( i , : ) = pX( i , :)+0.3*abs(pX(i , : )-worse)+a*0.1*(XX( i , :)); % Equation (1) else aaa= randperm(180,1); if ( aaa==0 ||aaa==90 ||aaa==180 ) X( i , : ) = pX( i , :); end theta= aaa*pi/180; X( i , : ) = pX( i , :)+tan(theta).*abs(pX(i , : )-XX( i , :)); % Equation (2) end X( i , :) = Bounds(X(i , : ), Xmin, Xmax ); fit( i ) = fitness( X( i , : ),G ); end [ fMMin, bestII ] = min( fit ); bestXX = X( bestII, : ); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% R=1-gen/maxgen; % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Xnew1 = bestXX.*(1-R); Xnew2 =bestXX.*(1+R); %%% Equation (3) Xnew1= Bounds(Xnew1, Xmin, Xmax ); Xnew2 = Bounds(Xnew2, Xmin, Xmax ); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Xnew11 = bestX.*(1-R); Xnew22 =bestX.*(1+R); %%% Equation (5) Xnew11= Bounds(Xnew11, Xmin, Xmax ); Xnew22 = Bounds(Xnew22, Xmin, Xmax ); for i = ( pNum + 1 ) :12 % Equation (4) X( i, : )=bestXX+((rand(1,dim)).*(pX( i , : )-Xnew1)+(rand(1,dim)).*(pX( i , : )-Xnew2)); X( i , :) = Bounds(X(i , : ), min(Xnew1), max(Xnew2) ); fit( i ) = fitness( X( i , : ),G ); end for i = 13: 19 % Equation (6) X( i, : )=pX( i , : )+((randn(1)).*(pX( i , : )-Xnew11)+((rand(1,dim)).*(pX( i , : )-Xnew22))); X( i , :) = Bounds(X(i , : ), Xmin, Xmax ); fit( i ) = fitness( X( i , : ),G ); end for j = 20 : NP % Equation (7) X( j,: )=bestX+randn(1,dim).*((abs(( pX(j,: )-bestXX)))+(abs(( pX(j,: )-bestX))))./2; X( j , :) = Bounds(X(j , : ), Xmin, Xmax ); fit( j ) = fitness( X( j , : ),G ); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% XX=pX; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 更新个体最优值和全局最优值 for i = 1 : NP if (fit(i) < fpbest(i)) fpbest(i) = fit(i); pX(i, :) = X(i, :); end if(fpbest(i) < fgbest) fgbest = fpbest(i); bestX = pX(i, :); end end bestX = LocalSearch(bestX,Xmax,G); fgbest = fitness(bestX,G); FG(gen,1)=fgbest; end