中度宫颈糜烂危害:遗传 MTSP

function varargout = mtspf_ga(xy,dmat,salesmen,min_tour,pop_size,num_iter,show_prog,show_res)
% MTSPF_GA固定多个旅行推销员问题（M - TSP）遗传算法（GA）
%   通过设置查找到的M - TSP的变化（近）的最优解
%   一个遗传算法寻找最短的路线（所需的最小距离
%   每个业务员从一开始的位置，以个别城市旅游
%   回到原来的出发点）
%
% 摘要：
%     1。每个业务员开始的第一点，并在第一次结束
%        点，但以一套独特的城市之间的旅行
%     2。除第一，每个城市访问过的一个业务员
%
% 注：固定的开始/结束的位置被视为第一XY点
%
% 输入：
%     XY（浮动）是一个城市位置的NX2矩阵，其中N是城市数量
%     DMAT（浮动）是一个城市到城市的距离或成本的NxN的矩阵
%     推销员（标量整数）是访问的城市推销员
%     MIN_TOUR（标量整数）的任何最低游览长度
%         推销员，不包括起点/终点
%     POP_SIZE（标量整数）人口规模（应被8整除）
%     NUM_ITER（标量整数）是算法运行所需的迭代数
%     如果真正SHOW_PROG（标量逻辑）显示GA进展
%     SHOW_RES（标量逻辑）的GA结果显示，如果为true
%
% 输出：
%     OPT_RTE（整型数组）是由算法发现的最佳途径
%     OPT_BRK（整型数组）是路线破发点的名单（这些指定的指数
%         到用来获取个人业务员路线路线）
%     MIN_DIST（标浮动）由推销员走过的总距离
%
% 路由/断点详情：
%     如果有10个城市和3个推销员，一个可能的途径/休息
%     组合可能是：RTE = [5 6 9 4 2 8 10 3 7]，brks = [3 7]
%     两者合计，这些代表的解决方案[1 5 6 9 1] [1 4 2 8 1] [1 10 3 7 1]，
%     指定为如下3推销员路线：
%         。业务员1旅游城市1到5至6至9回1
%         。业务员2旅游城市1至4日至2日至8回1
%         。业务员3旅游城市1至10日至3日至7回1
% 2D Example:
%     n = 35;
%     xy = 10*rand(n,2);
%     salesmen = 5;
%     min_tour = 3;
%     pop_size = 80;
%     num_iter = 5e3;
%     a = meshgrid(1:n);
%     dmat = reshape(sqrt(sum((xy(a,:)-xy(a',:)).^2,2)),n,n);
%     [opt_rte,opt_brk,min_dist] = mtspf_ga(xy,dmat,salesmen,min_tour, ...
%         pop_size,num_iter,1,1);
%
% 3D Example:
%     n = 35;
%     xyz = 10*rand(n,3);
%     salesmen = 5;
%     min_tour = 3;
%     pop_size = 80;
%     num_iter = 5e3;
%     a = meshgrid(1:n);
%     dmat = reshape(sqrt(sum((xyz(a,:)-xyz(a',:)).^2,2)),n,n);
%     [opt_rte,opt_brk,min_dist] = mtspf_ga(xyz,dmat,salesmen,min_tour, ...
%         pop_size,num_iter,1,1);
%
% See also: mtsp_ga, mtspo_ga, mtspof_ga, mtspofs_ga, mtspv_ga, distmat
%
% Author:朱小波
% Email:
% Release: 1.3
% Release Date:% 过程的输入和初始化默认值
nargs = 8;
for k = nargin:nargs-1
    switch k
        case 0
            xy = 10*rand(40,2);
        case 1
            N = size(xy,1);
            a = meshgrid(1:N);
            dmat = reshape(sqrt(sum((xy(a,:)-xy(a',:)).^2,2)),N,N);
        case 2
            salesmen = 5;
        case 3
            min_tour = 2;
        case 4
            pop_size = 80;
        case 5
            num_iter = 5e3;
        case 6
            show_prog = 1;
        case 7
            show_res = 1;
        otherwise
    end
end% 验证输入
[N,dims] = size(xy);
[nr,nc] = size(dmat);
if N ~= nr || N ~= nc
    error('Invalid XY or DMAT inputs!')
end
n = N - 1; % Separate Start/End City% Sanity Checks
salesmen = max(1,min(n,round(real(salesmen(1)))));
min_tour = max(1,min(floor(n/salesmen),round(real(min_tour(1)))));
pop_size = max(8,8*ceil(pop_size(1)/8));
num_iter = max(1,round(real(num_iter(1))));
show_prog = logical(show_prog(1));
show_res = logical(show_res(1));% Initializations for Route Break Point Selection
num_brks = salesmen-1;
dof = n - min_tour*salesmen;          % degrees of freedom
addto = ones(1,dof+1);
for k = 2:num_brks
    addto = cumsum(addto);
end
cum_prob = cumsum(addto)/sum(addto);% Initialize the Populations
pop_rte = zeros(pop_size,n);          % population of routes
pop_brk = zeros(pop_size,num_brks);   % population of breaks
for k = 1:pop_size
    pop_rte(k,:) = randperm(n)+1;
    pop_brk(k,:) = randbreaks();
end% Select the Colors for the Plotted Routes
clr = [1 0 0; 0 0 1; 0.67 0 1; 0 1 0; 1 0.5 0];
if salesmen > 5
    clr = hsv(salesmen);
end% Run the GA
global_min = Inf;
total_dist = zeros(1,pop_size);
dist_history = zeros(1,num_iter);
tmp_pop_rte = zeros(8,n);
tmp_pop_brk = zeros(8,num_brks);
new_pop_rte = zeros(pop_size,n);
new_pop_brk = zeros(pop_size,num_brks);
if show_prog
    pfig = figure('Name','MTSPF_GA | Current Best Solution','Numbertitle','off');
end
for iter = 1:num_iter
    % Evaluate Members of the Population
    for p = 1:pop_size
        d = 0;
        p_rte = pop_rte(p,:);
        p_brk = pop_brk(p,:);
        rng = [[1 p_brk+1];[p_brk n]]';
        for s = 1:salesmen
            d = d + dmat(1,p_rte(rng(s,1))); % Add Start Distance
            for k = rng(s,1):rng(s,2)-1
                d = d + dmat(p_rte(k),p_rte(k+1));
            end
            d = d + dmat(p_rte(rng(s,2)),1); % Add End Distance
        end
        total_dist(p) = d;
    end    % Find the Best Route in the Population
    [min_dist,index] = min(total_dist);
    dist_history(iter) = min_dist;
    if min_dist < global_min
        global_min = min_dist;
        opt_rte = pop_rte(index,:);
        opt_brk = pop_brk(index,:);
        rng = [[1 opt_brk+1];[opt_brk n]]';
        if show_prog
            % Plot the Best Route
            figure(pfig);
            for s = 1:salesmen
                rte = [1 opt_rte(rng(s,1):rng(s,2)) 1];
                if dims == 3, plot3(xy(rte,1),xy(rte,2),xy(rte,3),'.-','Color',clr(s,:));
                else plot(xy(rte,1),xy(rte,2),'.-','Color',clr(s,:)); end
                title(sprintf('总路程 = %1.4f, 迭代次数 = %d',min_dist,iter));
                hold on
            end
            if dims == 3, plot3(xy(1,1),xy(1,2),xy(1,3),'ko');
            else plot(xy(1,1),xy(1,2),'ko'); end
            hold off
        end
    end    % Genetic Algorithm Operators
    rand_grouping = randperm(pop_size);
    for p = 8:8:pop_size
        rtes = pop_rte(rand_grouping(p-7:p),:);
        brks = pop_brk(rand_grouping(p-7:p),:);
        dists = total_dist(rand_grouping(p-7:p));
        [ignore,idx] = min(dists);
        best_of_8_rte = rtes(idx,:);
        best_of_8_brk = brks(idx,:);
        rte_ins_pts = sort(ceil(n*rand(1,2)));
        I = rte_ins_pts(1);
        J = rte_ins_pts(2);
        for k = 1:8 % Generate New Solutions
            tmp_pop_rte(k,:) = best_of_8_rte;
            tmp_pop_brk(k,:) = best_of_8_brk;
            switch k
                case 2 % Flip
                    tmp_pop_rte(k,I:J) = fliplr(tmp_pop_rte(k,I:J));
                case 3 % Swap
                    tmp_pop_rte(k,[I J]) = tmp_pop_rte(k,[J I]);
                case 4 % Slide
                    tmp_pop_rte(k,I:J) = tmp_pop_rte(k,[I+1:J I]);
                case 5 % Modify Breaks
                    tmp_pop_brk(k,:) = randbreaks();
                case 6 % Flip, Modify Breaks
                    tmp_pop_rte(k,I:J) = fliplr(tmp_pop_rte(k,I:J));
                    tmp_pop_brk(k,:) = randbreaks();
                case 7 % Swap, Modify Breaks
                    tmp_pop_rte(k,[I J]) = tmp_pop_rte(k,[J I]);
                    tmp_pop_brk(k,:) = randbreaks();
                case 8 % Slide, Modify Breaks
                    tmp_pop_rte(k,I:J) = tmp_pop_rte(k,[I+1:J I]);
                    tmp_pop_brk(k,:) = randbreaks();
                otherwise % Do Nothing
            end
        end
        new_pop_rte(p-7:p,:) = tmp_pop_rte;
        new_pop_brk(p-7:p,:) = tmp_pop_brk;
    end
    pop_rte = new_pop_rte;
    pop_brk = new_pop_brk;
endif show_res
    % Plots
    figure('Name','MTSPF_GA | Results','Numbertitle','off');
    subplot(2,2,1);
    if dims == 3, plot3(xy(:,1),xy(:,2),xy(:,3),'k.');
    else plot(xy(:,1),xy(:,2),'k.'); end
    title('City Locations');
    subplot(2,2,2);
    imagesc(dmat([1 opt_rte],[1 opt_rte]));
    title('Distance Matrix');
    subplot(2,2,3);
    rng = [[1 opt_brk+1];[opt_brk n]]';
    for s = 1:salesmen
        rte = [1 opt_rte(rng(s,1):rng(s,2)) 1];
        if dims == 3, plot3(xy(rte,1),xy(rte,2),xy(rte,3),'.-','Color',clr(s,:));
        else plot(xy(rte,1),xy(rte,2),'.-','Color',clr(s,:)); end
        title(sprintf('Total Distance = %1.4f',min_dist));
        hold on;
    end
    if dims == 3, plot3(xy(1,1),xy(1,2),xy(1,3),'ko');
    else plot(xy(1,1),xy(1,2),'ko'); end
    subplot(2,2,4);
    plot(dist_history,'b','LineWidth',2);
    title('Best Solution History');
    set(gca,'XLim',[0 num_iter+1],'YLim',[0 1.1*max([1 dist_history])]);
end% Return Outputs
if nargout
    varargout{1} = opt_rte;
    varargout{2} = opt_brk;
    varargout{3} = min_dist;
end    % Generate Random Set of Break Points
    function breaks = randbreaks()
        if min_tour == 1 % No Constraints on Breaks
            tmp_brks = randperm(n-1);
            breaks = sort(tmp_brks(1:num_brks));
        else % Force Breaks to be at Least the Minimum Tour Length
            num_adjust = find(rand < cum_prob,1)-1;
            spaces = ceil(num_brks*rand(1,num_adjust));
            adjust = zeros(1,num_brks);
            for kk = 1:num_brks
                adjust(kk) = sum(spaces == kk);
            end
            breaks = min_tour*(1:num_brks) + cumsum(adjust);
        end
    end
end