【文章內(nèi)容簡介】 % m_numendTBestPath=path0。disp(BestPath)figure。plot(PointCoordinates(path0,1),PointCoordinates(path0,2),PointCoordinates(path0,1),PointCoordnates(path0,2),39。*39。)minDistance=leng(path0,m) if (dt0)|(exp(dt/T)rand(1)) path0=path1。% dt。% Distance=leng(path2,m)。 iter_num=iter_num+1。 m_num=0。 else m_num=m_num+1 。% iter_num=iter_num+1。 end end T=*T。% iter_num% m_numendTBestPath=path0。disp(BestPath)figure。plot(PointCoordinates(path0,1),PointCoordinates(path0,2),PointCoordinates(path0,1),PointCoordnates(path0,2),39。*39。)minDistance=leng(path0,m)上述為主程序，子程序和孔群坐標(biāo)見附錄。對于單鉆頭的假設(shè)，采用模擬退火算法由MATLAB軟件進(jìn)行數(shù)據(jù)處理，根據(jù)最佳切刀方案（defghabcdef）得出各個刀所加工路線的隨機(jī)圖形、最優(yōu)圖形、最短距離如下：① d刀加工D、G孔的隨機(jī)圖形（圖2）、最優(yōu)圖形（圖3）和最短距離： 圖2 圖 3minDistance =+007（mil）②e刀加工D、I孔的隨機(jī)圖形（圖4）、最優(yōu)圖形（圖5）和最短距離： 圖4 圖5minDistance =+007(mil)③f刀加工J孔的隨機(jī)圖形（圖6）、最優(yōu)圖形（圖7）和最短距離： 圖6 圖7minDistance =+006(mil)④g刀加工F、G孔的隨機(jī)圖形（圖8）、最優(yōu)圖形（圖9）和最短距離： 圖8 圖9minDistance =+006(mil)⑤h刀加工F、H孔的隨機(jī)圖形（圖10）、最優(yōu)圖形（圖11）和最短距離： 圖10 圖11minDistance =+006(mil)⑥a刀加工A、C孔的隨機(jī)圖形（圖12）、最優(yōu)圖形（圖13）和最短距離： 圖12 圖13minDistance =+007(mil)⑦b刀加工B孔的隨機(jī)圖形（圖14）、最優(yōu)圖形（圖15）和最短距離： 圖14 圖15minDistance =+007(mil)⑧c刀加工C、E、I、J孔的隨機(jī)圖形（圖16）、最優(yōu)圖形（圖17）和最短距離： 圖16 圖17minDistance =+007(mil)⑨f刀加工E、G孔的隨機(jī)圖形（圖18）、最優(yōu)圖形（圖19）和最短距離： 圖18 圖19 minDistance =+006(mil)由上圖1到圖18可知單鉆頭作業(yè)的最優(yōu)線路（包括刀具轉(zhuǎn)換方案）的條件下得到最短距離為各刀依次加工對應(yīng)孔型最優(yōu)路徑長之和，結(jié)果如下：Shortest_Length = (mil)= (mm)注：1mil=所有鉆頭的行進(jìn)速度是相同的，為180mm/s，所以：鉆頭的進(jìn)行時間=/180=(s)刀具轉(zhuǎn)換時間=1118=198(s)則単鉆頭打孔的作業(yè)時間(T)為鉆頭進(jìn)行時間與刀具轉(zhuǎn)換時間之和，即：作業(yè)時間=(s)綜上，作業(yè)成本=鉆頭進(jìn)行成本+刀具切換成本, 即：鉆頭進(jìn)行成本==(元)刀具切換成本=1987/60=(元)則：作業(yè)成本=+=(元)根據(jù)遺傳算法編寫代碼如下：% Verify Inputs[N,dims] = size(xy)。[nr,nc] = size(dmat)。if N ~= nr || N ~= ncerror(39。Invalid XY or DMAT inputs!39。)endn = N。% Sanity Checkspop_size = 4*ceil(pop_size/4)。num_iter = max(1,round(real(num_iter(1))))。show_prog = logical(show_prog(1))。show_res = logical(show_res(1))。% Initialize pop = zeros(pop_size,n)。for k = 1:pop_sizepop(k,:) = randperm(n)。end% Run the GAglobal_min = Inf。total_dist = zeros(1,pop_size)。dist_history = zeros(1,num_iter)。tmp_pop = zeros(4,n)。new_pop = zeros(pop_size,n)。if show_progpfig = figure(39。Name39。,39。TSP_GA | Current BestSolution39。,39。Numbertitle39。,39。off39。)。Endfor iter = 1:num_iter% Evaluate Each Population Member (Calculate Total Distance)for p = 1:pop_sized = dmat(pop(p,n),pop(p,1))。 % Closed Pathfor k = 2:nd = d + dmat(pop(p,k1),pop(p,k))。endtotal_dist(p) = d。 end% Find the Best Route[min_dist,index] = min(total_dist)。dist_history(iter) = min_dist。if min_dist global_minglobal_min = min_dist。opt_rte = pop(index,:)。if show_prog% Plot the Best Routefigure(pfig)。rte = opt_rte([1:n 1])。if dims == 3, plot3(xy(rte,1),xy(rte,2),xy(rte,3),39。39。)。else plot(xy(rte,1),xy(rte,2),39。39。)。 endtitle(sprintf(39。Total Distance = %, Iteration = %d39。,min_dist,iter))。endend% Genetic Algorithm Operatorsrand_pair = randperm(pop_size)。for p = 4:4:pop_sizertes = pop(rand_pair(p3:p),:)。dists = total_dist(rand_pair(p3:p))。 [ignore,idx] = min(dists)。 best_of_4_rte = rtes(idx,:)。 ins_pts = sort(ceil(n*rand(1,2)))。