1 概述
本文为解决卡车和两个无人机旅行推销员的问题，表示为串联团队，无人机提供最后一英里的努力。使用遗传算法（路由突变）来解决问题。该问题可以解决随机生成坐标的100个城市的D2TSP。该解决方案基于最小化时间而不是距离。最短时间计算基于卡车或无人机在操作中交付的最长时间。操作表示为无人机（或两者）发射、发送交付然后在会合位置恢复的节点或停靠点。每架无人机都受到航程和容量的限制。因此，操作中的停靠范围和停靠次数受到无人机约束的限制。
2 运行结果
部分代码：
% Sanity Checks popSize = max(12,12*ceil(popSize(1)/12)); numIter = max(1,round(real(numIter(1)))); showProg = logical(showProg(1)); showResult = logical(showResult(1)); showWaitbar = logical(showWaitbar(1)); % Initialize the Populations popRoute = zeros(popSize,n); %routes popTrk = zeros(popSize,n); %truck route popDrn = zeros(popSize,n); %drone route popDrn2 = zeros(popSize,n); %drone 2 route popOps = zeros(popSize,n); %operations drone 1 popRoute(1,:) = (1:n); rp =popRoute(1,:); %[rp, optr, oppr, opdr] = map_cir2(rp) ; [rp, optr, oppr, opdr, opdr2] = map_cir2(rp); popRoute(1,:) = map_cir( rp); popTrk(1,:) = optr; popDrn(1,:) = opdr; popDrn2(1,:) = opdr2; popOps(1,:) = oppr; for k = 2:popSize [op, tr, dr, dr2] = rand_ops_d_o(); % op=oppr; tr=optr; dr=opdr; dr2=opdr2; popRoute(k,:) = randperm(n); if mod(k,2)==0 % swap r = sort([randi(n) randi(n)]); r1=r(1); r2=r(2); popRoute(k,:)= popRoute(1,:); popRoute(k,[r1 r2]) = popRoute(k,[r2 r1]); elseif mod(k,3)==0 % flip r = sort([randi(n) randi(n)]); r1=r(1); r2=r(2); popRoute(k,:)= popRoute(1,:); popRoute(k,r1:r2) = popRoute(k,r2:-1:r1); elseif mod(k,4)==0% swap, flip r = sort([randi(n) randi(n)]); r1=r(1); r2=r(2); popRoute(k,:)= popRoute(1,:); popRoute(k,[r1 r2]) = popRoute(k,[r2 r1]); popRoute(k,r1:r2) = popRoute(k,r2:-1:r1); else r = sort([randi(n) randi(n)]); r1=r(1); r2=r(2); popRoute(k,:)= popRoute(1,:); popRoute(k,[r1 r2]) = popRoute(k,[r2 r1]); % popRoute(k,r1:r2) = popRoute(k,r2:-1:r1); end popTrk(k,:) = tr; popOps(k,:) = op; popDrn(k,:) = dr; popDrn2(k,:) = dr2; end % Run the GA globalMin = Inf; totalDist = zeros(1,popSize); totalTime = zeros(1,popSize); % total time for route totalEnergy = zeros(1,popSize); % total energy for route totalCost = zeros(1,popSize); % cost of route totalCostkm = zeros(1,popSize); totalCosthr = zeros(1,popSize); totalEnergyP = zeros(1,popSize); distHistory = zeros(1,numIter); timeHistory = zeros(1,numIter); energyHistory= zeros(1,numIter); costHistory = zeros(1,numIter); costkmHistory= zeros(1,numIter); costhrHistory= zeros(1,numIter); tmpPopRoute = zeros(12,n); tmpPopTrks = zeros(12,n); tmpPopOps = zeros(12,n); tmpPopDrns = zeros(12,n); tmpPopDrns2 = zeros(12,n); newPopRoute = zeros(popSize,n); newPopTrks = zeros(popSize,n); newPopOps = zeros(popSize,n); newPopDrns = zeros(popSize,n); newPopDrns2 = zeros(popSize,n);
3 参考文献
[1]黄书召,田军委,乔路,王沁,苏宇.基于改进遗传算法的无人机路径规划[J.计算机应用,2021,41(02):390-397.
