📋1 概述
本文利用遺傳算法解決了卡車無人機串聯或包裹遞送操作(即UPS,FedEx)的旅行推銷員問題的“最後一英裡”問題。每輛卡車都攜帶一架無人機,該無人機從一個站點發射,将範圍内的包裹運送到附近的站點,同時與卡車并行運作。卡車和無人機并行工作以運送包裹。無人機受到航程和容量的限制。是以,它必須作為操作在卡車附近運作。操作是指卡車發射無人機,卡車和無人機運送到不同的位置,然後卡車在會合地點回收無人機以進行電池更換和裝載。這個想法是确定卡車和無人機(以及操作)的路線,以最大限度地減少總時間。總時間基于操作(啟動-傳遞-恢複)的時間。操作(卡車或無人機)的最大時間用于計算路徑的總時間。
📝2 運作結果
部分代碼:
% Find the Best Route in the Population
[minDist,index] = min(totalDist);
distHistory(iter) = minDist;
if minDist < globalMin
globalMin = minDist;
optPop2 = pop2(index,:);
optPop1 = pop(index,:);
if showprogress % gaph results
tr_route = (optPop2==0).*optPop1;
tr_route = tr_route(tr_route>0);
tr_route = [tr_route tr_route(1)];
if optPop2(n)==1
dr_route = [optPop1 optPop1(1) ];
else
dr_route = [optPop1 ];
end
plot(xy(dr_route,1), xy(dr_route,2), 'k.--'); hold on;
plot(xy(tr_route,1), xy(tr_route,2),'ks-');
xlabel('x-coordinate (km)');
ylabel('y-coordinate (km)');
legend('drone','truck');
title(sprintf('Truck-1-drone time %1.1f',minDist));
hold off;
drawnow;
end
end
% Genetic Algorithm Operators
randomOrder = randperm(popSize); for p = 5:5:popSize
% basically a random sampling in matrix format with a
rtes = pop(randomOrder(p-4:p),:);
dists = totalDist(randomOrder(p-4:p));
% what are the min distances?
[~,idx] = min(dists);
% what is the best route
bestOf5Route = rtes(idx,:);
% randomly select two route insertion points and sort
routeInsertionPoints = sort(ceil(n*rand(1,2))); I = routeInsertionPoints(1);
J = routeInsertionPoints(2);
for k = 1:5 % Mutate the Best row (dist) to get Three New Routes and orig.
% a small matrix of 4 rows of best time
tmpPop(k,:) = bestOf5Route;
switch k
% flip two of the cities and cities between
case 2 % Flip
tmpPop(k,I:J) = tmpPop(k,J:-1:I);
case 3 % Swap
tmpPop(k,[I J]) = tmpPop(k,[J I]);
case 4 % Slide segment
tmpPop(k,I:J) = tmpPop(k,[I+1:J I]);
% tmpPop2(k,I2)=flag;
case 5 % increment sequence one space
tmpPop(k,1:end) = tmpPop(k,[2:end 1]); otherwise % Do Nothing
end
end
% using the original population, create a new population
newPop(p-4:p,:) = tmpPop;
end
pop = newPop; % update entire populations with mutations end
res=[optPop2;
optPop1]