【路徑規(guī)劃-VRP問題】基于混合K-Means和蟻群算法求解帶容量車輛路徑規(guī)劃CVRP問題附matlab代碼
【路徑規(guī)劃-VRP問題】基于混合K-Means和蟻群算法求解帶容量車輛路徑規(guī)劃CVRP問題附matlab代碼
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博主簡介：擅長智能優(yōu)化算法、神經(jīng)網(wǎng)絡(luò)預(yù)測、信號處理、元胞自動機(jī)、圖像處理、路徑規(guī)劃、無人機(jī)等多種領(lǐng)域的Matlab仿真，完整matlab代碼或者程序定制加qq1575304183。
1 內(nèi)容介紹
隨著市場經(jīng)濟(jì)快速發(fā)展和現(xiàn)代技術(shù)的不斷演變,現(xiàn)代物流業(yè)也得到了空前的發(fā)展.在物流配送活動的各個環(huán)節(jié)中,配送路徑優(yōu)化對企業(yè)提高服務(wù)質(zhì)量,降低物流成本,提高經(jīng)濟(jì)效益起到至關(guān)重要的作用.蟻群優(yōu)化算法作為群智能算法的典型代表,在路徑規(guī)劃求解中表現(xiàn)出良好的效果.本文研究了帶容量約束車輛路徑問題(Capacitated Vehicle Routing Problem,CVRP),并采用蟻群優(yōu)化算法進(jìn)行優(yōu)化求解.實(shí)驗(yàn)結(jié)果表明,蟻群優(yōu)化算法能夠有效地求解帶容量約束車輛路徑問題.
2 部分代碼
%%%%%%%%%%%%%%%%%%%%蟻群算法求函數(shù)極值%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all; %清除所有變量
close all; %清圖
clc; %清屏
m=20; %螞蟻個數(shù)
G_max=200; %最大迭代次數(shù)
Rho=0.9; %信息素蒸發(fā)系數(shù)
P0=0.2; %轉(zhuǎn)移概率常數(shù)
XMAX= 5; %搜索變量x最大值
XMIN= -5; %搜索變量x最小值
YMAX= 5; %搜索變量y最大值
YMIN= -5; %搜索變量y最小值
%%%%%%%%%%%%%%%%%隨機(jī)設(shè)置螞蟻初始位置%%%%%%%%%%%%%%%%%%%%%%
for i=1:m
X(i,1)=(XMIN+(XMAX-XMIN)*rand);
X(i,2)=(YMIN+(YMAX-YMIN)*rand);
Tau(i)=func(X(i,1),X(i,2));
end
step=0.1; %局部搜索步長
for NC=1:G_max
lamda=1/NC;
[Tau_best,BestIndex]=min(Tau);
%%%%%%%%%%%%%%%%%%計(jì)算狀態(tài)轉(zhuǎn)移概率%%%%%%%%%%%%%%%%%%%%
for i=1:m
P(NC,i)=(Tau(BestIndex)-Tau(i))/Tau(BestIndex);
end
%%%%%%%%%%%%%%%%%%%%%%位置更新%%%%%%%%%%%%%%%%%%%%%%%%
for i=1:m
%%%%%%%%%%%%%%%%%局部搜索%%%%%%%%%%%%%%%%%%%%%%
if P(NC,i)<P0
temp1=X(i,1)+(2*rand-1)*step*lamda;
temp2=X(i,2)+(2*rand-1)*step*lamda;
else
%%%%%%%%%%%%%%%%全局搜索%%%%%%%%%%%%%%%%%%%%%%%
temp1=X(i,1)+(XMAX-XMIN)*(rand-0.5);
temp2=X(i,2)+(YMAX-YMIN)*(rand-0.5);
end
%%%%%%%%%%%%%%%%%%%%%邊界處理%%%%%%%%%%%%%%%%%%%%%%%
if temp1<XMIN
temp1=XMIN;
end
if temp1>XMAX
temp1=XMAX;
end
if temp2<YMIN
temp2=YMIN;
end
if temp2>YMAX
temp2=YMAX;
end
%%%%%%%%%%%%%%%%%%螞蟻判斷是否移動%%%%%%%%%%%%%%%%%%
if func(temp1,temp2)<func(X(i,1),X(i,2))
X(i,1)=temp1;
X(i,2)=temp2;
end
end
%%%%%%%%%%%%%%%%%%%%%%%更新信息素%%%%%%%%%%%%%%%%%%%%%%%
for i=1:m
Tau(i)=(1-Rho)*Tau(i)+func(X(i,1),X(i,2));
end
[value,index]=min(Tau);
trace(NC)=func(X(index,1),X(index,2));
end
[min_value,min_index]=min(Tau);
minX=X(min_index,1); %最優(yōu)變量
minY=X(min_index,2); %最優(yōu)變量
minValue=func(X(min_index,1),X(min_index,2)); %最優(yōu)值
figure
plot(trace)
xlabel(’搜索次數(shù)’);
ylabel(’適應(yīng)度值’);
title(’適應(yīng)度進(jìn)化曲線’)
3 運(yùn)行結(jié)果
4 參考文獻(xiàn)
[1]陳廷偉, 施銥鵬, 周敏宣,等. 基于改進(jìn)蟻群算法的帶容量約束車輛路徑問題求解[J]. 信息與電腦, 2022, 34(7):4.