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⛄ 内容介绍
了提高机器人在全局静态环境下路径规划的速度,本文提出了一种基于人工蜂群算法的机器人路径规划算法.该算法将蜜蜂分为侦察蜂,跟随蜂和引领蜂并分别实施不同的搜索策略.为了提高算法的环境适应性和搜索多样性,侦察蜂采用大步长在环境中进行花源搜索,以快速寻找最佳目标点;为了加快算法的搜索速度,同时使算法向最优路径收敛,使用跟随蜂进行局部路径搜索,以距离最近策略进行下一节点选择.大量仿真实验结果表明,该算法能在不同环境下规划出全局最优或近似最优路径,比相近的规划算法速度更快,效率更高.
⛄ 部分代码
clear all
close all
clc
NP=40; %/* The number of colony size (employed bees+onlooker bees)*/
FoodNumber=NP/2; %/*The number of food sources equals the half of the colony size*/
maxCycle=150; %/*The number of cycles for foraging {a stopping criteria}*/
limit=0.1*maxCycle; %/*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/
D= 40;
ub=ones(1,D).*55; %/*lower bounds of the parameters. */
lb=ones(1,D).*-90;%/*upper bound of the parameters.*/
runtime=1;%/*Algorithm can be run many times in order to see its robustness*/
GlobalMins=zeros(1,runtime);
Range = repmat((ub-lb),[FoodNumber 1]);
Lower = repmat(lb, [FoodNumber 1]);
Foods = rand(FoodNumber,D) .* Range + Lower;
for r=1:runtime
for i = 1:FoodNumber
ObjVal(i) = calcu(Foods(i,:));
end
Fitness = calculateFitness(ObjVal);
trial=zeros(1,FoodNumber);
BestInd=find(ObjVal==min(ObjVal));
BestInd=BestInd(end);
GlobalMin = ObjVal(BestInd);
GlobalParams=Foods(BestInd,:);
iter=1;
while ((iter <= maxCycle)),
for i=1:(FoodNumber)
Param2Change=fix(rand*D)+1;
neighbour=fix(rand*(FoodNumber))+1;
while(neighbour==i)
neighbour=fix(rand*(FoodNumber))+1;
end;
sol=Foods(i,:);
sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;
%
ind=find(sol<lb);
libai = rand(1,D).*(ub-lb)+lb;
sol(ind)=libai(ind);
ind=find(sol>ub);
libai = rand(1,D).*(ub-lb)+lb;
sol(ind)=libai(ind);
%
ObjValSol = calcu(sol);
FitnessSol=calculateFitness(ObjValSol);
if (FitnessSol>Fitness(i))
Foods(i,:)=sol;
Fitness(i)=FitnessSol;
ObjVal(i)=ObjValSol;
trial(i)=0;
else
trial(i)=trial(i)+1;
end;
end;
prob=(0.9.*Fitness./max(Fitness))+0.1;
i=1;
t=0;
while(t<FoodNumber)
if(rand<prob(i))
t=t+1;
Param2Change=fix(rand*D)+1;
neighbour=fix(rand*(FoodNumber))+1;
while(neighbour==i)
neighbour=fix(rand*(FoodNumber))+1;
end;
sol=Foods(i,:);
sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;
%
ind=find(sol<lb);
libai = rand(1,D).*(ub-lb)+lb;
sol(ind)=libai(ind);
ind=find(sol>ub);
libai = rand(1,D).*(ub-lb)+lb;
sol(ind)=libai(ind);
%
ObjValSol = calcu(sol);
FitnessSol=calculateFitness(ObjValSol);
if (FitnessSol>Fitness(i))
Foods(i,:)=sol;
Fitness(i)=FitnessSol;
ObjVal(i)=ObjValSol;
trial(i)=0;
else
trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/
end;
end;
i=i+1;
if (i==(FoodNumber)+1)
i=1;
end;
end;
ind=find(ObjVal==min(ObjVal));
ind=ind(end);
if (ObjVal(ind)<GlobalMin)
GlobalMin=ObjVal(ind);
GlobalParams=Foods(ind,:);
end;
ind=find(trial==max(trial));
ind=ind(end);
if (trial(ind)>limit)
trial(ind)=0;
sol=(ub-lb).*rand(1,D)+lb;
ObjValSol = calcu(sol);
FitnessSol=calculateFitness(ObjValSol);
Foods(ind,:)=sol;
Fitness(ind)=FitnessSol;
ObjVal(ind)=ObjValSol;
end;
fprintf('iteration = %d ObjVal=%g\n',iter,GlobalMin);
aaaaa(iter) = GlobalMin;
iter=iter+1;
end % End of ABC
storeer(r,:) = aaaaa;
end
radar1 = [100,200,300,120,220,320,70,170,270,140,240,390,420];
radar2 = [0,0,0,-50,-50,-50,40,40,40,70,20,40,-30];
r = [24,24,24,24,24,24,24,24,24,24,24,24,30];
fenmu = [71.8976 71.8976 71.8976 71.8976 71.8976 71.8976 71.8976 71.8976 71.8976 71.8976 71.8976 71.8976 89.8720];
figure (1)
a = mean(storeer);
plot(a)
figure (2)
hold on
plot(0,0,'k*')
plot(500,0,'ks')
for i = 1:13
hold on
plot(radar1(i),radar2(i),'ko');
cir_plot([radar1(i),radar2(i)],r(i));
end
legend('starting point','target point','threat center')
axis equal
for i = 1:(D-1)
plot([500/(D+1)*i,500/(D+1)*(i+1)],[GlobalParams(i),GlobalParams(i+1)],'LineWidth',2);
hold on
end
plot([0,500/(D+1)*(1)],[0,GlobalParams(1)],'LineWidth',2);
plot([500/(D+1)*D,500],[GlobalParams(D),0],'LineWidth',2);
⛄ 运行结果
⛄ 参考文献
[1] 夏瑞, 赵磊, 吴书宇,等. 基于人工蜂群算法的无人机协同路径规划[J]. 无线互联科技, 2018, 15(13):9.
[2] 王海泉, 胡瀛月, 廖伍代,等. 基于改进人工蜂群算法的机器人路径规划[J]. 控制工程, 2016, 23(9):5.
[3] 李保胜, 李士心, 刘晓倩,等. 基于改进人工蜂群算法的无人机路径规划研究[J]. 计算机科学与应用, 2022, 12(9):6.
[4] 张海涛. 基于人工蜂群算法的车辆主动悬架LQG控制设计[J]. 噪声与振动控制, 2016, 036(005):65-69,81.
