1 基于狼群算法之三维路径规划模型
4 部分代码
%% 清空环境
clc
clear all
close all
%% 数据初始化
%下载数据
load HeightData
% HeightData=HeightData';
%网格划分
LevelGrid=10;
PortGrid=21;
%起点终点网格点
starty=6;starth=1;
endy=8;endh=21;
m=1;
%算法参数
PopNumber=10; %种群个数
BestFitness=[]; %最佳个体
iter=100;
%初始信息素
pheromone=ones(21,21,21);
dim=PortGrid*2;
Max_iter=100;
ub=PortGrid;
lb=1;
% initialize alpha, beta, and delta_pos
Alpha_pos=zeros(1,dim);
Alpha_score=inf; %change this to -inf for maximization problems
Beta_pos=zeros(1,dim);
Beta_score=inf; %change this to -inf for maximization problems
Delta_pos=zeros(1,dim);
Delta_score=inf; %change this to -inf for maximization problems
%Initialize the path of search agents
Convergence_curve=zeros(1,Max_iter);
l=0;% Loop counter
%% 初始搜索路径
[path,pheromone]=searchpath(PopNumber,LevelGrid,PortGrid,pheromone, ...
HeightData,starty,starth,endy,endh);
fitness=CacuFit(path); %适应度计算
[bestfitness,bestindex]=min(fitness); %最佳适应度
bestpath=path(bestindex,:); %最佳路径
BestFitness=[BestFitness;bestfitness]; %适应度值记录
l=0;% Loop counter
% Main loop
while l<Max_iter
for i=1:size(path,1)
% Return back the search agents that go beyond the boundaries of the search space
Flag4ub=path(i,:)>ub;
Flag4lb=path(i,:)<lb;
path(i,:)=round((path(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb);
% Calculate objective function for each search agent
fitness=CacuFit(path(i,:));
% Update Alpha, Beta, and Delta
if fitness<Alpha_score
Alpha_score=fitness; % Update alpha
Alpha_pos=path(i,:);
end
if fitness>Alpha_score && fitness<Beta_score
Beta_score=fitness; % Update beta
Beta_pos=path(i,:);
end
end
5 仿真结果
6 参考文献
[1]段海滨, 张岱峰, 范彦铭,等. 从狼群智能到无人机集群协同决策[J]. 中国科学F辑, 2019, 049(001):112-118.
