【无人机三维路径规划】基于自适应遗传算法求解单无人机三维路径规划问题附

1 简介

2 部分代码

function [globel]=GA( model )
%UNTITLED4 Summary of this function goes here
% Detailed explanation goes here
seeds_fitness =model.seeds_fitness;
chromosome =model.chromosome;
next_chromosome=model.next_chromosome;
AllChromosome=model.AllChromosome;
globel.cost =inf;
%适应度最优值保留
best=zeros(model.MaxIt+1,1);
best(1)=model.globel.cost;
[~,global_index ]=min(seeds_fitness);
for it=1:model.MaxIt
%由于适应度值越小越好
seeds_fitness = 1./seeds_fitness;
total_fitness = sum(seeds_fitness);
seeds_probability = seeds_fitness/ total_fitness;
%计算累计概率
seeds_accumulate_probability = cumsum(seeds_probability, 2);
%根据轮盘赌选择父母,总共选择出NP个子代
for seed=1:2:model.NP
flag =0;
%保证父母和子代都符合要求
[parents,flag] = SelectChromosome(seeds_accumulate_probability,model,chromosome);
%在父母染色体进行基因重组和变异操作，
%并获得保证每个子代都符合约束条件
[ sons] = CrossoverAndMutation( parents,model );
%符合要求以后计算子代的适应度值
[sons(1).cost,sons(1).sol] = FitnessFunction(sons(1),model);
[sons(2).cost,sons(2).sol] = FitnessFunction(sons(2),model);
next_chromosome(seed) = (sons(1));
next_chromosome(seed+1) = (sons(2));
end
%把新旧合并同一种群
AllChromosome(1:model.NP) = chromosome(1:model.NP);
AllChromosome(model.NP+1:model.NP*2) = next_chromosome(1:model.NP);
%精英保留,新旧种群一起比较
[~,order]=sort([AllChromosome.cost]);
%选出迭代的染色体和全局最优染色体
for index =1:model.NP
chromosome(index) = next_chromosome(index);
seeds_fitness(index) =chromosome(index).cost;
if globel.cost >chromosome(index).cost
globel = chromosome(index);
global_index =index;
end
end
%保留最优值
chromosome(global_index) = globel;
best(it+1) = globel.cost;
disp(['it: ',num2str(it),' best value:',num2str(best(it))]);
end
globel.best_plot =best;
end

3 仿真结果

4 参考文献

[1]黄书召, 田军委, 乔路,等. 基于改进遗传算法的无人机路径规划[J]. 计算机应用, 2021, 41(2):8.

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部分理论引用网络文献，若有侵权联系博主删除。