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⛄ 内容介绍

为了解决三维复杂环境下的无人机航迹规划问题,提出一种基于灰狼优化算法的无人机三维航迹规划方法.模拟真实的地理环境,建立三维地形模型和禁飞区模型,构造合理的评价函数.

⛄ 部分代码

function [Alpha_pos,Alpha_score,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

% 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 positions of search agents

Positinotallow=initialization(SearchAgents_no,dim,ub,lb);

Convergence_curve=zeros(1,Max_iter);

l=0;% Loop counter

% Main loop

while l

    for i=1:size(Positions,1)  

       % Return back the search agents that go beyond the boundaries of the search space

        Flag4ub=Positions(i,:)>ub;

        Flag4lb=Positions(i,:)

        Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;               

        % Calculate objective function for each search agent

        fitness=fobj(Positions(i,:));

        % Update Alpha, Beta, and Delta

        if fitness

            Alpha_score=fitness; % Update alpha

            Alpha_pos=Positions(i,:);

        end

        if fitness>Alpha_score && fitness

            Beta_score=fitness; % Update beta

            Beta_pos=Positions(i,:);

        end

        if fitness>Alpha_score && fitness>Beta_score && fitness

            Delta_score=fitness; % Update delta

            Delta_pos=Positions(i,:);

        end

    end

    a=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0

    % Update the Position of search agents including omegas

    for i=1:size(Positions,1)

        for j=1:size(Positions,2)     

            r1=rand(); % r1 is a random number in [0,1]

            r2=rand(); % r2 is a random number in [0,1]

            A1=2*a*r1-a; % Equation (3.3)

            C1=2*r2; % Equation (3.4)

            D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1

            X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1

            r1=rand();

            r2=rand();

            A2=2*a*r1-a; % Equation (3.3)

            C2=2*r2; % Equation (3.4)

            D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2

            X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2       

            r1=rand();

            r2=rand(); 

            A3=2*a*r1-a; % Equation (3.3)

            C3=2*r2; % Equation (3.4)

            D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3

            X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3             

            Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)

        end

    end

    l=l+1;    

    Convergence_curve(l)=Alpha_score;

end

⛄ 运行结果

⛄ 参考文献

[1]柳长安, 王晓鹏, 刘春阳,等. 基于改进灰狼优化算法的无人机三维航迹规划[J]. 华中科技大学学报：自然科学版, 2017, 45(10):5.

[2]曲承志. 基于混合灰狼算法的无人机航迹规划研究. 

[3]许乐, 赵文龙. 基于新型灰狼优化算法的无人机航迹规划[J]. 电子测量技术, 2022(005):045.

⛳️ 完整代码

❤️部分理论引用网络文献，若有侵权联系博主删除

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