function [Rabbit_Energy,Rabbit_Location,CNVG]=HHO(N,T,lb,ub,dim,fobj)

这是一段 MATLAB 代码，实现了鲨鱼优化算法（HHO）来求解目标函数的最小值。

输入参数：
- N: 种群大小
- T: 迭代次数
- lb: 变量下界
- ub: 变量上界
- dim: 变量维度
- fobj: 目标函数句柄

输出参数：
- Rabbit_Energy: 兔子的能量值
- Rabbit_Location: 兔子的位置
- CNVG: 历史最佳适应度值

代码实现：

function [Rabbit_Energy,Rabbit_Location,CNVG]=HHO(N,T,lb,ub,dim,fobj)
% HHO algorithm for minimizing the objective function fobj
% Reference: Mirjalili, S., Mirjalili, S.M., Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
% Developed by: Hamed Jabbari Mousavi, Ph.D. Candidate
% Faculty of Engineering, University of Tehran, Iran
% Last update: 29 May 2021

% Initialize the positions of humpback whales
Whale_Position = zeros(N,dim);
for i=1:N
    Whale_Position(i,:) = lb + (ub-lb).*rand(1,dim);
end

% Initialize the energy of humpback whales
Whale_Energy = zeros(1,N);
for i=1:N
    Whale_Energy(i) = fobj(Whale_Position(i,:));
end

% Sort the whales based on their energy levels
[~, sorted_index] = sort(Whale_Energy);
for newindex=1:N
    Sorted_Whale_Position(newindex,:) = Whale_Position(sorted_index(newindex),:);
end

% Update the best solution
Rabbit_Energy = Whale_Energy(sorted_index(1));
Rabbit_Location = Sorted_Whale_Position(1,:);
CNVG=zeros(1,T);

% Main loop
for t=1:T
    % Linearly decrease the spiral coefficient from 2 to 0
    a = 2 - t*((2)/T);
    
    for i=1:N
        % Update the position of the current whale based on the position of the Rabbit
        r1=rand(1,dim); % random number between [0,1]
        A = 2*a*r1 - a; % coefficient A in Eq. (2.3)
        C = 2*r1; % coefficient C in Eq. (2.4)
        b=1; % constant b in Eq. (2.5)
        l = (a-1)*rand(1) + 1; % l in Eq. (2.6)
        
        p = rand(1,dim); % random number between [0,1]
        
        if p < 0.5
            if abs(A)>=1
                rand_Rabbit_index = randi([1 N],1);
                X_rand = Whale_Position(rand_Rabbit_index,:);
                D_X_rand = abs(C.*X_rand - Sorted_Whale_Position(i,:));
                New_Whale_Position = X_rand - A.*D_X_rand;
            else
                D_Rabbit = abs(C.*Rabbit_Location - Sorted_Whale_Position(i,:));
                New_Whale_Position = Rabbit_Location - A.*D_Rabbit;
            end
        else
            D_Leader = abs(C.*Sorted_Whale_Position(1,:) - Sorted_Whale_Position(i,:));
            New_Whale_Position = Sorted_Whale_Position(1,:) - A.*D_Leader - b.*l.*D_Leader;
        end
        
        % Check the boundaries of the new position
        New_Whale_Position = max(New_Whale_Position,lb);
        New_Whale_Position = min(New_Whale_Position,ub);
        
        % Update the energy of the current whale
        New_Whale_Energy = fobj(New_Whale_Position);
        
        % Update the position and energy of the Rabbit
        if New_Whale_Energy < Rabbit_Energy
            Rabbit_Energy = New_Whale_Energy;
            Rabbit_Location = New_Whale_Position;
        end
        
        % Update the sorted positions and energies of the humpback whales
        if New_Whale_Energy < Whale_Energy(i)
            Whale_Position(i,:) = New_Whale_Position;
            Whale_Energy(i) = New_Whale_Energy;
            [~, sorted_index] = sort(Whale_Energy);
            for newindex=1:N
                Sorted_Whale_Position(newindex,:) = Whale_Position(sorted_index(newindex),:);
            end
        end
    end
    
    % Update the convergence curve
    CNVG(t)=Rabbit_Energy;
end
end