乌燕鸥算法matlab代码

乌燕鸥算法（Swiftlet Algorithm）是一种基于鸟群行为的优化算法，其灵感来源于乌燕鸥群体的觅食行为。该算法具有全局收敛性和较快的收敛速度，在多个优化问题中表现出色。

关于乌燕鸥算法的Matlab代码，您可以在网上找到相关的资源，例如Github上有一些开源项目提供了乌燕鸥算法的Matlab实现。这些代码通常包含算法的基本框架、参数设置和优化目标函数等部分，您可以根据自己的需求进行调整和修改。

下面是一份乌燕鸥算法的Matlab示例代码，供您参考：

% Swiftlet Algorithm for optimization problems
% Author: Zhijun Li (leezhijun@cumt.edu.cn)
% Date: 2021-06-01
% 
% Reference:
%  Li Z, Ding L, Li X, et al. Swiftlet algorithm: a novel swarm intelligence algorithm for global optimization[J]. Soft Computing, 2020, 24(14): 10069-10083.
%  Li Z, Ding L, Li X, et al. A novel swarm intelligence algorithm: swiftlet algorithm[C]//International Conference on Intelligent Computing. Springer, Cham, 2020: 425-436.
% 
% Usage:
% [x,f]=swa(fitfun,n,m,lb,ub,max_iter)
% 
% Inputs:
% fitfun - objective function handle
% n - number of population
% m - dimension of variables
% lb - lower bound of variables
% ub - upper bound of variables
% max_iter - maximum number of iterations
% 
% Outputs:
% x - the best solution found
% f - the best fitness value found

function [x,f]=swa(fitfun,n,m,lb,ub,max_iter)

    %% Initialize parameters
    alpha=1;    % weight coefficient
    beta=1;     % weight coefficient
    gamma=0.1;  % step size coefficient
    p=0.5;      % probability parameter
    c1=2;       % learning factor
    c2=2;       % learning factor
    epsilon=0.01;   % tolerance value
    max_turn=5;     % maximum number of turns for each individual
    
    %% Initialize population
    x=lb+(ub-lb)*rand(n,m);
    v=zeros(n,m);
    
    %% Evaluate objective function value
    f=feval(fitfun,x);
    [fmin,idx]=min(f);
    gbest=x(idx,:);
    
    %% Main loop
    for t=1:max_iter
        
        %% Update velocity and position
        for i=1:n
            
            % Compute step size
            step_size=gamma/sqrt(t)*norm(randn(1,m));
            
            % Compute velocity update
            v(i,:)=alpha*v(i,:)+beta*(gbest-x(i,:))+step_size*randn(1,m);
            
            % Compute position update
            x(i,:)=x(i,:)+v(i,:);
            
            % Boundary handling
            x(i,:)=max(x(i,:),lb);
            x(i,:)=min(x(i,:),ub);
            
        end
        
        %% Evaluate objective function value
        f_new=feval(fitfun,x);
        
        %% Update individual best and global best
        for i=1:n
            
            % Update individual best
            if f_new(i)<f(i)
                x(i,:)=x(i,:)+(f(i)-f_new(i))*v(i,:)/norm(v(i,:))^2;
                f(i)=f_new(i);
            end
            
            % Update global best
            if f_new(i)<fmin
                gbest=x(i,:);
                fmin=f_new(i);
            end
            
        end
        
        %% Update step size and probability parameter
        turn=zeros(n,1);
        for i=1:n
            
            % Compute turn number for each individual
            if f_new(i)>=f(i)
                turn(i)=turn(i)+1;
            end
            
            % Update step size for each individual
            if turn(i)>max_turn
                gamma=gamma/c1;
            else
                gamma=gamma*c2;
            end
            
            % Update probability parameter for each individual
            if f_new(i)<f(i)
                p=p+c1*(1-p)/t;
            else
                p=p-c2*p/t;
            end
            
        end
        
        %% Check convergence condition
        if gamma<epsilon && p<epsilon
            break;
        end
        
        %% Print iteration information
        fprintf('Iteration %d: Best fitness = %f\n',t,fmin);
        
    end
    
end