北苍鹰优化算法(Northern Goshawk Optimization，NGO)是发表在EEE Access期刊上的智能优化算法

01.引言

基于群体的北苍鹰优化算法(Northern Goshawk Optimization，NGO)是用于模拟北苍鹰在捕食过程中的行为。这种狩猎策略包括两个阶段:猎物识别和追踪过程。描述了该算法的各个步骤，并给出了求解优化问题的数学模型。

02.代码流程图

03.部分代码

北苍鹰优化算法(Northern Goshawk Optimization，NGO)是发表在EEE Access期刊上的智能优化算法


01.引言
基于群体的北苍鹰优化算法(Northern Goshawk Optimization，NGO)是用于模拟北苍鹰在捕食过程中的行为。这种狩猎策略包括两个阶段:猎物识别和追踪过程。描述了该算法的各个步骤，并给出了求解优化问题的数学模型。

02.代码流程图



03.部分代码
%% NGO.
%%
function [Score,Best_pos,NGO_curve]=NGO(Search_Agents,Max_iterations,Lowerbound,Upperbound,dimensions,objective)
tic
disp('PLEASE WAIT, The program is running.')
Lowerbound=ones(1,dimensions).*(Lowerbound);                              % Lower limit for variables
Upperbound=ones(1,dimensions).*(Upperbound);                              % Upper limit for variables
X=[];
X_new=[];
fit=[];
fit_new=[];
NGO_curve=zeros(1,Max_iterations);
%%
for i=1:dimensions
    X(:,i) = Lowerbound(i)+rand(Search_Agents,1).*(Upperbound(i) -Lowerbound(i));              % Initial population
end
for i =1:Search_Agents
    L=X(i,:);
    fit(i)=objective(L);                    % Fitness evaluation (Explained at the top of the page. )
end
for t=1:Max_iterations  % algorithm iteration
    
    %%  update: BEST proposed solution
    [best , blocation]=min(fit);
    
    if t==1
        xbest=X(blocation,:);                                           % Optimal location
        fbest=best;                                           % The optimization objective function
    elseif best<fbest
        fbest=best;
        xbest=X(blocation,:);
    end
    
    
    %% UPDATE Northern goshawks based on PHASE1 and PHASE2
    
    for i=1:Search_Agents
        %% Phase 1: Exploration
        I=round(1+rand);
        k=randperm(Search_Agents,1);
        P=X(k,:); % Eq. (3)
        F_P=fit(k);
        
        if fit(i)> F_P
            X_new(i,:)=X(i,:)+rand(1,dimensions) .* (P-I.*X(i,:)); % Eq. (4)
        else
            X_new(i,:)=X(i,:)+rand(1,dimensions) .* (X(i,:)-P); % Eq. (4)
        end
        X_new(i,:) = max(X_new(i,:),Lowerbound);X_new(i,:) = min(X_new(i,:),Upperbound);
        
        % update position based on Eq (5)
        L=X_new(i,:);
        fit_new(i)=objective(L);
        if(fit_new(i)<fit(i))
            X(i,:) = X_new(i,:);
            fit(i) = fit_new(i);
        end
        %% END PHASE 1
        
        %% PHASE 2 Exploitation
        R=0.02*(1-t/Max_iterations);% Eq.(6)
        X_new(i,:)= X(i,:)+ (-R+2*R*rand(1,dimensions)).*X(i,:);% Eq.(7)
        
        X_new(i,:) = max(X_new(i,:),Lowerbound);X_new(i,:) = min(X_new(i,:),Upperbound);
        
        % update position based on Eq (8)
        L=X_new(i,:);
        fit_new(i)=objective(L);
        if(fit_new(i)<fit(i))
            X(i,:) = X_new(i,:);
            fit(i) = fit_new(i);
        end
        %% END PHASE 2
        
    end% end for i=1:N
    
    %%
    %% SAVE BEST SCORE
    best_so_far(t)=fbest; % save best solution so far
    average(t) = mean (fit);
    Score=fbest;
    Best_pos=xbest;
    NGO_curve(t)=Score;
end
%%
end
04.代码效果图

04.代码效果图

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