matlab語言ALO演算法

clear all 
clc
%%%%%%%%%%%
SearchAgents_no=200; % 設定個數
Ts1 = [ 3.0448 1];%Ts1低通濾波器初始引數
Ts2 = [ 13.94501];%Ts2低通濾波器初始引數
Usc =48*6; %超級電容單體個數
Rsc = 7*6.3e-3;%超級電容內阻
F = 165/(Usc/48);%超級電容容量
Ahbat=30;%電池容量


Max_iteration=50;  % 智能尋優演算法

lb = [1,10,300,40];%優化引數上界
ub = [5,15,500,60];%優化引數下界
dim = 4;%優化引數個數
fobj = @costfunction
[Best_score,Best_pos,cg_curve]=ALO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)

figure('Position',[269   240   660   290])

semilogy(WOA_cg_curve,'Color','r')
title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');
plot(WOA_cg_curve,'LineWidth',2,'Color','r')
title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');

axis tight
grid on
box on 
function [Elite_antlion_fitness,Elite_antlion_position,Convergence_curve]=ALO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

% Initialize the positions of antlions and ants
antlion_position=initialization(SearchAgents_no,dim,ub,lb);
ant_position=initialization(SearchAgents_no,dim,ub,lb);

% Initialize variables to save the position of elite, sorted antlions, 
% convergence curve, antlions fitness, and ants fitness
Sorted_antlions=zeros(SearchAgents_no,dim);
Elite_antlion_position=zeros(1,dim);
Elite_antlion_fitness=inf;
Convergence_curve=zeros(1,Max_iter);
antlions_fitness=zeros(1,SearchAgents_no);
ants_fitness=zeros(1,SearchAgents_no);

% Calculate the fitness of initial antlions and sort them
for i=1:size(antlion_position,1)
    antlions_fitness(1,i)=fobj(antlion_position(i,:)); 
end

[sorted_antlion_fitness,sorted_indexes]=sort(antlions_fitness);
    
for newindex=1:SearchAgents_no
     Sorted_antlions(newindex,:)=antlion_position(sorted_indexes(newindex),:);
end
    
Elite_antlion_position=Sorted_antlions(1,:);
Elite_antlion_fitness=sorted_antlion_fitness(1);

% Main loop start from the second iteration since the first iteration 
% was dedicated to calculating the fitness of antlions
Current_iter=2; 
while Current_iter<Max_iter+1
    
    % This for loop simulate random walks
    for i=1:size(ant_position,1)
        % Select ant lions based on their fitness (the better anlion the higher chance of catching ant)
        Rolette_index=RouletteWheelSelection(1./sorted_antlion_fitness);
        if Rolette_index==-1  
            Rolette_index=1;
        end
      
        % RA is the random walk around the selected antlion by rolette wheel
        RA=Random_walk_around_antlion(dim,Max_iter,lb,ub, Sorted_antlions(Rolette_index,:),Current_iter);
        
        % RA is the random walk around the elite (best antlion so far)
        [RE]=Random_walk_around_antlion(dim,Max_iter,lb,ub, Elite_antlion_position(1,:),Current_iter);
        
        ant_position(i,:)= (RA(Current_iter,:)+RE(Current_iter,:))/2; % Equation (2.13) in the paper          
    end
    
    for i=1:size(ant_position,1)  
        
        % Boundar checking (bring back the antlions of ants inside search
        % space if they go beyoud the boundaries
        Flag4ub=ant_position(i,:)>ub;
        Flag4lb=ant_position(i,:)<lb;
        ant_position(i,:)=(ant_position(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;  
        
        ants_fitness(1,i)=fobj(ant_position(i,:));    
        All_fitness(1,i)=ants_fitness(1,i);
       
    end
    
    % Update antlion positions and fitnesses based of the ants (if an ant 
    % becomes fitter than an antlion we assume it was cought by the antlion  
    % and the antlion update goes to its position to build the trap)
    double_population=[Sorted_antlions;ant_position];
    double_fitness=[sorted_antlion_fitness ants_fitness];
        
    [double_fitness_sorted I]=sort(double_fitness);
    double_sorted_population=double_population(I,:);
        
    antlions_fitness=double_fitness_sorted(1:SearchAgents_no);
    Sorted_antlions=double_sorted_population(1:SearchAgents_no,:);
       % Calculate objective function for each search agent
        a1=0.5;%電池損傷所占權重
        a2=0.2;%電網波動所占權重
        a3=0.3;%經濟性所占權重
        X =  ant_position(i,:);
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        Rsc = (X(3)/48)*6e-3;  %計算超級電容電阻
        Rbat = 0.4/(X(4)/10);%計算電池電阻
        Ts1=X(1);
        Ts2=X(2);
        F=165/(X(3)/48);
        Usc=X(3);
        Ahbat=X(4);
        %%%%%%%%%%%%%%%%設定模型引數%%%%%%%%%%%%%%%%%%%%
        set_param('energy_soure_designV2/Battery1','NomQ',num2str(Ahbat))
        set_param('energy_soure_designV2/Battery1','R',num2str(Rbat))
        
        set_param('energy_soure_designV2/Saturation1','UpperLimit',num2str(Ahbat*560))
        set_param('energy_soure_designV2/Saturation1','LowerLimit',num2str(-Ahbat*560))
        set_param('energy_soure_designV2/Constant','Value',num2str(Usc*0.85))
        
        set_param('energy_soure_designV2/Supercapacitor1','Voltage',num2str(Usc))
        set_param('energy_soure_designV2/Supercapacitor1','Vinit',num2str(Usc*0.85))
        set_param('energy_soure_designV2/Supercapacitor1','C',num2str(F))
        set_param('energy_soure_designV2/Supercapacitor1','Rdc',num2str(Rsc))
        set_param('energy_soure_designV2/Constant6','Value',num2str(Ts1))
        set_param('energy_soure_designV2/Constant8','Value',num2str(Ts2))
        %set_param('energy_soure_designV2/ts1','Denominator',num2str([Ts1 1]))
        %set_param('energy_soure_designV2/ts2','Denominator',num2str([Ts2 1]))
        %運行模型
        sim('energy_soure_designV2');
        %資料處理——船舶機動航行模型處理
        Ibat(1,:)=[];
        I= Ibat(:,2);
        Vbat(1,:)=[];
        Vsc_d=Vsc;
        I1=mapminmax(Ibat'); %已分類資料的標準化
        Ibat=I1';
        V=mapminmax(Vbat'); %待分類資料的標準化
        Vbat=V';
        %超級電容電壓過低懲罰函式
        Phess=0;
        Vuc_min=X(3)*0.6;
        Vuc_max=X(3)*1.05;
        for a=1:1800
            if (Vsc_d(a,2)< Vuc_min||Vsc_d(a,2)>Vuc_max)
                Phess =Phess+1;
            end
        end
        %計算電池電流波動
        Ibat1=Ibat;
        Ibat(end,:)=[];
        Ibat1(1,:)=[];
        Cibat=(13.9/X(4))*sum(abs(Ibat(:,2)-Ibat1(:,2)));
        %計算電池偏移波動
        CVbus=(0.000005)*sum(abs(Vbat(:,2)-610));
        %計算電池電壓波動
        Vbat1=Vbat;
        Vbat(end,:)=[];
        Vbat1(1,:)=[];
        Cvbat=(13.9/X(4))*sum(abs(Vbat(:,2)-Vbat1(:,2)));
        %計算經濟性
        Csc=sum(0.2*Rsc*Isc(:,2).^2)/1000;      %超級電容內阻消耗的能量
        Cbat=sum(0.1*Rbat*I.^2)/1000;   %電池組內阻消耗的能量
        CE=(Csc+Cbat)/36; 
        cost = 0.5*(X(3)/48) + 0.25*X(4); %（超級電容） （電池/DC變換器） 價格
        %計算綜合費用
        C=(a1*Cibat+a2*(Cvbat+CVbus)+a3*CE+0.3*cost+Phess);
        fitness = C;       
    % Update the position of elite if any antlinons becomes fitter than it
    if antlions_fitness(1)<Elite_antlion_fitness 
        Elite_antlion_position=Sorted_antlions(1,:);
        Elite_antlion_fitness=antlions_fitness(1);
    end
      
    % Keep the elite in the population
    Sorted_antlions(1,:)=Elite_antlion_position;
    antlions_fitness(1)=Elite_antlion_fitness;
  
    % Update the convergence curve
    Convergence_curve(Current_iter)=Elite_antlion_fitness;

    
    
    if Current_iter>2
        line([Current_iter-1 Current_iter], [Convergence_curve(Current_iter-1) Convergence_curve(Current_iter)],'Color',[0    0.4470    0.7410])
        xlabel('Iteration');
        ylabel('Best score obtained so far');        
        drawnow
    end
 
    results = get(handles.uitable1,'data');
    results{2,1}=Current_iter;
    results{2,2}=Elite_antlion_fitness;
    set(handles.uitable1,'data',results);
  
    Current_iter=Current_iter+1; 
    end
end

出現以下問題：
fobj =

  包含以下值的 function_handle:

    @costfunction

錯誤使用 costfunction
輸入引數太多。

出錯 ALO_new>ALO (line 52)
    antlions_fitness(1,i)=fobj(antlion_position(i,:));

出錯 ALO_new (line 19)
[Best_score,Best_pos,cg_curve]=ALO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)
請大佬幫忙看看，我應該去怎么解決呢？

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