【改进哈里鹰算法(NCHHO)】使用混沌和非线性控制参数来提高哈里鹰算法的优化性能，解决车联网相关的路由问题（Matlab代码实现）

function [Rabbit_Energy,Rabbit_Location, Div, Xpl, Xpt,CNVG]=LMHHO(N,T,lb,ub,dim,fobj)% initialize the location and Energy of the rabbitRabbit_Location=zeros(1,dim);Rabbit_Energy=inf;%Initialize the locations of Harris' hawksX=initialization(N,dim,ub,lb);Div = [];CNVG=zeros(1,T);MemoryLimit=10;gBestCollPos=zeros(MemoryLimit,dim);gBestCollCost=1.0e+100*ones(MemoryLimit,1);gBestCollProb=zeros(MemoryLimit,1);gBestCollCount=0;t=0; % Loop counterwhile t<T    for i=1:size(X,1)        % Check boundries        FU=X(i,:)>ub;FL=X(i,:)<lb;X(i,:)=(X(i,:).*(~(FU+FL)))+ub.*FU+lb.*FL;        % fitness of locations        fitness=fobj(X(i,:));        % Update the location of Rabbit        if fitness<Rabbit_Energy            Rabbit_Energy=fitness;            Rabbit_Location=X(i,:);            if gBestCollCount<=MemoryLimit && gBestCollCount>0                gBestCollPos(gBestCollCount,:)=X(i,:);                gBestCollCost(gBestCollCount)=fitness;            else                gBestCollCount=1;                gBestCollPos(gBestCollCount,:)=X(i,:);                gBestCollCost(gBestCollCount)=fitness;            end                    end    end    F=zeros(MemoryLimit,1);    MeanCost = mean(gBestCollCost);    for j=1:MemoryLimit        F(j) = exp(-gBestCollCost(j)/MeanCost);    end    gBestCollProb(:)=F/sum(F);    if range(gBestCollCost)~=0        pr=RouletteWheelSelection(gBestCollProb);        % Choose k randomly, not equal to i        K=[1:pr-1 pr+1:MemoryLimit];        k=K(randi([1 numel(K)]));        gbestX=gBestCollPos(k,:);    else        gbestX=Rabbit_Location;    end        E1=2*(1-(t/T)); % factor to show the decreaing energy of rabbit    % Update the location of Harris' hawks    for i=1:size(X,1)        E0=2*rand()-1; %-1<E0<1        Escaping_Energy=E1*(E0);  % escaping energy of rabbit                if abs(Escaping_Energy)>=1            %% Exploration:            % Harris' hawks perch randomly based on 2 strategy:                        q=rand();            rand_Hawk_index = floor(N*rand()+1);            X_rand = X(rand_Hawk_index, :);            if q<0.5                % perch based on other family members                X(i,:)=X_rand-rand()*abs(X_rand-2*rand()*X(i,:));            elseif q>=0.5                % perch on a random tall tree (random site inside group's home range)                X(i,:)=(gbestX-mean(X))-rand()*((ub-lb)*rand+lb);            end                    elseif abs(Escaping_Energy)<1            %% Exploitation:            % Attacking the rabbit using 4 strategies regarding the behavior of the rabbit                        %% phase 1: surprise pounce (seven kills)            % surprise pounce (seven kills): multiple, short rapid dives by different hawks                        r=rand(); % probablity of each event                        if r>=0.5 && abs(Escaping_Energy)<0.5 % Hard besiege                X(i,:)=(gbestX)-Escaping_Energy*abs(gbestX-X(i,:));            end                        if r>=0.5 && abs(Escaping_Energy)>=0.5  % Soft besiege                Jump_strength=2*(1-rand()); % random jump strength of the rabbit                X(i,:)=(gbestX-X(i,:))-Escaping_Energy*abs(Jump_strength*gbestX-X(i,:));            end                        %% phase 2: performing team rapid dives (leapfrog movements)            if r<0.5 && abs(Escaping_Energy)>=0.5, % Soft besiege % rabbit try to escape by many zigzag deceptive motions                                Jump_strength=2*(1-rand());                X1=gbestX-Escaping_Energy*abs(Jump_strength*gbestX-X(i,:));                                if fobj(X1)<fobj(X(i,:)) % improved move?                    X(i,:)=X1;                else % hawks perform levy-based short rapid dives around the rabbit                    X2=gbestX-Escaping_Energy*abs(Jump_strength*gbestX-X(i,:))+rand(1,dim).*Levy(dim);                    if (fobj(X2)<fobj(X(i,:))), % improved move?                        X(i,:)=X2;                    end                end            end                        if r<0.5 && abs(Escaping_Energy)<0.5, % Hard besiege % rabbit try to escape by many zigzag deceptive motions                % hawks try to decrease their average location with the rabbit                Jump_strength=2*(1-rand());                X1=gbestX-Escaping_Energy*abs(Jump_strength*gbestX-mean(X));                                if fobj(X1)<fobj(X(i,:)) % improved move?                    X(i,:)=X1;                else % Perform levy-based short rapid dives around the rabbit                    X2=gbestX-Escaping_Energy*abs(Jump_strength*gbestX-mean(X))+rand(1,dim).*Levy(dim);                    if (fobj(X2)<fobj(X(i,:))), % improved move?                        X(i,:)=X2;                    end                end            end            %%        end    end    t=t+1;    CNVG(t)=Rabbit_Energy;%    Print the progress every 100 iterations%    if mod(t,100)==0    % display(['Iteration: ', num2str(t), ', fitness: ', num2str(Rabbit_Energy)]);%    end        % Normalize particle position values    pTemp=X;            pTemp2=X;    for j=1:dim        for i=1:N            pTemp2(i,j)=pTemp(i,j)+abs(min(pTemp(:,j)));        end              if max(pTemp2(:,j))>0            pTemp2(:,j)=pTemp2(:,j)/max(pTemp2(:,j));                  else            pTemp2(:,j)=0;        end               end    for i=1:N        % Population diversity of swarm individuals        med=0;        for j=1:dim            med= med + abs(median(pTemp2(:,j))-pTemp2(i,j));        end    end            % Population diversity as a whole    temp=[];    for j=1:dim        temp(j)=mean(abs(pTemp2(:,j)-mean(pTemp2(:,j))));    end    Div(t)= sum(temp)/N;end        Xpl = sum((Div/max(Div))*100);    Xpt =sum((abs((Div-max(Div)))/max(Div))*100);end% ___________________________________function o=Levy(d)beta=1.5;sigma=(gamma(1+beta)*sin(pi*beta/2)/(gamma((1+beta)/2)*beta*2^((beta-1)/2)))^(1/beta);u=randn(1,d)*sigma;v=randn(1,d);step=u./abs(v).^(1/beta);o=step;end