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🔥 内容介绍
在当今信息技术高速发展的时代，车联网已经成为了现实生活中不可或缺的一部分。随着车辆之间和车辆与基础设施之间的通信需求不断增加，如何优化车联网的路由问题成为了研究的热点之一。本文将介绍混沌和非线性控制哈里鹰算法(NCHHO)在优化车联网相关路由问题中的应用。
车联网是指通过无线通信技术将车辆、道路和基础设施连接起来，形成一个智能化的交通系统。在车联网中，车辆之间可以通过通信网络进行信息交换，实现车辆之间的协同和智能交通管理。而路由问题则是指在车联网中确定车辆之间传输数据的最优路径，以确保数据的高效传输和低延迟。
传统的路由算法主要基于线性控制理论和传统优化算法，但这些算法在处理复杂的非线性系统和大规模网络时存在一定的局限性。而混沌和非线性控制哈里鹰算法(NCHHO)则是一种基于混沌和非线性控制理论的新型优化算法，具有较强的全局搜索能力和适应性。
NCHHO算法的基本思想是将混沌搜索和非线性控制相结合，通过模拟哈里鹰的觅食行为来寻找最优解。混沌搜索的特点是具有随机性和无序性，可以在搜索空间中进行全局搜索；而非线性控制则可以对搜索过程进行调节和优化，提高搜索效率和收敛速度。
在优化车联网路由问题中，NCHHO算法可以通过建立适当的目标函数来寻找最优路径。目标函数可以包括数据传输的时延、能耗、网络拥塞程度等指标，通过调整这些指标的权重可以得到不同的路由策略。NCHHO算法通过不断迭代和优化，可以在较短的时间内找到最优解，提高车联网的通信效率和性能。
除了优化路由问题，NCHHO算法还可以应用于车联网中其他相关的问题，如车辆调度、资源分配等。通过将NCHHO算法与其他智能算法相结合，可以进一步提高车联网的整体性能和效果。
总之，混沌和非线性控制哈里鹰算法(NCHHO)是一种应用于优化车联网相关路由问题的新型算法。通过结合混沌搜索和非线性控制理论，NCHHO算法可以在较短的时间内找到最优解，提高车联网的通信效率和性能。未来，我们可以进一步研究和改进NCHHO算法，以应对车联网中更加复杂和实际的问题，推动车联网技术的发展和应用。
📣 部分代码
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
⛳️ 运行结果
🔗 参考文献
[1] Dehkordi A A , Sadiq A S , Mirjalili S ,et al.Nonlinear-based Chaotic Harris Hawks Optimizer: Algorithm and Internet of Vehicles application[J].Applied Soft Computing, 2021, 109(2):107574.DOI:10.1016/j.asoc.2021.107574.
