镇平网站建设,etc工程承包模式,淮南招投标信息网,门户网站功能#x1f4a5;#x1f4a5;#x1f49e;#x1f49e;欢迎来到本博客❤️❤️#x1f4a5;#x1f4a5; #x1f3c6;博主优势#xff1a;#x1f31e;#x1f31e;#x1f31e;博客内容尽量做到思维缜密#xff0c;逻辑清晰#xff0c;为了方便读者。 ⛳️座右铭欢迎来到本博客❤️❤️ 博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。 ⛳️座右铭行百里者半于九十。 本文目录如下 目录 1 概述 2 运行结果 3 参考文献 4 Matlab代码及文献 1 概述
Harris Hawks Optimizer HHO 是元启发式领域的众多最新算法之一。HHO算法模仿哈里斯鹰的合作行为及其在自然界中的觅食行为称为惊喜突袭。HHO受益于少量的控制参数设置实施的简单性以及高水平的勘探和开发。为了缓解该算法的缺点该文提出一种基于非线性的混沌哈里斯鹰优化NCHHO的改进版本。NCHHO使用混沌和非线性控制参数来提高HHO的优化性能。在所提出的方法中使用混沌地图的主要目的是改善HHO的探索行为。此外本文还引入了一个非线性控制参数来调整HHO的探索和开发行为。所提出的NCHHO算法使用各种混沌图展示了性能的改进这些混沌图是为了识别最有效的混沌图并在几个众所周知的基准函数上进行了测试。本文还考虑解决车联网IoV优化问题该问题展示了NCHHO在解决大规模现实问题方面的适用性。结果表明与其他算法相比NCHHO算法非常具有竞争力并且通常更胜一筹。特别是NCHHO在求解问题维数为D 92和30的单模态和多模态函数时平均提供了50%更好的结果而对于更高维的问题我们提出的算法与其他算法相比在D 100和100的情况下显示出1000%一致的改进。在解决车联网问题时成功率为62.5%与最先进的算法相比这要好得多。为此本文提出的NCHHO算法展示了一种被不同应用广泛使用的有前途的方法这为行业和企业解决日常遇到的优化问题带来了好处例如资源分配信息检索寻找通过网络发送数据的最佳路径路径规划以及许多其他应用。
【改进哈里鹰算法(NCHHO)】是一种使用混沌和非线性控制参数来提高哈里鹰算法优化性能的方法用于解决与车联网相关的路由问题。
NCHHO方法通过引入混沌地图和非线性控制参数来改进HHO算法的性能。其中混沌地图的应用旨在改善HHO算法的搜索行为增强其对解空间的探索能力。此外还引入了非线性控制参数来调节HHO算法的探索性和剥削性行为使其具备更好的适应性和灵活性。
在该研究中NCHHO算法通过采用多个混沌地图并在多个众所周知的基准函数上进行测试展示了性能的改进。通过比较不同混沌地图的效果研究者能够确定最有效的混沌地图并对算法进行优化。此外该方法还考虑了解决车联网IoV优化问题展示了NCHHO在解决大规模现实问题上的适用性和有效性。
这项研究为改进哈里鹰算法提供了一种新的方法通过引入混沌和非线性控制参数显著提高了算法的性能和适应性。通过在车联网路由问题中的应用研究者们展示了NCHHO算法在实际场景下解决问题的能力。这对于提升车联网系统的路由效果和优化性能具有重要意义。 2 运行结果 部分代码
function [Rabbit_Energy,Rabbit_Location,CNVG] NCHHO_IoV(N,T,lb,ub,dim,fobj)% initialize the location and Energy of the rabbit
Rabbit_Locationzeros(1,dim);
Rabbit_Energy0;%Initialize the locations of Harris hawks
Xinitialization(N,dim,ub,lb);CNVGzeros(1,T);t0; % Loop counterwhile tTfor i1:size(X,1)% Check boundriesFUX(i,:)ub;FLX(i,:)lb;X(i,:)(X(i,:).*(~(FUFL)))ub.*FUlb.*FL;% fitness of locationsfitnessfobj(X(i,:));% Update the location of Rabbitif fitnessRabbit_EnergyRabbit_Energyfitness;Rabbit_LocationX(i,:);endendE1abs(2*(1-(t/T))-2); % factor to show the decreaing energy of rabbita1 4; % Initial chaotic map parameter configurationteta 0.7; % Initial chaotic map parameter configuration% Update the location of Harris hawksfor i1:size(X,1)for ii1:4Cm(1,ii) abs((a1/4)*sin(pi*teta));teta Cm(1,ii);endE02*rand()-1; %-1E01Escaping_EnergyE1*(E0); % escaping energy of rabbitif abs(Escaping_Energy)1%% Exploration:% Harris hawks perch randomly based on 2 strategy:qrand();rand_Hawk_index floor(N*rand()1);X_rand X(rand_Hawk_index, :);if q0.5% perch based on other family membersX(i,:)X_rand-Cm(1,1)*abs(X_rand-2*Cm(1,2)*X(i,:));elseif q0.5% perch on a random tall tree (random site inside groups home range)X(i,:)(Rabbit_Location(1,:)-mean(X))-Cm(1,3)*((ub-lb)*Cm(1,4)lb);endelseif 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 hawksrrand(); % probablity of each eventif r0.5 abs(Escaping_Energy)0.5 % Hard besiegeX(i,:)(Rabbit_Location)-Escaping_Energy*abs(Rabbit_Location-X(i,:));endif r0.5 abs(Escaping_Energy)0.5 % Soft besiegeJump_strength2*(1-rand()); % random jump strength of the rabbitX(i,:)(Rabbit_Location-X(i,:))-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:));end%% phase 2: performing team rapid dives (leapfrog movements)if r0.5 abs(Escaping_Energy)0.5 % Soft besiege % rabbit try to escape by many zigzag deceptive motionsw12*exp(-(8*t/T)^2); % Non-linear control ParameterJump_strength2*(1-rand());X1w1*Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:));if fobj(X1)fobj(X(i,:)) % improved move?X(i,:)X1;else % hawks perform levy-based short rapid dives around the rabbitX2w1*Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:))rand(1,dim).*Levy(dim);if (fobj(X2)fobj(X(i,:))) % improved move?X(i,:)X2;endendendif r0.5 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 rabbitw12*exp(-(8*t/T)^2);Jump_strength2*(1-rand());X1w1*Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-mean(X));if fobj(X1)fobj(X(i,:)) % improved move?X(i,:)X1;else % Perform levy-based short rapid dives around the rabbitX2w1*Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-mean(X))rand(1,dim).*Levy(dim);if (fobj(X2)fobj(X(i,:))) % improved move?X(i,:)X2;endendend%%endendtt1;CNVG(t)Rabbit_Energy;
endend
function [Rabbit_Energy,Rabbit_Location,CNVG] NCHHO_IoV(N,T,lb,ub,dim,fobj) % initialize the location and Energy of the rabbit Rabbit_Locationzeros(1,dim); Rabbit_Energy0;
%Initialize the locations of Harris hawks Xinitialization(N,dim,ub,lb);
CNVGzeros(1,T);
t0; % Loop counter
while tT for i1:size(X,1) % Check boundries FUX(i,:)ub;FLX(i,:)lb;X(i,:)(X(i,:).*(~(FUFL)))ub.*FUlb.*FL; % fitness of locations fitnessfobj(X(i,:)); % Update the location of Rabbit if fitnessRabbit_Energy Rabbit_Energyfitness; Rabbit_LocationX(i,:); end end E1abs(2*(1-(t/T))-2); % factor to show the decreaing energy of rabbit a1 4; % Initial chaotic map parameter configuration teta 0.7; % Initial chaotic map parameter configuration % Update the location of Harris hawks for i1:size(X,1) for ii1:4 Cm(1,ii) abs((a1/4)*sin(pi*teta)); teta Cm(1,ii); end E02*rand()-1; %-1E01 Escaping_EnergyE1*(E0); % escaping energy of rabbit if abs(Escaping_Energy)1 %% Exploration: % Harris hawks perch randomly based on 2 strategy: qrand(); rand_Hawk_index floor(N*rand()1); X_rand X(rand_Hawk_index, :); if q0.5 % perch based on other family members X(i,:)X_rand-Cm(1,1)*abs(X_rand-2*Cm(1,2)*X(i,:)); elseif q0.5 % perch on a random tall tree (random site inside groups home range) X(i,:)(Rabbit_Location(1,:)-mean(X))-Cm(1,3)*((ub-lb)*Cm(1,4)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 rrand(); % probablity of each event if r0.5 abs(Escaping_Energy)0.5 % Hard besiege X(i,:)(Rabbit_Location)-Escaping_Energy*abs(Rabbit_Location-X(i,:)); end if r0.5 abs(Escaping_Energy)0.5 % Soft besiege Jump_strength2*(1-rand()); % random jump strength of the rabbit X(i,:)(Rabbit_Location-X(i,:))-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:)); end %% phase 2: performing team rapid dives (leapfrog movements) if r0.5 abs(Escaping_Energy)0.5 % Soft besiege % rabbit try to escape by many zigzag deceptive motions w12*exp(-(8*t/T)^2); % Non-linear control Parameter Jump_strength2*(1-rand()); X1w1*Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:)); if fobj(X1)fobj(X(i,:)) % improved move? X(i,:)X1; else % hawks perform levy-based short rapid dives around the rabbit X2w1*Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:))rand(1,dim).*Levy(dim); if (fobj(X2)fobj(X(i,:))) % improved move? X(i,:)X2; end end end if r0.5 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 w12*exp(-(8*t/T)^2); Jump_strength2*(1-rand()); X1w1*Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-mean(X)); if fobj(X1)fobj(X(i,:)) % improved move? X(i,:)X1; else % Perform levy-based short rapid dives around the rabbit X2w1*Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-mean(X))rand(1,dim).*Levy(dim); if (fobj(X2)fobj(X(i,:))) % improved move? X(i,:)X2; end end end %% end end tt1; CNVG(t)Rabbit_Energy; end
end
3 参考文献 文章中一些内容引自网络会注明出处或引用为参考文献难免有未尽之处如有不妥请随时联系删除。 4 Matlab代码及文献
