优化求解|【优化求解】基于病毒免疫算法(CHIO)求解最优目标matlab源码优化求解|1024程序员节

1 简介
In this paper, a new nature-inspired human-based optimization algorithm is proposed which called Coronavirus Herd Immunity Optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease social distancing is suggested by health experts. Herd immunity is a state the population reach when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains.

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2 部分代码

%======================================================================= %Coronavirus herd immunity optimizer (CHIO)% All rights reserved. %=======================================================================clear all close all clcPopSize=30; %/* The number of Solutions*/MaxAge = 100; C0 = 1; % number of solutions have corona virusMax_iter=100000; %/*The number of cycles for foraging {a stopping criteria}*/SpreadingRate = 0.05; % Spreading rate parameterruns = 1; %/*Algorithm can be run many times in order to see its robustness*/ObjVal = zeros(1,PopSize); Age = zeros(1,PopSize); BestResults = zeros(runs,1); % saving the best solution at each runfor funNum=7:7% fun#1 to fun#23 if(funNum==1) Function_name='F1'; elseif(funNum==2) Function_name='F2'; elseif(funNum==3) Function_name='F3'; elseif(funNum==4) Function_name='F4'; elseif(funNum==5) Function_name='F5'; elseif(funNum==6) Function_name='F6'; elseif(funNum==7) Function_name='F7'; elseif(funNum==8) Function_name='F8'; elseif(funNum==9) Function_name='F9'; elseif(funNum==10) Function_name='F10'; elseif(funNum==11) Function_name='F11'; elseif(funNum==12) Function_name='F12'; elseif(funNum==13) Function_name='F13'; elseif(funNum==14) Function_name='F14'; elseif(funNum==15) Function_name='F15'; elseif(funNum==16) Function_name='F16'; elseif(funNum==17) Function_name='F17'; elseif(funNum==18) Function_name='F18'; elseif(funNum==19) Function_name='F19'; elseif(funNum==20) Function_name='F20'; elseif(funNum==21) Function_name='F21'; elseif(funNum==22) Function_name='F22'; elseif(funNum==23) Function_name='F23'; end% Load details of the selected benchmark function [lb,ub,dim,fobj]=Get_Functions_details(Function_name); for run = 1:runs % Initializing arrays swarm=zeros(PopSize,dim); % Initialize the population/solutions swarm=initialization(PopSize,dim,ub,lb); for i=1:PopSize, ObjVal(i)=fobj(swarm(i,:)); endFitness=calculateFitness(ObjVal); %% update the status of the swarms (normal, confirmed) %%the minmum C0 Immune rate will take 1 status which means %%infected by corona Status=zeros(1,PopSize); for i=1:C0, Status(fix(rand*(PopSize))+1)=1; end%===================== loop =================================== ticitr=1; % Loop counterwhile itr0) Status(i) = 1; Age(i)=1; end% change the solution from confirmed to recovered if ((Fitness(i) >= mean(Fitness))&& Status(i)==1) Status(i) = 2; Age(i)=0; end% killed the current soluion and regenerated from scratch if(Age(i)>=MaxAge) NewSolConst = initialization(1,dim,ub,lb); swarm(i,:) = NewSolConst(:); Status(i) = 0; end endif(mod(itr,100)==0) display(['Fun#',num2str(funNum),' Run#', num2str(run), ', Itr ', num2str(itr), ' Results ', num2str(min(ObjVal))]); y(itr)=min(ObjVal); enditr=itr+1; endtoc; % Save the best results at each iteration BestResults(run)=min(ObjVal); end % run fprintf(1, '\n\n Done \n\n'); end figure(1) plot(y) xlabel('迭代次数') ylabel('适应度值')

3 仿真结果