【智能优化算法】野狗智能优化算法(Dingo Optimization Algorithm DOA)

野狗智能优化算法(Dingo Optimization Algorithm DOA)是期刊“MATHEMATICAL PROBLEMS IN ENGINEERING”的2021年智能优化算法

01.引言

野狗智能优化算法(Dingo Optimization Algorithm DOA)该算法的灵感来自野狗的狩猎策略，即迫害攻击，分组策略和清除行为。为了提高该方法的整体效率和性能，在DOA中制定了与四个规则相关联的三种搜索策略。这些策略和规则在搜索空间的强化（开发）和多样化（探索）之间提供了一个很好的平衡。

02.优化算法的流程

3.论文中算法对比图

04.部分代码

function [vMin,theBestVct,Convergence_curve]=DOA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)
P= 0.5;  % Hunting or Scavenger?  rate.  See section 3.0.4, P and Q parameters analysis
Q= 0.7;  % Group attack or persecution? 
beta1= -2 + 4* rand();  % -2 < beta < 2     Used in Eq. 2, 
beta2= -1 + 2* rand();  % -1 < beta2 < 1    Used in Eq. 2,3, and 4
naIni= 2; % minimum number of dingoes that will attack
naEnd= SearchAgents_no /naIni; % maximum number of dingoes that will attack
na= round(naIni + (naEnd-naIni) * rand()); % number of dingoes that will attack, used in Attack.m Section 2.2.1: Group attack
Positions=initialization(SearchAgents_no,dim,ub,lb);for i=1:size(Positions,1)Fitness(i)=fobj(Positions(i,:)); % get fitness     end
[vMin minIdx]= min(Fitness);  % the min fitness value vMin and the position minIdx
theBestVct= Positions(minIdx,:);  % the best vector
[vMax maxIdx]= max(Fitness); % the max fitness value vMax and the position maxIdx
Convergence_curve=zeros(1,Max_iter);
Convergence_curve(1)= vMin;
survival= survival_rate(Fitness,vMin,vMax);  % Section 2.2.4 Dingoes'survival rates
t=0;% Loop counter
% Main loop
for t=1:Max_iter       for r=1:SearchAgents_nosumatory=0;if rand() < P  % If Hunting?sumatory= Attack( SearchAgents_no, na, Positions, r );     % Section 2.2.1, Strategy 1: Part of Eq.2   if rand() < Q  % If group attack?                v(r,:)=  beta1 * sumatory-theBestVct; % Strategy 1: Eq.2else  %  Persecutionr1= round(1+ (SearchAgents_no-1)* rand()); % v(r,:)= theBestVct + beta1*(exp(beta2))*((Positions(r1,:)-Positions(r,:))); % Section 2.2.2, Strategy 2:  Eq.3end  else % Scavengerr1= round(1+ (SearchAgents_no-1)* rand());v(r,:)=   (exp(beta2)* Positions(r1,:)-((-1)^getBinary)*Positions(r,:))/2; % Section 2.2.3, Strategy 3: Eq.4endif survival(r) <= 0.3  % Section 2.2.4, Algorithm 3 - Survival procedureband=1; while band r1= round(1+ (SearchAgents_no-1)* rand());r2= round(1+ (SearchAgents_no-1)* rand());if r1 ~= r2 band=0;endendv(r,:)=   theBestVct + (Positions(r1,:)-((-1)^getBinary)*Positions(r2,:))/2;  % Section 2.2.4, Strategy 4: Eq.6end % Return back the search agents that go beyond the boundaries of the search space .  Flag4ub=v(r,:)>ub;Flag4lb=v(r,:)<lb;v(r,:)=(v(r,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;% Evaluate new solutionsFnew= fobj(v(r,:));% Update if the solution improvesif Fnew <= Fitness(r)Positions(r,:)= v(r,:);Fitness(r)= Fnew;endif Fnew <= vMintheBestVct= v(r,:);vMin= Fnew;end endConvergence_curve(t+1)= vMin; [vMax maxIdx]= max(Fitness);survival= survival_rate( Fitness, vMin, vMax); % Section 2.2.4 Dingoes'survival ratesend
%_____________________________________________________End DOA Algorithm]%