能量谷优化器(EVO)是一种新的元启发式算法，它的算法是受到了关于稳定性和不同粒子衰变模式的先进物理原理的启发。在文献中，作者与CEC函数中最先进的算法进行了比较，并且证明该算法确实很强劲。算法原理大家请参考文献。

[1] Azizi M ,  Aickelin U ,  Khorshidi H A , et al. Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization[J]. Scientific Reports.

evo算法与其他算法的对比结果图：

 更多对比结果与数据请参考本文献，文献与代码作者打包在一起了，请大家查收。

直接上结果图：

算法核心代码：

function [Best_score,Best_Pos,Conv_History]=EVO(nParticles,MaxFes,lb,ub,VarNumber,fobj)

%% Problem Information
CostFunction = fobj;          % @ Cost Function
VarMin = lb *ones(1,VarNumber);        % Lower bound of variable;
VarMax = ub *ones(1,VarNumber);         % Upper bound of variable;

%% Counters
Iter=0;   % Iterations
FEs=0;    % Function Evaluations

%% Initialization
Particles=[]; NELs=[];
for i=1:nParticles
    Particles(i,:)=unifrnd(VarMin,VarMax,[1 VarNumber]);
    NELs(i,1)=CostFunction(Particles(i,:));
    FEs=FEs+1;
end

% Sort Particles
[NELs, SortOrder]=sort(NELs);
Particles=Particles(SortOrder,:);
BS=Particles(1,:); 
BS_NEL=NELs(1);
WS_NEL=NELs(end);

%% Main Loop
while FEs<MaxFes
    Iter=Iter+1;
    NewParticles=[];
    NewNELs=[];   
   for i=1:nParticles
       Dist=[];
       for j=1:nParticles
           Dist(j,1)=distance(Particles(i,:), Particles(j,:));
       end
       [ ~, a]=sort(Dist);
       CnPtIndex=randi(nParticles);
       if CnPtIndex<3
           CnPtIndex=CnPtIndex+2;
       end
       CnPtA=Particles(a(2:CnPtIndex),:);
       CnPtB=NELs(a(2:CnPtIndex),:);
       X_NG=mean(CnPtA);
       X_CP=mean(Particles);
       EB=mean(NELs);            
       SL=(NELs(i)-BS_NEL)/(WS_NEL-BS_NEL); SB=rand;
       if NELs(i)>EB   
           if SB>SL         
               AlphaIndex1=randi(VarNumber);
               AlphaIndex2=randi([1 VarNumber], AlphaIndex1 , 1);
               NewParticle(1,:)=Particles(i,:);
               NewParticle(1,AlphaIndex2)=BS(AlphaIndex2);               
               GamaIndex1=randi(VarNumber);
               GamaIndex2=randi([1 VarNumber], GamaIndex1 , 1);
               NewParticle(2,:)=Particles(i,:);
               NewParticle(2,GamaIndex2)=X_NG(GamaIndex2);           
               NewParticle = max(NewParticle,VarMin);
               NewParticle = min(NewParticle,VarMax);  
               NewNEL(1,1)=CostFunction(NewParticle(1,:));
               NewNEL(2,1)=CostFunction(NewParticle(2,:));               
               FEs=FEs+2;    
           else               
               Ir=unifrnd(0,1,1,2); Jr=unifrnd(0,1,1,VarNumber);
               NewParticle(1,:)=Particles(i,:)+(Jr.*(Ir(1)*BS-Ir(2)*X_CP)/SL);
               Ir=unifrnd(0,1,1,2); Jr=unifrnd(0,1,1,VarNumber);
               NewParticle(2,:)=Particles(i,:)+(Jr.*(Ir(1)*BS-Ir(2)*X_NG));  
               NewParticle = max(NewParticle,VarMin);
               NewParticle = min(NewParticle,VarMax);
               NewNEL(1,1)=CostFunction(NewParticle(1,:));
               NewNEL(2,1)=CostFunction(NewParticle(2,:)); 
               FEs=FEs+2;   
           end    
       else 
           NewParticle(1,:)=Particles(i,:)+randn*SL*unifrnd(VarMin,VarMax,[1 VarNumber]);         
           NewParticle = max(NewParticle,VarMin);
           NewParticle = min(NewParticle,VarMax);
           NewNEL(1,1)=CostFunction(NewParticle(1,:));   
           FEs=FEs+1;
       end
   NewParticles=[NewParticles ; NewParticle];    
   NewNELs=[NewNELs ; NewNEL];
   end
   NewParticles=[NewParticles ; Particles];    
   NewNELs=[NewNELs ; NELs]; 
   
   % Sort Particles
   [NewNELs, SortOrder]=sort(NewNELs);
   NewParticles=NewParticles(SortOrder,:);
   BS=NewParticles(1,:); 
   BS_NEL=NewNELs(1); 
   WS_NEL=NewNELs(end);
   Particles=NewParticles(1:nParticles,:);
   NELs=NewNELs(1:nParticles,:);

   % Store Best Cost Ever Found
   BestCosts(Iter)=BS_NEL;
   
   % Show Iteration Information
   disp(['Iteration ' num2str(Iter) ': Best Cost = ' num2str(BestCosts(Iter))]);
end

Eval_Number=FEs;
Conv_History=BestCosts;
Best_Pos=BS;
Best_score=BestCosts(end);
end
%% Calculate the Euclidean Distance
function o = distance(a,b)
for i=1:size(a,1)
    o(1,i)=sqrt((a(i)-b(i))^2);
end
end

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