【智能优化算法-灰狼算法】基于贪婪非分级灰狼优化器求解单目标优化问题附matlab代码

% E. Akbari, A. Rahimnejad, S. A. Gadsden, "A greedy non-hierarchical grey % wolf optimizer for real-world optimization", Electronics Letters, Apr. 2021 % http://dx.doi.org/10.1049/ell2.12176 clc clear global NFE NFE = 0; nPop = 30; % Number of search agents (Population Number) MaxIt = 1000; % Maximum number of iterations nVar = 30; % Number of Optimization Variables nFun = 1; % Function No, select any integer number from 1 to 14 CostFunction = @(x,nFun) Cost(x,nFun); % Cost Function %% Problem Definition VarMin = -100; % Decision Variables Lower Bound if nFun==7 VarMin = -600; % Decision Variables Lower Bound end if nFun==8 VarMin = -32; % Decision Variables Lower Bound end if nFun==9 VarMin = -5; % Decision Variables Lower Bound end if nFun==10 VarMin = -5; % Decision Variables Lower Bound end if nFun==11 VarMin = -0.5; % Decision Variables Lower Bound end if nFun==12 VarMin = -pi; % Decision Variables Lower Bound end if nFun==14 VarMin = -100; % Decision Variables Lower Bound end VarMax = -VarMin; % Decision Variables Upper Bound if nFun==13 VarMin = -3; % Decision Variables Lower Bound VarMax = 1; % Decision Variables Upper Bound end %% NH-Grey Wold Optimizer (GWO) % Initialize Best Solution (Alpha) which will be used for archiving Alpha_pos = zeros(1,nVar); Alpha_score = inf; %Initialize the positions of search agents Positions = rand(nPop,nVar).*(VarMax-VarMin)+VarMin; Positions1 = rand(nPop,nVar).*(VarMax-VarMin)+VarMin; BestCosts = zeros(1,MaxIt); fitness(1 : nPop)=inf; fitness1 = fitness; iter = 0; % Loop counter %% Main loop while iter<MaxIt for i=1:nPop % Return back the search agents that go beyond the boundaries of the search space Flag4ub = Positions1(i,:)>VarMax; Flag4lb = Positions1(i,:)<VarMin; Positions1(i, : )=(Positions1(i,:).*(~(Flag4ub+Flag4lb)))+VarMax.*Flag4ub+VarMin.*Flag4lb; % Calculate objective function for each search agent fitness1(i) = CostFunction(Positions1(i,:), nFun); % Update Grey Wolves if fitness1(i)<fitness(i) Positions(i, : )=Positions1(i,:); fitness(i) = fitness1(i) ; end % Update Best Solution (Alpha) for archiving if fitness(i)<Alpha_score Alpha_score = fitness(i); Alpha_pos = Positions(i,:); end end a = 2-(iter*((2)/MaxIt)); % a decreases linearly fron 2 to 0 % Update the Position of all search agents for i=1:nPop for j=1:nVar GGG = randperm(nPop-1,3); ind1 = GGG>=i; GGG(ind1) = GGG(ind1)+1; m1 = GGG(1); m2 = GGG(2); m3 = GGG(3); r1 = rand; r2 = rand; A1 = 2*a*r1-a; C1 = 2*r2; D_alpha = abs(C1*Positions(m1,j)-Positions(i,j)); X1 = Positions(m1,j)-A1*D_alpha; r1 = rand; r2 = rand; A2 = 2*a*r1-a; C2 = 2*r2; D_beta = abs(C2*Positions(m2,j)-Positions(i,j)); X2 = Positions(m2,j)-A2*D_beta; r1 = rand; r2 = rand; A3 = 2*a*r1-a; C3 = 2*r2; D_delta = abs(C3*Positions(m3,j)-Positions(i,j)); X3 = Positions(m3,j)-A3*D_delta; Positions1(i,j) = (X1+X2+X3)/3; end end iter = iter+1; BestCosts(iter) = Alpha_score; fprintf(’Iter = %g, NFE= %g, Best Cost = %g ’,iter,NFE,Alpha_score); end figure plot(BestCosts) xlabel(’迭代次数’) ylabel(’适应度值’)