基于Levy飞行和FDB的郊狼优化算法附matlab代码.zip

function [] = lrfdb_coa() rand('state', sum(100*clock)); [n_coy, n_packs, D, nfevalMAX, lb, ub] = problem_terminate(); lu=[lb;ub]; VarMin = lu(1,:); VarMax = lu(2,:); p_leave = 0.005*n_coy^2; Ps = 1/D; pop_total = n_packs*n_coy; costs = zeros(pop_total,1); coyotes = repmat(VarMin,pop_total,1) + rand(pop_total,D).*(repmat(VarMax,pop_total,1) - repmat(VarMin,pop_total,1)); ages = zeros(pop_total,1); packs = reshape(randperm(pop_total),n_packs,[]); coypack = repmat(n_coy,n_packs,1); for c=1:pop_total costs(c,1) = problem(coyotes(c,:)); end nfeval = 0; [GlobalMin,ibest] = min(costs); GlobalParams = coyotes(ibest,:); year=0; while nfeval<nfevalMAX % Stopping criteria %% Update the years counter year = year + 1; %% Execute the operations inside each pack for p=1:n_packs % Get the coyotes that belong to each pack coyotes_aux = coyotes(packs(p,:),:); costs_aux = costs(packs(p,:),:); ages_aux = ages(packs(p,:),1); n_coy_aux = coypack(p,1); % Detect alphas according to the costs (Eq. 5) [costs_aux,inds] = sort(costs_aux,'ascend'); coyotes_aux = coyotes_aux(inds,:); ages_aux = ages_aux(inds,:); c_alpha = coyotes_aux(1,:); % Compute the social tendency of the pack (Eq. 6) tendency = coyotes(rouletteFitnessDistanceBalance( coyotes, costs ), :); % Update coyotes' social condition new_coyotes = zeros(n_coy_aux,D); for c=1:n_coy_aux rc1 = c; while rc1==c rc1 = randi(n_coy_aux); end rc2 = c; while rc2==c || rc2 == rc1 rc2 = randi(n_coy_aux); end % Try to update the social condition according to the alpha and % the pack tendency (Eq. 12) new_c = coyotes_aux(c,:) + rand*(c_alpha - coyotes_aux(rc1,:))+ ... rand*(tendency - coyotes_aux(rc2,:)); % Keep the coyotes in the search space (optimization problem % constraint) new_coyotes(c,:) = min(max(new_c,VarMin),VarMax); % Evaluate the new social condition (Eq. 13) new_cost = problem(new_coyotes(c,:)); nfeval = nfeval+1; % Adaptation (Eq. 14) if new_cost < costs_aux(c,1) costs_aux(c,1) = new_cost; coyotes_aux(c,:) = new_coyotes(c,:); end end %% Birth of a new coyote from random parents (Eq. 7 and Alg. 1) parents = randperm(n_coy_aux,2); prob1 = (1-Ps)/2; prob2 = prob1; pdr = randperm(D); p1 = zeros(1,D); p2 = zeros(1,D); p1(pdr(1)) = 1; % Guarantee 1 charac. per individual p2(pdr(2)) = 1; % Guarantee 1 charac. per individual L = levy(D); r = L(1:D-2); p1(pdr(3:end)) = r < prob1; p2(pdr(3:end)) = r > 1-prob2; % Eventual noise n = ~(p1|p2); % Generate the pup considering intrinsic and extrinsic influence pup = p1.*coyotes_aux(parents(1),:) + ... p2.*coyotes_aux(parents(2),:) + ... n.*(VarMin + rand(1,D).*(VarMax-VarMin)); % Verify if the pup will survive pup_cost = problem(pup); nfeval = nfeval + 1; worst = find(pup_cost<costs_aux==1); if ~isempty(worst) [~,older] = sort(ages_aux(worst),'descend'); which = worst(older); coyotes_aux(which(1),:) = pup; costs_aux(which(1),1) = pup_cost; ages_aux(which(1),1) = 0; end %% Update the pack information coyotes(packs(p,:),:) = coyotes_aux; costs(packs(p,:),:) = costs_aux; ages(packs(p,:),1) = ages_aux; end %% A coyote can leave a pack and enter in another pack (Eq. 4) if n_packs>1 if rand < p_leave rp = randperm(n_packs,2); rc = [randperm(coypack(rp(1),1),1) ... randperm(coypack(rp(2),1),1)]; aux = packs(rp(1),rc(1)); packs(rp(1),rc(1)) = packs(rp(2),rc(2)); packs(rp(2),rc(2)) = aux; end end %% Update coyotes ages ages = ages + 1; %% Output variables (best alpha coyote among all alphas) [GlobalMin,ibest] = min(costs); GlobalParams = coyotes(ibest,:); end fprintf('Best Fitness: %d\n', GlobalMin); disp('Best Solution:'); disp(GlobalParams); end function [ L ] = levy( D ) beta=3/2; sigma=(gamma(1+beta)*sin(pi*beta/2)/(gamma((1+beta)/2)*beta*2^((beta-1)/2)))^(1/beta); u=randn(1,D)*sigma; v=randn(1); step=u./abs(v).^(1/beta); L=100*abs(0.01*step); end