matlab-基于matlab的黑洞优化算法仿真-源码

function [blackHole, bestSolution, blackHoleCost, allBestCost] = BH_Func(blackHole) locationOfStars = repmat(blackHole.varMin ,blackHole.numOfStars,1)... + repmat(blackHole.varMax - blackHole.varMin ,blackHole.numOfStars,1).*rand(blackHole.numOfStars,blackHole.nVar); allBestCost = zeros(blackHole.maxIter,1); for iter = 1:blackHole.maxIter % Evaluation starsCost = ObjectiveFunc(locationOfStars); % Selection the Best Star that has the best fitness value as the Black Hole [blackHoleCost, BH_ind] = max(starsCost); % Change the location of each star and compute new fitness values locationOfStars = locationOfStars + (repmat(locationOfStars(BH_ind),blackHole.numOfStars,blackHole.nVar) - locationOfStars)... .*unifrnd(0,1,blackHole.numOfStars,blackHole.nVar); starsCost = ObjectiveFunc(locationOfStars); % If there is a star reaches a location with lower cost than the black hole, exchange their locations if blackHoleCost < max(starsCost) [blackHoleCost, BH_ind] = max(starsCost); end % store the best cost value allBestCost(iter) = blackHoleCost; if iter<blackHole.maxIter % Compute the Radius of the Event Horizon of the Black Hole R = blackHoleCost / sum(starsCost); distances = (sqrt(sum(locationOfStars-repmat(locationOfStars(BH_ind),blackHole.numOfStars,blackHole.nVar),2).^2))'; crosserStarsInd = find(distances<R); if length(crosserStarsInd)>0 for i=1:length(crosserStarsInd) locationOfStars(crosserStarsInd(i),:) = unifrnd(0,1,blackHole.nVar,1); end end end end bestSolution = locationOfStars(BH_ind,:); disp(['BH_index is' num2str(BH_ind)]); end