Artificial Bee Colony Algorithm Based on Multi-dimensional Greedy Search
Zhang Suq · Jisuanji gongcheng · 2014
Artificial Bee Colony(ABC)algorithm can be efficiently employed to solve the multimodal and high dimensional function optimization problem. However,low search speed and premature convergence frequently appear with more complex problem. In order to improve the algorithm performance,this paper proposes a new artifciall bee colony algorithm. It introduces a search equation based on multi-dimensional greedy search to enhance local search and avoid the solution to be abandoned which achieves optimum value in some dimensions but reach the maximum update limit. New algorithm also adds a disturbance mechanism to avoid obtaining partial optimal solutions when premature convergence in a few dimensions. Experimental results show the new algorithm can balance the exploitation and exploration,has more fast convergence speed and better computational precision in solving the multimodal and high dimensional function optimization problem.