Improved artificial bee colony algorithm with self-adaptive global bestguided quick searching strategy

For the problems of the unbalanced capability between exploration and exploitation of artificial bee colony(ABC) algorithm, an improved ABC algorithm with the self-adaptive global best-guided quick searching strategy(ABCSGQ) is proposed. On the one hand, the self-adaptive search equation is used for employed bees so as to balance the exploration and exploitation of two different solution searching methods. On the other hand, the global best-guided neighborhood search method is adopted for onlooker bees in order to improve the convergence precision and the global search ability. The simulation on 14 benchmark functions shows that the proposed algorithm fully utilizes and balances the exploration and exploitation, and greatly improves the accuracy of optima solutions and convergence speed compared with other current improved ABC algorithms for optimization. ©, 2014, Northeast University. All right reserved.