Extreme Individual Guided Artificial Bee Colony Algorithm

Abstract

To overcome the drawbacks of poor development ability, easy to fall into local optimum, slow conver-gence speed of artificial bee colony (ABC) algorithm in solving function optimization problems, an extreme indi-vidual guided artificial bee colony (EABC) algorithm is proposed. Firstly, global extremum and neighborhood ex-tremum individuals are used to guide search for employed bees and following bees. The global extremum individual guided search is good for the retention and development of excellent individuals in the population, so that the algorithm jumps out of local extremum and avoids premature convergence. The neighborhood extremum individual guided search is good for enhancing the search accuracy and improving the convergence speed of the algorithm, and the random number ris used to balance two search mechanisms. Secondly, the small probability mutation operator is introduced into search process, and each dimension of bee individual is mutated with a small probability to overcome local extremum and premature convergence of the algorithm. Finally, the greedy selection strategy based on the value of objective function is adopted to improve the optimization performance of the algorithm. Simulation experiments are carried out with 28 test functions and the algorithm proposed in this paper is compared with other algorithms. Experimental results show that the improved algorithm has higher optimization performance and faster convergence speed.

Keywords

• |artificial bee colony (abc) algorithm|extreme individual guidance|small probability mutation|ob-jective function value