Multi-strategy ensemble artificial bee colony algorithm

Abstract Artificial bee colony (ABC) is a recently proposed optimization technique which has shown to be competitive to other population-based stochastic algorithms. However, ABC is good at exploration but poor at exploitation because of its solution search strategy. Thus, to obtain an efficient performance, utilizing different characteristics of solution search strategies can be appropriate during different stages of the search process to achieve a tradeoff between exploration and exploitation. In this paper, we propose a novel multi-strategy ensemble ABC (MEABC) algorithm. In MEABC, a pool of distinct solution search strategies coexists throughout the search process and competes to produce offspring. Experiments are conducted on a set of commonly used numerical benchmark functions, including the CEC 2013 shifted and rotated problems. Results show that MEABC performs significantly better than, or at least comparable to, some well-established evolutionary algorithms.