Ensemble of cuckoo search variants. (September 2019) - Cheng, Jiatang; Wang, Lei; Xiong, Yan
Highlights: A candidate pool consisting of three different CS algorithms is constructed. An adaptive scheme is employed to determine the probability of each CS being selected. An external archive is embedded to further discourage premature convergence. Abstract: Cuckoo search is a simple yet effective evolutionary algorithm for solving numerical optimization problems. Recently, many variants of cuckoo search have been developed to further enhance the performance. These improved versions have different capabilities in tackling the optimization problems with different properties, so it is difficult to determine which algorithm is best for all problems. To address this issue, we present a new cuckoo search algorithm named the ensemble cuckoo search variant. In this developed version, a candidate pool consisting of three different cuckoo search algorithms is first constructed. According to the previous experiences in producing promising solutions, an adaptive scheme is then used to determine the probability that each algorithm can be assigned to distinct individuals in the current population. Also, an external archive is embedded to further discourage premature convergence. To assess the performance of this ensemble algorithm, 42 test problems derived from CEC 2005 and CEC 2013 are employed. Experimental results indicate that the proposed algorithm is a competitive method compared with seven well-established cuckoo search variants and several other well-known evolutionary algorithms.