A cuckoo search algorithm with elite opposition-based strategy

Abstract

In this paper, a cuckoo search (CS) algorithm using elite opposition-based strategy is proposed. The opposite solution of the elite individual in the population is generated by an opposition-based strategy in the proposed algorithm and form an opposite search space by constructing the opposite population that locates inside the dynamic search boundaries, then, the search space of the algorithm is guided to approximate the space in which the global optimum is included by simultaneously evaluating the current population and the opposite one. This approach is helpful to obtain a tradeoff between the exploration and exploitation ability of CS. In order to enhance the local searching ability, local neighborhood search strategy is also applied in this proposed algorithm. The experiments were conducted on 14 classic benchmark functions and 28 more complex functions from the IEEE CEC'2013 competition, and the experimental results, compared with five other meta-heuristic algorithms and four improved cuckoo search algorithms, show that the proposed algorithm is much better than the compared ones at not only the accuracy of solutions but also for the convergence speed.