Speeded-up cuckoo search using opposition-based learning

Abstract

For several decades, swarm intelligence (SI), emergent collective intelligence of groups of simple agents, has been applied to diverse research areas including optimization problems. Particle swarm optimization, ant colony optimization, artificial bee colony algorithm are well-known examples, and many variants are proposed so far. Recently proposed cuckoo search is also one class of SI. It mimics behaviors of cuckoo: intraspecific brood parasitism, cooperative breeding, and nest takeover. From the previous studies, it has quite a potential, so that it could outperform existing algorithms such as PSO. However, with respect to the convergence, CS shows slow performance. In this paper, we combine opposition-based learning (OBL) with CS, so that the convergence speed of CS becomes faster, not deteriorating the search ability of the algorithm. Through the simulation, the results indicate that the proposed algorithm outperforms the original algorithm not only in terms of convergence speed but also in terms of solution accuracy and success rate.