A novel swarm intelligence algorithm based on cuckoo search algorithm (NSICS)

Abstract

Cuckoo Search algorithm (CS) is swarm intelligence based algorithm motivated by nature. This algorithm is based on brood parasitism of some cuckoo species and has high capability of global search. Therefore, the global optimum can be figured out with higher probability. This paper proposes a novel meta-heuristic approach, called NSICS, based on CS. NSICS is able to explore not only the search space on global scale but also around the optimum on local scale more efficiently. Consequently, more accurate results can be obtained. To approach these purposes, three operators of Eggs laying, lévy fights and Move are applied. Experiments are studied on thirteen common benchmark functions among unimodal, multimodal, shifted and shifted rotated classes and then compared with CS, GPSO, SFLA and GSA algorithms. These algorithms are chosen from swarm intelligence based, bio-inspired based and chemistry and physics based algorithms’ category. The simulations indicate the proposed algorithm has satisfactory performance.