Cuckoo search algorithm is a nature-inspired search algorithm, in which all the individuals have identical search behaviors. However, this simple homogeneous search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To overcome the drawback, this study presents a new variant of cuckoo search algorithm with non-homogeneous search strategies based on quantum mechanism to enhance search ability of the classical cuckoo search algorithm. Featured contributions in this study include: (1) quantum-based strategy is developed for non-homogeneous update laws; (2) we for the first time present a set of theoretical analyses on cuckoo search algorithm as well as the proposed algorithm, respectively, and conclude a set of parameter boundaries guaranteeing the convergence of the cuckoo search algorithm and the proposed algorithm. On twenty-four benchmark functions, we compare our method with five existing cuckoo search based methods and other ten state-of-the-art algorithms. The numerical results demonstrate the proposed algorithm is significantly better than the original cuckoo search algorithm and the rest of compared methods according to two non-parametric tests.
Click here to download the source codes of NoCuSa.
Figure 1. Schematic illustration of the motion strategies for the ith individual in NoCuSa.