Abstract

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.

Software package

Click here to download the source codes of NoCuSa.

Nonhomogeneous search strategies

Figure 1. Schematic illustration of the motion strategies for the ith individual in NoCuSa.

Reference

  • Ngaam J. Cheung, Xue-Ming Ding, Hong-Bin Shen. A Nonhomogeneous Cuckoo Search Algorithm based-on Quantum Mechanism for Real-Parameter Optimization. IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2016.2517140, Jan. 2016 (In Press). [Supplementary Material]