A Nonhomogeneous Cuckoo Search Algorithm Based on Quantum Mechanism for Real Parameter Optimization
针对标准布谷鸟搜索算法个体搜索行为单一、易早熟收敛的问题，提出一种基于量子机制的非均匀搜索策略变体，并在24个基准函数上验证其优于原算法及多种对比方法。
Cuckoo search (CS) 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 paper presents a new variant of CS algorithm with nonhomogeneous search strategies based on quantum mechanism to enhance search ability of the classical CS algorithm. Featured contributions in this paper include: 1) quantum-based strategy is developed for nonhomogeneous update laws and 2) we, for the first time, present a set of theoretical analyses on CS algorithm as well as the proposed algorithm, respectively, and conclude a set of parameter boundaries guaranteeing the convergence of the CS algorithm and the proposed algorithm. On 24 benchmark functions, we compare our method with five existing CS-based methods and other ten state-of-the-art algorithms. The numerical results demonstrate that the proposed algorithm is significantly better than the original CS algorithm and the rest of compared methods according to two nonparametric tests.