A Hybrid Spherical Search and Moth-flame optimization Algorithm

Spherical search (SS) was recently proposed for solving the bound-constrained non-linear global optimization problems, and it shows quite competitive performance results with respect to other peer algorithms. However, it still suffers from the slow convergence speed and can't jump out the local minima once it is trapped. In this paper, we innovatively propose a hybrid spherical search and moth-flame optimization algorithm, namely SSMFO, aiming to make a better balance between exploration and exploitation. It is realized by enabling both algorithms to perform the search in a co-evolutionary manner. Experimental results based on 30 benchmark functions of IEEE CEC2017 demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms in terms of solution quality and convergence speed.