Hybridizing grey wolf optimization with neural network algorithm for global numerical optimization problems

This paper proposes a novel hybrid algorithm, called grey wolf optimization with neural network algorithm (GNNA), for solving global numerical optimization problems. The core idea of GNNA is to make full use of good global search ability of neural network algorithm (NNA) and fast convergence of grey wolf optimizer (GWO). Moreover, both NNA and GWO are improved to boost their own advantages. For NNA, an improved NNA is given to strengthen the exploration ability of NNA by discarding transfer operator and introducing random modification factor. For GWO, an enhanced GWO is presented, which adjusts the exploration rate based on reinforcement learning principles. Then the improved NNA and the enhanced GWO are hybridized by dynamic population mechanism. A comprehensive set of 23 well-known unconstrained benchmark functions are employed to examine the performance of GNNA compared with 13 metaheuristic algorithms. Such comparisons suggest that the combination of the improved NNA and the enhanced GWO is very effective and GNNA is clearly seen to be more successful in both solution quality and computational efficiency.