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Neural Computing and Applications

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28-10-2019 | Original Article

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

Authors: Yiying Zhang, Zhigang Jin, Ye Chen

Published in: Neural Computing and Applications | Issue 14/2020

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Abstract

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.

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Title: Hybridizing grey wolf optimization with neural network algorithm for global numerical optimization problems
Authors: Yiying Zhang
Zhigang Jin
Ye Chen
Publication date: 28-10-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 14/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04580-4

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