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Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method

Sen Zhang

College of Information Science and Engineering, Guangxi University for Nationalities, Guangxi, Nanning 530006, China

E-mail Address: zhangsen98k@163.com

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Qifang Luo

College of Information Science and Engineering, Guangxi University for Nationalities, Guangxi, Nanning 530006, China

Key Laboratory of Guangxi High Schools Complex, System and Computational Intelligence, Guangxi, Nanning 530006, China

E-mail Address: l.qf@163.com

Corresponding author.

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Yongquan Zhou

College of Information Science and Engineering, Guangxi University for Nationalities, Guangxi, Nanning 530006, China

Key Laboratory of Guangxi High Schools Complex, System and Computational Intelligence, Guangxi, Nanning 530006, China

E-mail Address: Yongquanzhou@126.com

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https://doi.org/10.1142/S1469026817500122Cited by:82 (Source: Crossref)

Abstract

To overcome the poor population diversity and slow convergence rate of grey wolf optimizer (GWO), this paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO). The diversity of grey wolf population is increased and exploration ability is improved. The experiment results of 13 standard benchmark functions indicate that the proposed algorithm has strong global and local search ability, quick convergence rate and high accuracy. EOGWO is also effective and feasible in both low-dimensional and high-dimensional case. Compared to particle swarm optimization with chaotic search (CLSPSO), gravitational search algorithm (GSA), flower pollination algorithm (FPA), cuckoo search (CS) and bat algorithm (BA), the proposed algorithm shows a better optimization performance and robustness.

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