Research PaperNo Access

A novel multi-level image segmentation algorithm via random opposition learning-based Aquila optimizer

Jia Cai

https://orcid.org/0000-0001-8942-8668

School of Digital Economics, Guangdong University of Finance and Economics, Guangzhou 510320, P. R. China

E-mail Address: jiacai1999@gdufe.edu.cn

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Tianhua Luo

School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou 510320, P. R. China

E-mail Address: luoth0919@gmail.com

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Zhilong Xiong

School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou 510320, P. R. China

E-mail Address: zhilongxiong98@outlook.com

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, and

Yi Tang

School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, P. R. China

E-mail Address: yitang4math@ynni.edu.cn

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https://doi.org/10.1142/S0219691323500182Cited by:0 (Source: Crossref)

Abstract

Aquila optimizer (AO) is an efficient meta-heuristic optimization method, which mimics the hunting style of Aquila in nature. However, the AO algorithm may suffer from immature convergence during the exploitation stage. In this paper, two strategies are elegantly employed into conventional AO, such as random opposition-based learning and nonlinear flexible jumping factor, which can efficiently enhance the performance of conventional AO. Experiments on $17$ benchmark functions and image segmentation demonstrate the effectiveness of the proposed algorithm. Comparison with several state-of-the-art meta-heuristic optimization techniques indicates the efficacy of the developed method.

Keywords:

AMSC: 68W20

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