The heterogeneous Aquila optimization algorithm

Juan ZHAO; Zheng-Ming GAO; Juan ZHAO; Zheng-Ming GAO

doi:10.3934/mbe.2022275

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 6: 5867-5904. doi: 10.3934/mbe.2022275

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The heterogeneous Aquila optimization algorithm

Juan ZHAO ^1,,
Zheng-Ming GAO ^{2,3
,
,}

1.
School of electronics and information engineering, Jingchu University of Technology, Jingmen 448000, China
2.
School of computer engineering, Jingchu University of Technology, Jingmen 448000, China
3.
Institute of intelligent information technology, Hubei Jingmen industrial technology research institute, Jingmen 448000, China

Academic Editor: Runzhang Xu

Received: 10 February 2022 Revised: 21 March 2022 Accepted: 31 March 2022 Published: 08 April 2022

A new swarm-based optimization algorithm called the Aquila optimizer (AO) was just proposed recently with promising better performance. However, as reported by the proposer, it almost remains unchanged for almost half of the convergence curves at the latter iterations. Considering the better performance and the lazy latter convergence rates of the AO algorithm in optimization, the multiple updating principle is introduced and the heterogeneous AO called HAO is proposed in this paper. Simulation experiments were carried out on both unimodal and multimodal benchmark functions, and comparison with other capable algorithms were also made, most of the results confirmed the better performance with better intensification and diversification capabilities, fast convergence rate, low residual errors, strong scalabilities, and convinced verification results. Further application in optimizing three benchmark real-world engineering problems were also carried out, the overall better performance in optimizing was confirmed without any other equations introduced for improvement.
- Aquila optimization algorithm,
- meta-heuristic algorithm,
- nature-inspired algorithm,
- multiple updating principle
Citation: Juan ZHAO, Zheng-Ming GAO. The heterogeneous Aquila optimization algorithm[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 5867-5904. doi: 10.3934/mbe.2022275

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Abstract

A new swarm-based optimization algorithm called the Aquila optimizer (AO) was just proposed recently with promising better performance. However, as reported by the proposer, it almost remains unchanged for almost half of the convergence curves at the latter iterations. Considering the better performance and the lazy latter convergence rates of the AO algorithm in optimization, the multiple updating principle is introduced and the heterogeneous AO called HAO is proposed in this paper. Simulation experiments were carried out on both unimodal and multimodal benchmark functions, and comparison with other capable algorithms were also made, most of the results confirmed the better performance with better intensification and diversification capabilities, fast convergence rate, low residual errors, strong scalabilities, and convinced verification results. Further application in optimizing three benchmark real-world engineering problems were also carried out, the overall better performance in optimizing was confirmed without any other equations introduced for improvement.

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