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BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

Ameer Hamza Khan, Xinwei Cao, Shuai Li Orcid Logo, Vasilios N. Katsikis, Liefa Liao

IEEE/CAA Journal of Automatica Sinica, Volume: 7, Issue: 2, Pages: 461 - 471

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/jas.2020.1003048

Abstract

In this paper, we propose enhancements to Beetle Antennae search ( BAS ) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the a...

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Published in: IEEE/CAA Journal of Automatica Sinica
ISSN: 2329-9266 2329-9274
Published: Institute of Electrical and Electronics Engineers (IEEE) 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa53871
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Abstract: In this paper, we propose enhancements to Beetle Antennae search ( BAS ) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation ( ADAM ) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with particle swarm optimizer ( PSO ) and the original BAS algorithm.
Issue: 2
Start Page: 461
End Page: 471

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