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Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(2): 461-471
Ameer Hamza Khan, Xinwei Cao, Shuai Li, Vasilios N. Katsikis and Liefa Liao, "BAS-ADAM: An ADAM Based Approach to Improve the Performance of Beetle Antennae Search Optimizer," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 461-471, Mar. 2020. doi: 10.1109/JAS.2020.1003048
Citation: Ameer Hamza Khan, Xinwei Cao, Shuai Li, Vasilios N. Katsikis and Liefa Liao, "BAS-ADAM: An ADAM Based Approach to Improve the Performance of Beetle Antennae Search Optimizer," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 461-471, Mar. 2020. doi: 10.1109/JAS.2020.1003048
shu

BAS-ADAM: An ADAM Based Approach to Improve the Performance of Beetle Antennae Search Optimizer

doi: 10.1109/JAS.2020.1003048
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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.

     

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    Highlights

    • Incorporating the concept of gradient into a metaheuristic optimization framework.
    • Using the ADAM update-rule to adjust the step-size adaptively.
    • Experimental demonstration of smooth convergence and faster convergence near the valley.

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