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A Model-Free Approach for Online Optimization of Nonlinear Systems

Ameer Hamza Khan, Xinwei Cao, Bin Xu, Shuai Li Orcid Logo

IEEE Transactions on Circuits and Systems II: Express Briefs, Volume: 69, Issue: 1, Pages: 109 - 113

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/tcsii.2021.3079125

Abstract

This paper proposes a strategy to search optimal control parameters of a complex nonlinear system using a metaheuristic optimization algorithm in a computationally efficient manner. The proposed algorithm, called BAS-swarm (Beetle Antennae Search-swarm), is a gradient-free optimizer based on the BAS...

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Published in: IEEE Transactions on Circuits and Systems II: Express Briefs
ISSN: 1549-7747 1558-3791
Published: Institute of Electrical and Electronics Engineers (IEEE) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa56946
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spelling 2022-01-13T13:22:00.1579542 v2 56946 2021-05-24 A Model-Free Approach for Online Optimization of Nonlinear Systems 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-05-24 MECH This paper proposes a strategy to search optimal control parameters of a complex nonlinear system using a metaheuristic optimization algorithm in a computationally efficient manner. The proposed algorithm, called BAS-swarm (Beetle Antennae Search-swarm), is a gradient-free optimizer based on the BAS algorithm, inspired by mimicking the food foraging behavior of beetles. BAS-swarm takes advantage of the fact that the antennae of insects are not single sensory organs. The antennae contain an array of tiny fiber. Antennae fiber enables the insects to have a microscopic insight into the environment when moving toward the source of food smell. BAS-swarm uses this insight to improve the performance of BAS by approximating the gradient direction at each iteration with the help of a swarm of antenna fiber. Since the proposed algorithm approximates gradient by mimicking the behavior of beetle antenna fiber located at random locations, it does not require the numerical computation of the actual gradient, making it very efficient for optimization of nonlinear non-convex systems. We verified the accuracy and efficiency of the proposed algorithm by training single-layer neural networks with nonlinear activation function and compared its performance with Particle Swarm Optimizer (PSO), a well-studied extremum seeking algorithm, and the original BAS algorithm. The experiment shows that the proposed algorithm provides several-fold improvement and faster convergence as compared to other metaheuristic algorithms. Journal Article IEEE Transactions on Circuits and Systems II: Express Briefs 69 1 109 113 Institute of Electrical and Electronics Engineers (IEEE) 1549-7747 1558-3791 1 1 2022 2022-01-01 10.1109/tcsii.2021.3079125 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2022-01-13T13:22:00.1579542 2021-05-24T10:19:17.9801483 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Ameer Hamza Khan 1 Xinwei Cao 2 Bin Xu 3 Shuai Li 0000-0001-8316-5289 4 56946__19972__032f6378a5124b649fcaffabfff60c32.pdf 56946.pdf 2021-05-24T10:23:58.5815095 Output 986320 application/pdf Accepted Manuscript true true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title A Model-Free Approach for Online Optimization of Nonlinear Systems
spellingShingle A Model-Free Approach for Online Optimization of Nonlinear Systems
Shuai Li
title_short A Model-Free Approach for Online Optimization of Nonlinear Systems
title_full A Model-Free Approach for Online Optimization of Nonlinear Systems
title_fullStr A Model-Free Approach for Online Optimization of Nonlinear Systems
title_full_unstemmed A Model-Free Approach for Online Optimization of Nonlinear Systems
title_sort A Model-Free Approach for Online Optimization of Nonlinear Systems
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Ameer Hamza Khan
Xinwei Cao
Bin Xu
Shuai Li
format Journal article
container_title IEEE Transactions on Circuits and Systems II: Express Briefs
container_volume 69
container_issue 1
container_start_page 109
publishDate 2022
institution Swansea University
issn 1549-7747
1558-3791
doi_str_mv 10.1109/tcsii.2021.3079125
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description This paper proposes a strategy to search optimal control parameters of a complex nonlinear system using a metaheuristic optimization algorithm in a computationally efficient manner. The proposed algorithm, called BAS-swarm (Beetle Antennae Search-swarm), is a gradient-free optimizer based on the BAS algorithm, inspired by mimicking the food foraging behavior of beetles. BAS-swarm takes advantage of the fact that the antennae of insects are not single sensory organs. The antennae contain an array of tiny fiber. Antennae fiber enables the insects to have a microscopic insight into the environment when moving toward the source of food smell. BAS-swarm uses this insight to improve the performance of BAS by approximating the gradient direction at each iteration with the help of a swarm of antenna fiber. Since the proposed algorithm approximates gradient by mimicking the behavior of beetle antenna fiber located at random locations, it does not require the numerical computation of the actual gradient, making it very efficient for optimization of nonlinear non-convex systems. We verified the accuracy and efficiency of the proposed algorithm by training single-layer neural networks with nonlinear activation function and compared its performance with Particle Swarm Optimizer (PSO), a well-studied extremum seeking algorithm, and the original BAS algorithm. The experiment shows that the proposed algorithm provides several-fold improvement and faster convergence as compared to other metaheuristic algorithms.
published_date 2022-01-01T04:12:18Z
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score 11.013371

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