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Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization

Zuyan Chen, Adam Francis, Shuai Li Orcid Logo, Bolin Liao, Dunhui Xiao Orcid Logo, Tran Thu Ha, Jianfeng Li, Lei Ding Orcid Logo, Xinwei Cao

Biomimetics, Volume: 7, Issue: 4, Start page: 144

Swansea University Authors: Adam Francis, Shuai Li Orcid Logo, Dunhui Xiao Orcid Logo

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DOI (Published version): 10.3390/biomimetics7040144

Abstract

A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discr...

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Published in: Biomimetics
ISSN: 2313-7673
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa62232
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spelling 2023-02-03T13:07:29.0766630 v2 62232 2023-01-03 Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization 8449248c17fec32f131097c0d1a768cc Adam Francis Adam Francis true false 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 62c69b98cbcdc9142622d4f398fdab97 0000-0003-2461-523X Dunhui Xiao Dunhui Xiao true false 2023-01-03 ACEM A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92. Journal Article Biomimetics 7 4 144 MDPI AG 2313-7673 metaheuristic algorithm; swarm intelligence; egret swarm optimization algorithm; constrained optimization 27 9 2022 2022-09-27 10.3390/biomimetics7040144 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University This research received no external funding. 2023-02-03T13:07:29.0766630 2023-01-03T08:54:04.8828917 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Zuyan Chen 1 Adam Francis 2 Shuai Li 0000-0001-8316-5289 3 Bolin Liao 4 Dunhui Xiao 0000-0003-2461-523X 5 Tran Thu Ha 6 Jianfeng Li 7 Lei Ding 0000-0001-7403-4770 8 Xinwei Cao 9 62232__26161__abf6de3c0ba24bebb46e0fe1952cca4d.pdf 62232.pdf 2023-01-03T08:57:56.0370038 Output 7936334 application/pdf Version of Record true This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/ 150 true https://ww2.mathworks.cn/matlabcentral/fileexchange/115595-egret-swarm-optimization-algorithm-esoa 151 true https://github.com/Knightsll/Egret_Swarm_Optimization_Algorithm
title Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
spellingShingle Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
Adam Francis
Shuai Li
Dunhui Xiao
title_short Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
title_full Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
title_fullStr Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
title_full_unstemmed Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
title_sort Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
author_id_str_mv 8449248c17fec32f131097c0d1a768cc
42ff9eed09bcd109fbbe484a0f99a8a8
62c69b98cbcdc9142622d4f398fdab97
author_id_fullname_str_mv 8449248c17fec32f131097c0d1a768cc_***_Adam Francis
42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
62c69b98cbcdc9142622d4f398fdab97_***_Dunhui Xiao
author Adam Francis
Shuai Li
Dunhui Xiao
author2 Zuyan Chen
Adam Francis
Shuai Li
Bolin Liao
Dunhui Xiao
Tran Thu Ha
Jianfeng Li
Lei Ding
Xinwei Cao
format Journal article
container_title Biomimetics
container_volume 7
container_issue 4
container_start_page 144
publishDate 2022
institution Swansea University
issn 2313-7673
doi_str_mv 10.3390/biomimetics7040144
publisher MDPI AG
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 - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 1
active_str 0
description A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92.
published_date 2022-09-27T20:18:29Z
_version_ 1821347475974258688
score 11.04748

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