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Eagle perching optimizer for the online solution of constrained optimization

Ameer Tamoor Khan Orcid Logo, Shuai Li Orcid Logo, Yinyan Zhang, Predrag S. Stanimirovic

Memories - Materials, Devices, Circuits and Systems, Volume: 4, Start page: 100021

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.memori.2022.100021

Abstract

The paper proposes a novel nature-inspired optimization technique called Eagle Perching Optimizer (EPO). It is an addition to the family of swarm-based meta-heuristic algorithms. It mimics eagles’ perching nature to find prey (food). The EPO is based on the exploration and exploitation of an eagle w...

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Published in: Memories - Materials, Devices, Circuits and Systems
ISSN: 2773-0646
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62202
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first_indexed 2022-12-22T11:02:15Z
last_indexed 2023-01-13T19:23:30Z
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spelling 2023-01-11T14:59:23.8934455 v2 62202 2022-12-22 Eagle perching optimizer for the online solution of constrained optimization 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2022-12-22 MECH The paper proposes a novel nature-inspired optimization technique called Eagle Perching Optimizer (EPO). It is an addition to the family of swarm-based meta-heuristic algorithms. It mimics eagles’ perching nature to find prey (food). The EPO is based on the exploration and exploitation of an eagle when it descends from the height such that it formulates its trajectory in a way to get to the optimal solution (prey). The algorithm takes bigger chunks of search space and looks for the optimal solution. The optimal solution in that chunk becomes the search space for the next iteration, and this process is continuous until EPO converges to the optimal global solution. We performed the theoretical analysis of EPO, which shows that it converges to the optimal solution. The simulation includes three sets of problems, i.e., uni-model, multi-model, and constrained real-world problems. We employed EPO on the benchmark problems and compared the results with state-of-the-art meta-heuristic algorithms. For the real-world problems, we used a cantilever beam, three-bar truss, and gear train problems to test the robustness of EPO and later made the comparison. The comparison shows that EPO is comparable with other known meta-heuristic algorithms. Journal Article Memories - Materials, Devices, Circuits and Systems 4 100021 Elsevier BV 2773-0646 Optimization; Benchmark; Particle swarm optimization; Swarm algorithm; Constrained optimization; Stochastic algorithm; Heuristic algorithm 1 7 2023 2023-07-01 10.1016/j.memori.2022.100021 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2023-01-11T14:59:23.8934455 2022-12-22T11:00:07.8736996 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Ameer Tamoor Khan 0000-0001-6838-992x 1 Shuai Li 0000-0001-8316-5289 2 Yinyan Zhang 3 Predrag S. Stanimirovic 4 62202__26255__d62da0248a5242c39482d8d9e53106ef.pdf 62202.pdf 2023-01-11T14:57:09.3806802 Output 1478941 application/pdf Version of Record true © 2022 The Author(s). This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title Eagle perching optimizer for the online solution of constrained optimization
spellingShingle Eagle perching optimizer for the online solution of constrained optimization
Shuai Li
title_short Eagle perching optimizer for the online solution of constrained optimization
title_full Eagle perching optimizer for the online solution of constrained optimization
title_fullStr Eagle perching optimizer for the online solution of constrained optimization
title_full_unstemmed Eagle perching optimizer for the online solution of constrained optimization
title_sort Eagle perching optimizer for the online solution of constrained optimization
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Ameer Tamoor Khan
Shuai Li
Yinyan Zhang
Predrag S. Stanimirovic
format Journal article
container_title Memories - Materials, Devices, Circuits and Systems
container_volume 4
container_start_page 100021
publishDate 2023
institution Swansea University
issn 2773-0646
doi_str_mv 10.1016/j.memori.2022.100021
publisher Elsevier BV
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 The paper proposes a novel nature-inspired optimization technique called Eagle Perching Optimizer (EPO). It is an addition to the family of swarm-based meta-heuristic algorithms. It mimics eagles’ perching nature to find prey (food). The EPO is based on the exploration and exploitation of an eagle when it descends from the height such that it formulates its trajectory in a way to get to the optimal solution (prey). The algorithm takes bigger chunks of search space and looks for the optimal solution. The optimal solution in that chunk becomes the search space for the next iteration, and this process is continuous until EPO converges to the optimal global solution. We performed the theoretical analysis of EPO, which shows that it converges to the optimal solution. The simulation includes three sets of problems, i.e., uni-model, multi-model, and constrained real-world problems. We employed EPO on the benchmark problems and compared the results with state-of-the-art meta-heuristic algorithms. For the real-world problems, we used a cantilever beam, three-bar truss, and gear train problems to test the robustness of EPO and later made the comparison. The comparison shows that EPO is comparable with other known meta-heuristic algorithms.
published_date 2023-07-01T04:21:38Z
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score 11.013619

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