Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization
In this paper, a novel swarm-based metaheuristic algorithm is proposed, which is called tuna swarm optimization (TSO). The main inspiration for TSO is based on the cooperative foraging behavior of tuna swarm. The work mimics two foraging behaviors of tuna swarm, including spiral foraging and parabol...
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| Veröffentlicht in: | Computational intelligence and neuroscience 2021, Vol.2021 (1), p.9210050-9210050 |
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| creator | Xie, Lei Han, Tong Zhou, Huan Zhang, Zhuo-Ran Han, Bo Tang, Andi |
| description | In this paper, a novel swarm-based metaheuristic algorithm is proposed, which is called tuna swarm optimization (TSO). The main inspiration for TSO is based on the cooperative foraging behavior of tuna swarm. The work mimics two foraging behaviors of tuna swarm, including spiral foraging and parabolic foraging, for developing an effective metaheuristic algorithm. The performance of TSO is evaluated by comparison with other metaheuristics on a set of benchmark functions and several real engineering problems. Sensitivity, scalability, robustness, and convergence analyses were used and combined with the Wilcoxon rank-sum test and Friedman test. The simulation results show that TSO performs better compared to other comparative algorithms. |
| doi_str_mv | 10.1155/2021/9210050 |
| format | Article |
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The main inspiration for TSO is based on the cooperative foraging behavior of tuna swarm. The work mimics two foraging behaviors of tuna swarm, including spiral foraging and parabolic foraging, for developing an effective metaheuristic algorithm. The performance of TSO is evaluated by comparison with other metaheuristics on a set of benchmark functions and several real engineering problems. Sensitivity, scalability, robustness, and convergence analyses were used and combined with the Wilcoxon rank-sum test and Friedman test. The simulation results show that TSO performs better compared to other comparative algorithms.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2021/9210050</identifier><identifier>PMID: 34721567</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Algorithms ; Analysis ; Benchmarking ; Computer Simulation ; Design engineering ; Education ; Engineering ; Evolution ; Exploitation ; Food ; Foraging behavior ; Global optimization ; Heuristic methods ; Mathematical models ; Mathematical optimization ; Methods ; Optimization algorithms ; Review ; Swimming</subject><ispartof>Computational intelligence and neuroscience, 2021, Vol.2021 (1), p.9210050-9210050</ispartof><rights>Copyright © 2021 Lei Xie et al.</rights><rights>COPYRIGHT 2021 John Wiley & Sons, Inc.</rights><rights>Copyright © 2021 Lei Xie et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2021 Lei Xie et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-ecc9cfb54dbc768519f9e0cc409cf3021b540e5cc30ef0a6fb2690859fe05ebd3</citedby><cites>FETCH-LOGICAL-c476t-ecc9cfb54dbc768519f9e0cc409cf3021b540e5cc30ef0a6fb2690859fe05ebd3</cites><orcidid>0000-0002-8837-8977 ; 0000-0002-0185-3289 ; 0000-0003-4694-5053</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550856/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550856/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,4024,27923,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34721567$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Khalil, Ahmed Mostafa</contributor><contributor>Ahmed Mostafa Khalil</contributor><creatorcontrib>Xie, Lei</creatorcontrib><creatorcontrib>Han, Tong</creatorcontrib><creatorcontrib>Zhou, Huan</creatorcontrib><creatorcontrib>Zhang, Zhuo-Ran</creatorcontrib><creatorcontrib>Han, Bo</creatorcontrib><creatorcontrib>Tang, Andi</creatorcontrib><title>Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization</title><title>Computational intelligence and neuroscience</title><addtitle>Comput Intell Neurosci</addtitle><description>In this paper, a novel swarm-based metaheuristic algorithm is proposed, which is called tuna swarm optimization (TSO). 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| subjects | Algorithms Analysis Benchmarking Computer Simulation Design engineering Education Engineering Evolution Exploitation Food Foraging behavior Global optimization Heuristic methods Mathematical models Mathematical optimization Methods Optimization algorithms Review Swimming |
| title | Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization |
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