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A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion
The dragonfly algorithm (DA) is one of the optimization techniques developed in recent years. The random flying behavior of dragonflies in nature is modeled in the DA using the Levy flight mechanism (LFM). However, LFM has disadvantages such as the overflowing of the search area and interruption of...
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Published in: | Computational intelligence and neuroscience 2019-01, Vol.2019 (2019), p.1-17 |
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description | The dragonfly algorithm (DA) is one of the optimization techniques developed in recent years. The random flying behavior of dragonflies in nature is modeled in the DA using the Levy flight mechanism (LFM). However, LFM has disadvantages such as the overflowing of the search area and interruption of random flights due to its big searching steps. In this study, an algorithm, known as the Brownian motion, is used to improve the randomization stage of the DA. The modified DA was applied to 15 single-objective and 6 multiobjective problems and then compared with the original algorithm. The modified DA provided up to 90% improvement compared to the original algorithm’s minimum point access. The modified algorithm was also applied to welded beam design, a well-known benchmark problem, and thus was able to calculate the optimum cost 20% lower. |
doi_str_mv | 10.1155/2019/6871298 |
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The random flying behavior of dragonflies in nature is modeled in the DA using the Levy flight mechanism (LFM). However, LFM has disadvantages such as the overflowing of the search area and interruption of random flights due to its big searching steps. In this study, an algorithm, known as the Brownian motion, is used to improve the randomization stage of the DA. The modified DA was applied to 15 single-objective and 6 multiobjective problems and then compared with the original algorithm. The modified DA provided up to 90% improvement compared to the original algorithm’s minimum point access. The modified algorithm was also applied to welded beam design, a well-known benchmark problem, and thus was able to calculate the optimum cost 20% lower.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2019/6871298</identifier><identifier>PMID: 31281336</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Animals ; Benchmarking ; Brownian motion ; Civil engineering ; Comparative analysis ; Computer Simulation - economics ; Design modifications ; Mathematical optimization ; Models, Biological ; Motion ; Multiple objective analysis ; Odonata ; Optimization ; Optimization algorithms ; Optimization techniques ; Overflow ; Parameter estimation ; Problem Solving ; R&D ; Research & development ; Studies</subject><ispartof>Computational intelligence and neuroscience, 2019-01, Vol.2019 (2019), p.1-17</ispartof><rights>Copyright © 2019 Çiğdem İnan Acı and Hakan Gülcan.</rights><rights>COPYRIGHT 2019 John Wiley & Sons, Inc.</rights><rights>Copyright © 2019 Çiğdem İnan Acı and Hakan Gülcan. 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. http://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2019 Çiğdem İnan Acı and Hakan Gülcan. 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c499t-df7898bc21429b43e6c017d4881b6b7b22fc86a6d373e4a18c1871a427ffe9cc3</citedby><cites>FETCH-LOGICAL-c499t-df7898bc21429b43e6c017d4881b6b7b22fc86a6d373e4a18c1871a427ffe9cc3</cites><orcidid>0000-0002-0028-9890</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2241307206/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2241307206?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,25753,27924,27925,37012,37013,44590,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31281336$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Bartak, Roman</contributor><contributor>Roman Bartak</contributor><creatorcontrib>Aci, Cigdem Inan</creatorcontrib><creatorcontrib>Gulcan, Hakan</creatorcontrib><title>A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion</title><title>Computational intelligence and neuroscience</title><addtitle>Comput Intell Neurosci</addtitle><description>The dragonfly algorithm (DA) is one of the optimization techniques developed in recent years. 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subjects | Algorithms Animals Benchmarking Brownian motion Civil engineering Comparative analysis Computer Simulation - economics Design modifications Mathematical optimization Models, Biological Motion Multiple objective analysis Odonata Optimization Optimization algorithms Optimization techniques Overflow Parameter estimation Problem Solving R&D Research & development Studies |
title | A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion |
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