Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61365
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dc.contributor.authorKeivanian, Farshiden
dc.contributor.authorChiong, Raymonden
dc.date.accessioned2024-07-10T00:59:45Z-
dc.date.available2024-07-10T00:59:45Z-
dc.date.issued2022-
dc.identifier.citationExpert Systems with Applications, v.195, p. 1-20en
dc.identifier.issn1873-6793en
dc.identifier.issn0957-4174en
dc.identifier.urihttps://hdl.handle.net/1959.11/61365-
dc.description.abstract<p>In this paper, we propose a novel hybrid fuzzy–metaheuristic approach with the aim of overcoming premature convergence when solving multimodal single and multi-objective optimization problems. The metaheuristic algorithm used in our proposed approach is based on the imperialist competitive algorithm (ICA), a populationbased method for optimization. The ICA divides its population into sub-populations, known as empires. Each empire is composed of a high fitness solution—the imperialist—and some lower fitness solutions—the colonies. Colonies move towards their associated imperialist to achieve better status (higher fitness). The most powerful empire tends to attract weaker colonies. These competitions and movements can be enhanced for better algorithm performance. In our hybrid approach, a global learning strategy is devised for each colony to learn from its best-known position, its associated imperialist and the global best imperialist. A fast-evolutionary elitism local search is used to enhance the collaborative search mechanism (competition) in each empire, and thus the overall optimization performance may be improved. Other main evolutionary operators include velocity adaptation and velocity divergence. To address parameterization and computational cost evaluation issues, two fuzzy inferencing mechanisms are designed and used in parallel: one is a learning strategy adaptor in each run, and the other is a smart evolution selector in each running window. For Pareto front approximation, fast-elitism nondominated sorting is applied to the solutions, and a novel penalized sigma diversity index is designed to estimate the diversity (power) of solutions in the same rank. Comprehensive experimental results based on 22 singleobjective and 25 multi-objective benchmark instances clearly show that our proposed approach provides better solutions compared with other popular metaheuristics and state-of-the-art ICA variants. The proposed approach can be used as an optimization module in any intelligent decision-making systems to enhance efficiency and accuracy. The designed fuzzy inferencing mechanisms can also be incorporated into any single- or multi-objective optimizers for parameter tuning purposes, to make the optimizers more adaptive to new problems or environments.</p>en
dc.languageenen
dc.publisherElsevier Ltden
dc.relation.ispartofExpert Systems with Applicationsen
dc.titleA novel hybrid fuzzy–metaheuristic approach for multimodal single and multi-objective optimization problemsen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.eswa.2021.116199en
local.contributor.firstnameFarshiden
local.contributor.firstnameRaymonden
local.profile.schoolSchool of Science & Technologyen
local.profile.emailrchiong@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeUnited Kingdomen
local.identifier.runningnumber116199en
local.format.startpage1en
local.format.endpage20en
local.peerreviewedYesen
local.identifier.volume195en
local.contributor.lastnameKeivanianen
local.contributor.lastnameChiongen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61365en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleA novel hybrid fuzzy–metaheuristic approach for multimodal single and multi-objective optimization problemsen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorKeivanian, Farshiden
local.search.authorChiong, Raymonden
local.uneassociationNoen
dc.date.presented2022-
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2022en
local.year.presented2022en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/6688b788-bf28-40f2-a262-f0a5c3d41f35en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-07-23en
Appears in Collections:Journal Article
School of Science and Technology
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