Water cycle algorithm for solving constrained multi-objective optimization problems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sadollah, Ali | - |
dc.contributor.author | Eskandar, Hadi | - |
dc.contributor.author | Kim, Joong Hoon | - |
dc.date.accessioned | 2021-09-04T19:38:49Z | - |
dc.date.available | 2021-09-04T19:38:49Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2015-02 | - |
dc.identifier.issn | 1568-4946 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/94523 | - |
dc.description.abstract | In this paper, a metaheuristic optimizer, the multi-objective water cycle algorithm (MOWCA), is presented for solving constrained multi-objective problems. The MOWCA is based on emulation of the water cycle process in nature. In this study, a set of non-dominated solutions obtained by the proposed algorithm is kept in an archive to be used to display the exploratory capability of the MOWCA as compared to other efficient methods in the literature. Moreover, to make a comprehensive assessment about the robustness and efficiency of the proposed algorithm, the obtained optimization results are also compared with other widely used optimizers for constrained and engineering design problems. The comparisons are carried out using tabular, descriptive, and graphical presentations. (C) 2014 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | PARTICLE SWARM OPTIMIZATION | - |
dc.subject | GENETIC ALGORITHM | - |
dc.title | Water cycle algorithm for solving constrained multi-objective optimization problems | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Joong Hoon | - |
dc.identifier.doi | 10.1016/j.asoc.2014.10.042 | - |
dc.identifier.scopusid | 2-s2.0-84917740857 | - |
dc.identifier.wosid | 000346856600024 | - |
dc.identifier.bibliographicCitation | APPLIED SOFT COMPUTING, v.27, pp.279 - 298 | - |
dc.relation.isPartOf | APPLIED SOFT COMPUTING | - |
dc.citation.title | APPLIED SOFT COMPUTING | - |
dc.citation.volume | 27 | - |
dc.citation.startPage | 279 | - |
dc.citation.endPage | 298 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.subject.keywordPlus | PARTICLE SWARM OPTIMIZATION | - |
dc.subject.keywordPlus | GENETIC ALGORITHM | - |
dc.subject.keywordAuthor | Multi-objective optimization | - |
dc.subject.keywordAuthor | Water cycle algorithm | - |
dc.subject.keywordAuthor | Pareto optimal solutions | - |
dc.subject.keywordAuthor | Benchmark function | - |
dc.subject.keywordAuthor | Metaheuristics | - |
dc.subject.keywordAuthor | Constrained optimization | - |
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