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dc.contributor.authorNadimi-Shahraki, MH
dc.contributor.authorTaghian, S
dc.contributor.authorMirjalili, S
dc.contributor.authorFaris, H
dc.date.accessioned2021-01-06T22:49:25Z
dc.date.available2021-01-06T22:49:25Z
dc.date.issued2020
dc.identifier.issn1568-4946
dc.identifier.doi10.1016/j.asoc.2020.106761
dc.identifier.urihttp://hdl.handle.net/10072/400740
dc.description.abstractIn this article, an effective metaheuristic algorithm named multi-trial vector-based differential evolution (MTDE) is proposed. The MTDE is distinguished by introducing an adaptive movement step designed based on a new multi-trial vector approach named MTV, which combines different search strategies in the form of trial vector producers (TVPs). In the developed MTV approach, the TVPs are applied on their dedicated subpopulation, which are distributed by a winner-based distribution policy, and share their experiences efficiently by using a life-time archive. The MTV can be deployed by different types of TVPs, particularly, we use the MTV approach in the MTDE algorithm by three TVPs: representative based trial vector producer, local random based trial vector producer, and global best history based trial vector producer. Therefore, this study introduces the MTV approach to boost the performance of the MTDE and demonstrates its advantages in dealing with problems of different levels of complexity. The performance of the proposed MTDE algorithm is evaluated on CEC 2018 benchmark suite which include unimodal, multimodal, hybrid, and composition functions and four complex engineering design problems. The experimental and statistical results are compared with state-of-the-art metaheuristic algorithms: GWO, WOA, SSA, HHO, CoDE, EPSDE, QUATRE, and MKE. The results demonstrate that the MTDE algorithm shows improved performance and benefits from high accuracy of optimal solutions obtained.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom106761
dc.relation.ispartofjournalApplied Soft Computing Journal
dc.relation.ispartofvolume97
dc.subject.fieldofresearchApplied Mathematics
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchcode0102
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0806
dc.titleMTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationNadimi-Shahraki, MH; Taghian, S; Mirjalili, S; Faris, H, MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems, Applied Soft Computing Journal, 2020, 97, pp. 106761
dc.date.updated2021-01-06T22:47:42Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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