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dc.contributor.authorSahoo, SK
dc.contributor.authorSaha, AK
dc.contributor.authorSharma, S
dc.contributor.authorMirjalili, S
dc.contributor.authorChakraborty, S
dc.description.abstractNature-inspired meta-heuristics have demonstrated superior efficiency in the solution of complicated nonlinear optimization problems than conventional techniques. In this article, an enhanced moth flame optimization (EMFO) is designed using the mutualism phase from the symbiotic organism search (SOS) algorithm. The suggested approach is examined on 36 classical benchmark functions taken from literature. The outputs of EMFO are compared with the latest meta-heuristic algorithms and variants of the MFO algorithm. The comparison results indicate that our proposed method is competitive from the compared methods. Also, the Friedman rank test is used to evaluate the new algorithm’s efficiency, and it is found that the rank of EMFO is superior. Finally, EMFO is being applied to solve seven real-world problems, and the outcomes of the proposed algorithm were found to be satisfactory.
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofjournalSoft Computing
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchCognitive and computational psychology
dc.titleAn enhanced moth flame optimization with mutualism scheme for function optimization
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSahoo, SK; Saha, AK; Sharma, S; Mirjalili, S; Chakraborty, S, An enhanced moth flame optimization with mutualism scheme for function optimization, Soft Computing, 2022
gro.description.notepublicThis publication has been entered as an advanced online version in Griffith Research Online.
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali

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