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A Multi-objective Evolutionary Algorithm Based On Decomposition For Continuous Optimization Using A Step-function Technique

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dc.contributor.authorChuah, How Siang
dc.date.accessioned2023-08-24T04:43:14Z
dc.date.available2023-08-24T04:43:14Z
dc.date.issued2022-06
dc.description.abstractMulti-objective optimization is an area of study which solves complex real-world problem that involves two or three objectives. Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) is one of the algorithms that utilize the concepts of decomposition and neighbourhood to solve multi-objective problems. One of the recent MOEA/D algorithms, i.e., Constant-distance based Neighbours for MOEA/D with Dynamic Weight Vector Adjustment (MOEA/D-AWACD), integrates the concept of a constant-distance neighbourhood and a dynamic weight vector design. This combination creates a flexible neighbourhood that can adapt to the weight vectors changes. However, MOEA/D-AWACD’s performance is dependent on a constant-distance parameter,
dc.identifier.urihttps://erepo.usm.my/handle/123456789/17402
dc.language.isoen_US
dc.subjectA Multi-objective Evolutionary Algorithm Based
dc.subjectDecomposition For Continuous Optimization Using A Step-function Technique
dc.subjectChuah
dc.subjectHow Siang
dc.subjectQ Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
dc.subjectPusat Pengajian Sains Komputer
dc.titleA Multi-objective Evolutionary Algorithm Based On Decomposition For Continuous Optimization Using A Step-function Technique
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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