Publication:
Nature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problems

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dc.citedby5
dc.contributor.authorChang C.C.W.en_US
dc.contributor.authorDing T.J.en_US
dc.contributor.authorEe C.C.W.en_US
dc.contributor.authorHan W.en_US
dc.contributor.authorPaw J.K.S.en_US
dc.contributor.authorSalam I.en_US
dc.contributor.authorBhuiyan M.A.S.en_US
dc.contributor.authorKuan G.S.en_US
dc.contributor.authorid57473577900en_US
dc.contributor.authorid38863172300en_US
dc.contributor.authorid58953009700en_US
dc.contributor.authorid56097111100en_US
dc.contributor.authorid58168727000en_US
dc.contributor.authorid36601445500en_US
dc.contributor.authorid55433759000en_US
dc.contributor.authorid58953500700en_US
dc.date.accessioned2025-03-03T07:42:33Z
dc.date.available2025-03-03T07:42:33Z
dc.date.issued2024
dc.description.abstractNowadays, nature-inspired artificial intelligent metaheuristic optimization algorithms (MHOAs) have gained many attentions from researchers all over the world due to their capabilities in solving various decision-making problems. These algorithms are inspired and modelled based on the searching behaviour of animals in real life. This review paper provides in-depth discussions on various challenges and breakthroughs in numerous state-of-the-art nature-inspired artificial intelligence (AI) algorithms in solving multi-objective optimization engineering problems with emphasis on the mathematical modelling and algorithm developments. From conventional analysis such as speeds and accuracies to relatively advanced benchmarks such as complexities and convergence patterns, the comparison criteria of population-based and nature-inspired search mechanisms have evolved in the effort to further enhance the overall performance and reachability of these heuristic algorithms. This paper provides a platform for young readers and new researches who are about to indulge in the realm of various AI optimization techniques. Comprehensive analysis and discussions are presented on various state-of-the-art methods, with possible fields of applications proposed. Suitability of search mechanisms to specific optimization problem categories has also been investigated and presented, with combined or hybrid methods under scrutiny. ? The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11831-024-10090-x
dc.identifier.epage3584
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85188509203
dc.identifier.spage3551
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85188509203&doi=10.1007%2fs11831-024-10090-x&partnerID=40&md5=61906357271692d099defd9ba609f3cb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36463
dc.identifier.volume31
dc.pagecount33
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleArchives of Computational Methods in Engineering
dc.subjectBenchmarking
dc.subjectBiomimetics
dc.subjectDecision making
dc.subjectMultiobjective optimization
dc.subjectArtificial intelligent
dc.subjectDecision-making problem
dc.subjectEngineering optimization problems
dc.subjectMetaheuristic optimization
dc.subjectMulti objective
dc.subjectOptimization algorithms
dc.subjectReview papers
dc.subjectSearch mechanism
dc.subjectSearching behavior
dc.subjectState of the art
dc.subjectHeuristic algorithms
dc.titleNature-Inspired Heuristic Frameworks Trends in Solving Multi-objective Engineering Optimization Problemsen_US
dc.typeReviewen_US
dspace.entity.typePublication
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