UM
Meta-zoo-heuristic Algorithms
Fong, Simon; Ariwa, E; Pichappan, P
2017
Conference Name2017 SEVENTH INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING TECHNOLOGY (INTECH 2017)
Pages3-8
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractMetaheuristic algorithms (MH's) are referred to algorithms which has a two-level design 'meta' is upper-level procedure that controls the underlying 'heuristic' which learns and improves a solution iteratively until a sufficiently good solution is obtained for an optimization problem. Since 2008, MH's started to receive attention from researchers around the globe. Variants and new species of MH algorithms emerged. Most of them are claimed to be inspired from the nature or biology. The logics of the search algorithms are mimicked front animal behaviors or nature phenomenon. However, the necessity for creating more new species of such algorithms is doubted. Instead of inventing extra MH's which are similar to one another, we start to ponder if several classical MH's can be used together or in an ensemble. In this paper, the possibilities of putting several MH's into an ensemble are discussed. Different from ensemble in machine learning, we coin this unique collection of Mil's which may fuse together or function cooperatively in solving optimization problems, 'meta-zoo-heuristic'. The term 'zoo' here simply means that the selected MH's are to be kept under control. A preliminary simulation test is conducted, which demonstrates how suitable MH's are selected for a specific problem to solve.
KeywordKeywords Meta-heuristics bio-inspired optimization algorithms ensemble optimization
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS IDWOS:000425919500002
The Source to ArticleWOS
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
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GB/T 7714
Fong, Simon,Ariwa, E,Pichappan, P. Meta-zoo-heuristic Algorithms[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:3-8.
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