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Global optimal analysis of variant genetic operations in solar tracking

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dc.citedby0
dc.contributor.authorFam D.F.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorChong K.H.en_US
dc.contributor.authorid43361142100en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid36994481200en_US
dc.date.accessioned2023-12-29T07:45:53Z
dc.date.available2023-12-29T07:45:53Z
dc.date.issued2012
dc.description.abstractGenetic Algorithms (GAs), Evolution Strategies (ES), Evolutionary Programming (EP) and Genetic Programming (GP) are some of the best known types of Evolutionary Algorithm (EA)where it is a class of global search algorithms inspired by natural evolution. Lots of research has been carried out in solar tracking system using different types of Evolutionary Algorithm. In this research, genetic algorithm is explored to maximize the performance of solar tracking system. This work evaluates the best combination of GA parameters by always fine-tuning the position of solar tracking prototype to receive maximum solar radiation. Both software and hardware have been developed to simulate related genetic algorithm results using a combination of variant genetic operators. Under conventional genetic algorithm operation, it is concluded that genetic algorithm with selective clonal mutation is able to produce the best fitness value at 0.98027 with both axles X and Y with inclination of +2 degree to the sun position.en_US
dc.description.natureFinalen_US
dc.identifier.epage14
dc.identifier.issue6
dc.identifier.scopus2-s2.0-84867159019
dc.identifier.spage6
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84867159019&partnerID=40&md5=3eb3c98d20f1a7c949a27fb963834644
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30246
dc.identifier.volume6
dc.pagecount8
dc.sourceScopus
dc.sourcetitleAustralian Journal of Basic and Applied Sciences
dc.subjectGenetic algorithm
dc.subjectSelective clonal mutation (SCM)
dc.subjectSolar tracking
dc.titleGlobal optimal analysis of variant genetic operations in solar trackingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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