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Global optimal analysis of variant genetic operations in solar tracking

Genetic 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...

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Main Authors: Fam, D.F., Koh, S.P., Tiong, S.K., Chong, K.H.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-58102018-01-03T07:10:15Z Global optimal analysis of variant genetic operations in solar tracking Fam, D.F. Koh, S.P. Tiong, S.K. Chong, K.H. Genetic 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. 2017-12-08T07:26:22Z 2017-12-08T07:26:22Z 2012 Article https://www.scopus.com/record/display.uri?eid=2-s2.0-84867159019&origin=resultslist&sort=plf-f&src=s&sid=89707673262aad64745c9f4897b7fdaa&sot en_US Australian Journal of Basic and Applied Sciences Volume 6, Issue 6, June 2012, Pages 6-14
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language en_US
description Genetic 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.
format Article
author Fam, D.F.
Koh, S.P.
Tiong, S.K.
Chong, K.H.
spellingShingle Fam, D.F.
Koh, S.P.
Tiong, S.K.
Chong, K.H.
Global optimal analysis of variant genetic operations in solar tracking
author_facet Fam, D.F.
Koh, S.P.
Tiong, S.K.
Chong, K.H.
author_sort Fam, D.F.
title Global optimal analysis of variant genetic operations in solar tracking
title_short Global optimal analysis of variant genetic operations in solar tracking
title_full Global optimal analysis of variant genetic operations in solar tracking
title_fullStr Global optimal analysis of variant genetic operations in solar tracking
title_full_unstemmed Global optimal analysis of variant genetic operations in solar tracking
title_sort global optimal analysis of variant genetic operations in solar tracking
publishDate 2017
_version_ 1644493781512224768
score 13.211869

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