Global optimal analysis of variant genetic operations in solar tracking

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)6-14
Number of pages9
JournalAustralian Journal of Basic and Applied Sciences
Volume6
Issue number6
Publication statusPublished - 01 Jun 2012

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Genetic algorithms
Evolutionary algorithms
Genetic programming
Axles
Solar radiation
Sun
Mathematical operators
Tuning
Hardware

All Science Journal Classification (ASJC) codes

  • General

Cite this

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title = "Global optimal analysis of variant genetic operations in solar tracking",
abstract = "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.",
author = "Fam, {D. F.} and Koh, {Johnny Siaw Paw} and Tiong, {Sieh Kiong} and Chong, {Kok Hen}",
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