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Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA)

The aim of this research is to propose a self-adaptive hybrid genetic algorithm (SHGA) approach to solve Malaysian menu planning problem for adolescents aged 13 to 18 years old. We developed Malaysian menu planning model with the objectives to optimize the budget allocation for each student, maximiz...

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Main Authors:	Mohd Razali, Siti Noor Asyikin, Engku Abu Bakar, Engku Muhammad Nazri, Ku Mahamud, Ku Ruhana, Arbin, Norazman, Rusiman, Mohd Saifullah
Format:	Article
Language:	English
Published:	Pushpa Publishing House 2018
Subjects:	QA Mathematics QA75 Electronic computers. Computer science T Technology (General) QA273-280 Probabilities. Mathematical statistics
Online Access:	http://eprints.uthm.edu.my/4865/1/AJ%202018%20%28130%29.pdf http://eprints.uthm.edu.my/4865/ http://dx.doi.org/10.17654/MS103010171
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id	my.uthm.eprints.4865
record_format	eprints
spelling	my.uthm.eprints.48652021-12-23T04:01:41Z http://eprints.uthm.edu.my/4865/ Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA) Mohd Razali, Siti Noor Asyikin Engku Abu Bakar, Engku Muhammad Nazri Ku Mahamud, Ku Ruhana Arbin, Norazman Rusiman, Mohd Saifullah QA Mathematics QA75 Electronic computers. Computer science T Technology (General) QA273-280 Probabilities. Mathematical statistics The aim of this research is to propose a self-adaptive hybrid genetic algorithm (SHGA) approach to solve Malaysian menu planning problem for adolescents aged 13 to 18 years old. We developed Malaysian menu planning model with the objectives to optimize the budget allocation for each student, maximize the variety of daily meals, maximize the caterer’s ability, accomplish meals course structures and fulfill the standard recommended nutrient intake (RNI). Two new novel local searches are introduced in this study that combined the insertion search (IS) and insertion search with deleteand-create (ISDC) methods. Application of IS itself could not guarantee the production of feasible solutions as it only searches in a small neighborhood area. Thus, ISDC is proposed to enhance the search towards a large neighborhood area and the results indicated that the proposed algorithm is able to produce 100% feasible solutions with the best fitness value. Besides that, the application of self-adaptive probability for mutation is significantly minimizing computational time taken to generate the good solutions in just few minutes. Hybridization technique of two local search methods and self-adaptive strategy has successfully improved the performance of traditional genetic algorithm through balanced exploitation and exploration scheme Pushpa Publishing House 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/4865/1/AJ%202018%20%28130%29.pdf Mohd Razali, Siti Noor Asyikin and Engku Abu Bakar, Engku Muhammad Nazri and Ku Mahamud, Ku Ruhana and Arbin, Norazman and Rusiman, Mohd Saifullah (2018) Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA). Far East Journal of Mathematical Sciences (FJMS), 103 (1). pp. 171-190. ISSN 0972-0871 http://dx.doi.org/10.17654/MS103010171
institution	Universiti Tun Hussein Onn Malaysia
building	UTHM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tun Hussein Onn Malaysia
content_source	UTHM Institutional Repository
url_provider	http://eprints.uthm.edu.my/
language	English
topic	QA Mathematics QA75 Electronic computers. Computer science T Technology (General) QA273-280 Probabilities. Mathematical statistics
spellingShingle	QA Mathematics QA75 Electronic computers. Computer science T Technology (General) QA273-280 Probabilities. Mathematical statistics Mohd Razali, Siti Noor Asyikin Engku Abu Bakar, Engku Muhammad Nazri Ku Mahamud, Ku Ruhana Arbin, Norazman Rusiman, Mohd Saifullah Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA)
description	The aim of this research is to propose a self-adaptive hybrid genetic algorithm (SHGA) approach to solve Malaysian menu planning problem for adolescents aged 13 to 18 years old. We developed Malaysian menu planning model with the objectives to optimize the budget allocation for each student, maximize the variety of daily meals, maximize the caterer’s ability, accomplish meals course structures and fulfill the standard recommended nutrient intake (RNI). Two new novel local searches are introduced in this study that combined the insertion search (IS) and insertion search with deleteand-create (ISDC) methods. Application of IS itself could not guarantee the production of feasible solutions as it only searches in a small neighborhood area. Thus, ISDC is proposed to enhance the search towards a large neighborhood area and the results indicated that the proposed algorithm is able to produce 100% feasible solutions with the best fitness value. Besides that, the application of self-adaptive probability for mutation is significantly minimizing computational time taken to generate the good solutions in just few minutes. Hybridization technique of two local search methods and self-adaptive strategy has successfully improved the performance of traditional genetic algorithm through balanced exploitation and exploration scheme
format	Article
author	Mohd Razali, Siti Noor Asyikin Engku Abu Bakar, Engku Muhammad Nazri Ku Mahamud, Ku Ruhana Arbin, Norazman Rusiman, Mohd Saifullah
author_facet	Mohd Razali, Siti Noor Asyikin Engku Abu Bakar, Engku Muhammad Nazri Ku Mahamud, Ku Ruhana Arbin, Norazman Rusiman, Mohd Saifullah
author_sort	Mohd Razali, Siti Noor Asyikin
title	Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA)
title_short	Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA)
title_full	Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA)
title_fullStr	Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA)
title_full_unstemmed	Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA)
title_sort	malaysian menu planning model using self-adaptive hybrid genetic algorithm (shga)
publisher	Pushpa Publishing House
publishDate	2018
url	http://eprints.uthm.edu.my/4865/1/AJ%202018%20%28130%29.pdf http://eprints.uthm.edu.my/4865/ http://dx.doi.org/10.17654/MS103010171
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score	13.211869

Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA)

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