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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
Published: Pushpa Publishing House 2018
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://repo.uum.edu.my/27873/
http://doi.org/10.17654/MS103010171
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spelling my.uum.repo.278732020-11-11T06:02:24Z http://repo.uum.edu.my/27873/ 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 QA75 Electronic computers. Computer science 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 delete-and-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 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 09720871 http://doi.org/10.17654/MS103010171 doi:10.17654/MS103010171
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
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 delete-and-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://repo.uum.edu.my/27873/
http://doi.org/10.17654/MS103010171
_version_ 1684655811818684416
score 13.211869

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