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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Pushpa Publishing House
2018
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| Online Access: | http://eprints.uthm.edu.my/4865/ http://eprints.uthm.edu.my/4865/1/AJ%202018%20%28130%29.pdf |
| _version_ | 1848888400997777408 |
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| 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 |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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 |
| first_indexed | 2025-11-15T20:09:41Z |
| format | Article |
| id | uthm-4865 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:09:41Z |
| publishDate | 2018 |
| publisher | Pushpa Publishing House |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-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 |
| 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) |
| title | 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_short | Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA) |
| title_sort | malaysian menu planning model using self-adaptive hybrid genetic algorithm (shga) |
| topic | QA Mathematics QA75 Electronic computers. Computer science T Technology (General) QA273-280 Probabilities. Mathematical statistics |
| url | http://eprints.uthm.edu.my/4865/ http://eprints.uthm.edu.my/4865/ http://eprints.uthm.edu.my/4865/1/AJ%202018%20%28130%29.pdf |