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Malaysian menu planning model using self-adaptive hybrid genetic algorithm (SHGA)

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

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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.

Item Type: Article
Uncontrolled Keywords: Hybrid genetic algorithms; local search; heuristics; menu planning problem
Subjects: Q Science > QA Mathematics > QA273 Probabilities. Mathematical statistics
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 May 2019 01:12
Last Modified: 13 May 2019 01:12
URI: http://eprints.uthm.edu.my/id/eprint/11164
Statistic Details: View Download Statistic

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