Hybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet

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dc.citation.epage350
dc.citation.issue2
dc.citation.journalTitleМатематичне моделювання та комп'ютинг
dc.citation.spage338
dc.contributor.affiliationUSMBA Феса
dc.contributor.affiliationCAU Марракеша
dc.contributor.affiliationUCA Марракеша
dc.contributor.affiliationUSMBA of Fez
dc.contributor.affiliationCAU of Marrakech
dc.contributor.affiliationUCA of Marrakech
dc.contributor.authorЕль Мутауакіл, К.
dc.contributor.authorАхураг, А.
dc.contributor.authorЧакір, С.
dc.contributor.authorКаббадж, З.
dc.contributor.authorЧеллак, С.
dc.contributor.authorЧеггур, М.
dc.contributor.authorБайзр, Х.
dc.contributor.authorEl Moutaouakil, K.
dc.contributor.authorAhourag, A.
dc.contributor.authorChakir, S.
dc.contributor.authorKabbaj, Z.
dc.contributor.authorChellack, S.
dc.contributor.authorCheggour, M.
dc.contributor.authorBaizri, H.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-03-04T10:28:17Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractУ цій статті розв’язується марокканська проблема денного раціону на основі 6 оптимізаційних програм (P) з урахуванням дієтичних рекомендацій Міністерства охорони здоров’я, соціальних служб і Міністерства сільського господарства США. Цільова функція контролює нечітке глікемічне навантаження, сприятливий дефіцит поживних речовин і несприятливий надлишок поживних речовин. Для перетворення запропонованої програми в лінійне рівняння використовується інтегральна функція нечіткого ранжування. Для вирішення отриманої моделі використовуємо гібридний генетичний алгоритм світлячка (HFGA), який поєднує деякі переваги алгоритму світлячка (FA) і генетичного алгоритму (GA). Запропонована модель створює найкращі та загальні дієти з прийнятним глікемічним навантаженням і прийнятним дефіцитом основних поживних речовин. Крім того, запропонована модель показала дивовижну узгодженість з рівномірним розподілом глікемічного навантаження різних харчових продуктів.
dc.description.abstractIn this paper, we solve the Moroccan daily diet problem based on 6 optimization programming (P) taking into account dietary guidelines of US department of health, human services, and department of agriculture. The objective function controls the fuzzy glycemic load, the favorable nutrients gap, and unfavorable nutrient excess. To transform the proposed program into a line equation, we use the integral fuzzy ranking function. To solve the obtained model, we use the Hybrid Firefly Genetic Algorithm (HFGA) that combines some advantages of the Firefly Algorithm (FA) and the Genetic Algorithm (GA). The proposed model produces the best and generic diets with reasonable glycemic loads and acceptable core nutrient deficiencies. In addition, the proposed model showed remarkable consistency with the uniform distribution of glycemic load of different foods.
dc.format.extent338-350
dc.format.pages13
dc.identifier.citationHybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet / K. El Moutaouakil, A. Ahourag, S. Chakir, Z. Kabbaj, S. Chellack, M. Cheggour, H. Baizri // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 10. — No 2. — P. 338–350.
dc.identifier.citationenHybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet / K. El Moutaouakil, A. Ahourag, S. Chakir, Z. Kabbaj, S. Chellack, M. Cheggour, H. Baizri // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 10. — No 2. — P. 338–350.
dc.identifier.doi10.23939/mmc2023.02.338
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/63427
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofМатематичне моделювання та комп'ютинг, 2 (10), 2023
dc.relation.ispartofMathematical Modeling and Computing, 2 (10), 2023
dc.relation.references[1] Aswani A., Kaminsky P., Mintz Y., Flowers E., Fukuoka Y. Behavioral modeling in weight loss interventions. European Journal of Operational Research. 272 (3), 1058–1072 (2019).
dc.relation.references[2] Chen X.-C., Wei T.-R., Guo J.-H., Zhou W. X., Song A., Chen W.-N., Zhang J. Multi-objectives Evolutionary Algorithm with Double-level Archives for Nutrition-al Dietary Decision Problem. 2019 9th International Conference on Information Science and Technology (ICIST). 419–426 (2019).
dc.relation.references[3] Namany S., Al-Ansari T., Govindan R. Optimisation of the energy, water, and food nexus for food security scenarios. Computers & Chemical Engineering. 129, 106513 (2019).
dc.relation.references[4] Leung C. W., Epel E. S., Ritchie L. D., Crawford P. B., Laraia B. A. Food insecurity is inversely associated with diet quality of lower-income adults. Research and Professional Briefs. 114 (12), 1943–1953 (2014).
dc.relation.references[5] Abdellatif E. O., Karim E. M., Hicham B., Saliha C. Intelligent Local Search for an Optimal Control of Diabetic Population Dynamics. Mathematical Models and Computer Simulations. 14 (6), 1051–1071 (2022).
dc.relation.references[6] Zervoudakis K., Tsafarakis S., Paraskevi–Panagiota S. A New Hybrid Firefly – Genetic Algorithm for the Optimal Product Line Design Problem. International Conference on Learning and Intelligent Optimization. 284–297 (2019).
dc.relation.references[7] Dantzig G. Linear Programming and Extensions. Princeton University Press (2016).
dc.relation.references[8] Oreˇskovi´c P., Kljusuri´c J. G., Satali´c Z. Computer-generated vegan menus: The importance of food composition database choice. Journal of Food Composition and Analysis. 37, 112–118 (2015).
dc.relation.references[9] Masset G., Monsivais P., Maillot M., Darmon N., Drewnowski A. Diet optimization methods can help translate dietary guidelines into a cancer prevention food plan. The Journal of Nutrition. 139 (8), 1541–1548 (2009).
dc.relation.references[10] Donati M., Menozzi D., Zighetti C., Rosi A., Zinetti A., Scazzina F. Towards a sustainable diet combining economic, environmental and nutritional objectives. Appetite. 106, 48–57 (2016).
dc.relation.references[11] Farrokhi A., Farahbakhsh R., Rezazadeh J., Minerva R. Application of Internet of Things and artificial intelligence for smart fitness: A survey. Computer Networks. 189, 107859 (2021).
dc.relation.references[12] Van Mierlo K., Rohmer S., Gerdessen J. C. A model for composing meat replacers: Reducing the environmental impact of our food consumption pattern while retaining its nutritional value. Journal of Cleaner Production. 165, 930–950 (2017).
dc.relation.references[13] Taniguchi E. Concepts of city logistics for sustainable and liveable cities. Procedia – Social and Behavioral Sciences. 151, 310–317 (2014).
dc.relation.references[14] Ahourag A., Moutaouakil K. E., Chellak S., Baizri H., Cheggour M. Multi-criteria optimization for optimal nutrition of Moroccan diabetics. 2022 International Conference on Intelligent Systems and Computer Vision (ISCV). 1–6 (2022).
dc.relation.references[15] Bas E. A robust optimization approach to diet problem with overall glycemic load as objective function. Applied Mathematical Modelling. 38 (19–20), 4926–4940 (2014).
dc.relation.references[16] Xie W. Tractable reformulations of two-stage distributionally robust linear programs over the type -infinity Wasserstein ball. Operations Research Letters. 48 (4), 513–523 (2020).
dc.relation.references[17] El Moutaouakil K., El Ouissari A. Density based fuzzy support vector machine: application to diabetes dataset. Mathematical Modeling and Computing. 8 (4), 747–760 (2020).
dc.relation.references[18] Javanmard M., Nehi H. M. A solving method for fuzzy linear programming problem with interval type-2 fuzzy numbers. International Journal of Fuzzy Systems. 21, 882–891 (2019).
dc.relation.references[19] Bas E. A three-step methodology for GI classification, GL estimation of foods by fuzzy c-means classification and fuzzy pattern recognition, and an LP-based diet model for glycaemic control. Food Research International. 83, 1–13 (2016).
dc.relation.references[20] El Moutaouakil K., Cheggour M., Chellak S., Baizri H. Metaheuristics Optimization Algorithm to an Optimal Moroccan Diet. 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC). 364–368 (2021).
dc.relation.references[21] Liou T.-S., Wang M.-J. J. Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems. 50, 247–255 (1992).
dc.relation.references[22] Kaufmann A., Gupta M. M. Introduction to Fuzzy Arithmetic Theory and Applications. Van Nostrand Reinhold, New York (1985).
dc.relation.references[23] El Moutaouakil K., Touhafi A. A New Recurrent Neural Network Fuzzy Mean Square Clustering Method. 2020 5th International Conference on Cloud Computing and Artifi-cial Intelligence: Technologies and Applications (CloudTech). 1–5 (2020).
dc.relation.references[24] You A. Dietary guidelines for Americans. US Department of Health and Human Services and US Department of Agriculture (2015).
dc.relation.references[25] Fister I., Fister I. Jr., Yang X. S., Brest J. A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation. 13, 34–46 (2013).
dc.relation.references[26] Goldberg D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison–Wesley, Reading (1989).
dc.relation.references[27] Sivanandam S. N., Deepa S. N. Principles of Soft Computing. New Delhi, Wiley (2013).
dc.relation.references[28] Farook S., Raju P. S. Evolutionary hybrid genetic-firefly algorithm for global optimization. International Journal of Computational Engineering & Management. 16 (3), 37–45 (2013).
dc.relation.references[29] Mazen F., AbulSeoud R. A., Gody A. M. Genetic algorithm and firefly algorithm in a hybrid approach for breast cancer diagnosis. International Journal of Computer Trends and Technology. 32 (1), 62–68 (2016).
dc.relation.references[30] Humayun M. A., Elango R., Ball R. O., Pencharz P. B. Reevaluation of the protein requirement in young men with the indicator amino acid oxidation technique. The American Journal of Clinical Nutrition. 86 (4), 995–1002 (2007).
dc.relation.references[31] Verma H., Garg R. Effect of magnesium supplementation on type 2 diabetes asso-ciated cardiovascular risk factors: a systematic review and meta-analysis. Journal of Human Nutrition and Dietetics. 30, 621–633 (2017).
dc.relation.referencesen[1] Aswani A., Kaminsky P., Mintz Y., Flowers E., Fukuoka Y. Behavioral modeling in weight loss interventions. European Journal of Operational Research. 272 (3), 1058–1072 (2019).
dc.relation.referencesen[2] Chen X.-C., Wei T.-R., Guo J.-H., Zhou W. X., Song A., Chen W.-N., Zhang J. Multi-objectives Evolutionary Algorithm with Double-level Archives for Nutrition-al Dietary Decision Problem. 2019 9th International Conference on Information Science and Technology (ICIST). 419–426 (2019).
dc.relation.referencesen[3] Namany S., Al-Ansari T., Govindan R. Optimisation of the energy, water, and food nexus for food security scenarios. Computers & Chemical Engineering. 129, 106513 (2019).
dc.relation.referencesen[4] Leung C. W., Epel E. S., Ritchie L. D., Crawford P. B., Laraia B. A. Food insecurity is inversely associated with diet quality of lower-income adults. Research and Professional Briefs. 114 (12), 1943–1953 (2014).
dc.relation.referencesen[5] Abdellatif E. O., Karim E. M., Hicham B., Saliha C. Intelligent Local Search for an Optimal Control of Diabetic Population Dynamics. Mathematical Models and Computer Simulations. 14 (6), 1051–1071 (2022).
dc.relation.referencesen[6] Zervoudakis K., Tsafarakis S., Paraskevi–Panagiota S. A New Hybrid Firefly – Genetic Algorithm for the Optimal Product Line Design Problem. International Conference on Learning and Intelligent Optimization. 284–297 (2019).
dc.relation.referencesen[7] Dantzig G. Linear Programming and Extensions. Princeton University Press (2016).
dc.relation.referencesen[8] Oreˇskovi´c P., Kljusuri´c J. G., Satali´c Z. Computer-generated vegan menus: The importance of food composition database choice. Journal of Food Composition and Analysis. 37, 112–118 (2015).
dc.relation.referencesen[9] Masset G., Monsivais P., Maillot M., Darmon N., Drewnowski A. Diet optimization methods can help translate dietary guidelines into a cancer prevention food plan. The Journal of Nutrition. 139 (8), 1541–1548 (2009).
dc.relation.referencesen[10] Donati M., Menozzi D., Zighetti C., Rosi A., Zinetti A., Scazzina F. Towards a sustainable diet combining economic, environmental and nutritional objectives. Appetite. 106, 48–57 (2016).
dc.relation.referencesen[11] Farrokhi A., Farahbakhsh R., Rezazadeh J., Minerva R. Application of Internet of Things and artificial intelligence for smart fitness: A survey. Computer Networks. 189, 107859 (2021).
dc.relation.referencesen[12] Van Mierlo K., Rohmer S., Gerdessen J. C. A model for composing meat replacers: Reducing the environmental impact of our food consumption pattern while retaining its nutritional value. Journal of Cleaner Production. 165, 930–950 (2017).
dc.relation.referencesen[13] Taniguchi E. Concepts of city logistics for sustainable and liveable cities. Procedia – Social and Behavioral Sciences. 151, 310–317 (2014).
dc.relation.referencesen[14] Ahourag A., Moutaouakil K. E., Chellak S., Baizri H., Cheggour M. Multi-criteria optimization for optimal nutrition of Moroccan diabetics. 2022 International Conference on Intelligent Systems and Computer Vision (ISCV). 1–6 (2022).
dc.relation.referencesen[15] Bas E. A robust optimization approach to diet problem with overall glycemic load as objective function. Applied Mathematical Modelling. 38 (19–20), 4926–4940 (2014).
dc.relation.referencesen[16] Xie W. Tractable reformulations of two-stage distributionally robust linear programs over the type -infinity Wasserstein ball. Operations Research Letters. 48 (4), 513–523 (2020).
dc.relation.referencesen[17] El Moutaouakil K., El Ouissari A. Density based fuzzy support vector machine: application to diabetes dataset. Mathematical Modeling and Computing. 8 (4), 747–760 (2020).
dc.relation.referencesen[18] Javanmard M., Nehi H. M. A solving method for fuzzy linear programming problem with interval type-2 fuzzy numbers. International Journal of Fuzzy Systems. 21, 882–891 (2019).
dc.relation.referencesen[19] Bas E. A three-step methodology for GI classification, GL estimation of foods by fuzzy c-means classification and fuzzy pattern recognition, and an LP-based diet model for glycaemic control. Food Research International. 83, 1–13 (2016).
dc.relation.referencesen[20] El Moutaouakil K., Cheggour M., Chellak S., Baizri H. Metaheuristics Optimization Algorithm to an Optimal Moroccan Diet. 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC). 364–368 (2021).
dc.relation.referencesen[21] Liou T.-S., Wang M.-J. J. Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems. 50, 247–255 (1992).
dc.relation.referencesen[22] Kaufmann A., Gupta M. M. Introduction to Fuzzy Arithmetic Theory and Applications. Van Nostrand Reinhold, New York (1985).
dc.relation.referencesen[23] El Moutaouakil K., Touhafi A. A New Recurrent Neural Network Fuzzy Mean Square Clustering Method. 2020 5th International Conference on Cloud Computing and Artifi-cial Intelligence: Technologies and Applications (CloudTech). 1–5 (2020).
dc.relation.referencesen[24] You A. Dietary guidelines for Americans. US Department of Health and Human Services and US Department of Agriculture (2015).
dc.relation.referencesen[25] Fister I., Fister I. Jr., Yang X. S., Brest J. A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation. 13, 34–46 (2013).
dc.relation.referencesen[26] Goldberg D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison–Wesley, Reading (1989).
dc.relation.referencesen[27] Sivanandam S. N., Deepa S. N. Principles of Soft Computing. New Delhi, Wiley (2013).
dc.relation.referencesen[28] Farook S., Raju P. S. Evolutionary hybrid genetic-firefly algorithm for global optimization. International Journal of Computational Engineering & Management. 16 (3), 37–45 (2013).
dc.relation.referencesen[29] Mazen F., AbulSeoud R. A., Gody A. M. Genetic algorithm and firefly algorithm in a hybrid approach for breast cancer diagnosis. International Journal of Computer Trends and Technology. 32 (1), 62–68 (2016).
dc.relation.referencesen[30] Humayun M. A., Elango R., Ball R. O., Pencharz P. B. Reevaluation of the protein requirement in young men with the indicator amino acid oxidation technique. The American Journal of Clinical Nutrition. 86 (4), 995–1002 (2007).
dc.relation.referencesen[31] Verma H., Garg R. Effect of magnesium supplementation on type 2 diabetes asso-ciated cardiovascular risk factors: a systematic review and meta-analysis. Journal of Human Nutrition and Dietetics. 30, 621–633 (2017).
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.subjectоптимальна марокканська дієта
dc.subjectнечітке квадратичне програмування
dc.subjectтрикутні нечіткі числа
dc.subjectінтегральне ранжування більшості функцій
dc.subjectгенетичний алгоритм
dc.subjectалгоритм світлячка
dc.subjectoptimal Moroccan diet
dc.subjectfuzzy quadratic programming
dc.subjecttriangular fuzzy numbers
dc.subjectintegral ranking most function
dc.subjectgenetic algorithm
dc.subjectfirefly algorithm
dc.titleHybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet
dc.title.alternativeГібридний генетичний алгоритм світлячка та інтегральне нечітке квадратичне програмування для оптимальної марокканської дієти
dc.typeArticle

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Mathematical Modeling And Computing. – 2023. – Vol. 10, No. 2