Optimizing Nutritional Decisions: A Particle Swarm Optimization–Simulated Annealing-Enhanced Analytic Hierarchy Process Approach for Personalized Meal Planning.
Background/Objective: Nutritionists play a crucial role in guiding individuals toward healthier lifestyles through personalized meal planning; however, this task involves navigating a complex web of factors, including health conditions, dietary restrictions, cultural preferences, and socioeconomic c...
| Publicado en: | Nutrients Vol. 16; no. 18; pp. 3117 - 3135 |
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| Autores principales: | , , |
| Formato: | algorithm computer program equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Sep2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179966221&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179966221 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726643 B0TT jtl: Nutrients issn: 20726643 maglogo: N pubinfo: dt: Sep2024 vid: 16 iid: 18 pid: 97109 pub: MDPI artinfo: ui: 179966221 179966221 179966221 10.3390/nu16183117 179966221 ppf: 3117 ppct: 18 formats: tig: atl: Optimizing Nutritional Decisions: A Particle Swarm Optimization–Simulated Annealing-Enhanced Analytic Hierarchy Process Approach for Personalized Meal Planning. aug: au: Sarani Rad, Fatemeh Amiri, Maryam Li, Juan affil: Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA sug: subj: Particle Swarm Optimization Alternative Health Personnel Analytic Hierarchy Process Nutritionists Nutrition Decision Making, Clinical Patient Preference Diet Human Precision Meals Needs Assessment Mobile Applications Treatment Outcomes Funding Source ab: Background/Objective: Nutritionists play a crucial role in guiding individuals toward healthier lifestyles through personalized meal planning; however, this task involves navigating a complex web of factors, including health conditions, dietary restrictions, cultural preferences, and socioeconomic constraints. The Analytic Hierarchy Process (AHP) offers a valuable framework for structuring these multi-faceted decisions but inconsistencies can hinder its effectiveness in pairwise comparisons. Methods: This paper proposes a novel hybrid Particle Swarm Optimization–Simulated Annealing (PSO-SA) algorithm to refine inconsistent AHP weight matrices, ensuring a consistent and accurate representation of the nutritionist's expertise and client preferences. Our approach merges PSO's global search capabilities with SA's local search precision, striking an optimal balance between exploration and exploitation. Results: We demonstrate the practical utility of our algorithm through real-world use cases involving personalized meal planning for individuals with specific dietary needs and preferences. Results showcase the algorithm's efficiency in achieving consistency and surpassing standard PSO accuracy. Conclusion: By integrating the PSO-SA algorithm into a mobile app, we empower nutritionists with an advanced decision-making tool for creating tailored meal plans that promote healthier dietary choices and improved client outcomes. This research represents a significant advancement in multi-criteria decision-making for nutrition, offering a robust solution to the inconsistency challenge in AHP and paving the way for more effective and personalized dietary interventions. pubtype: Academic Journal doctype: algorithm computer program equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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