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Optimizing Nutritional Decisions: A Particle Swarm Optimization-Simulated Annealing-Enhanced Analytic Hierarchy

Fatemeh Sarani Rad¹, Maryam Amiri¹, Juan Li¹

¹Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA.

|September 28, 2024

Summary

This study introduces a new hybrid algorithm (PSO-SA) to improve personalized meal planning by refining decision-making in nutrition. It enhances the accuracy of the Analytic Hierarchy Process (AHP) for better dietary guidance.

Keywords:

analytic hierarchy process meal planning multi-criteria decision-making nutritional counseling particle swarm optimization personalized nutrition simulated annealing

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Area of Science:

Decision Sciences
Nutritional Science
Computational Intelligence

Background:

Personalized meal planning is complex, involving health, dietary, cultural, and economic factors.
The Analytic Hierarchy Process (AHP) aids decision-making but struggles with inconsistent pairwise comparisons.

Purpose of the Study:

To develop a novel algorithm for refining inconsistent AHP weight matrices in nutrition.
To enhance the accuracy and consistency of personalized meal planning decisions.

Main Methods:

A hybrid Particle Swarm Optimization-Simulated Annealing (PSO-SA) algorithm was developed.
The algorithm combines global search (PSO) with local search (SA) for optimal balance.
It refines inconsistent AHP weight matrices for improved decision accuracy.

Main Results:

The PSO-SA algorithm demonstrated practical utility in real-world meal planning scenarios.
It efficiently achieved consistency in AHP matrices.
The algorithm surpassed the accuracy of standard Particle Swarm Optimization (PSO).

Conclusions:

The PSO-SA algorithm offers a robust solution for AHP inconsistency in multi-criteria decision-making for nutrition.
Integration into a mobile app empowers nutritionists with advanced decision support.
This advances personalized dietary interventions for improved client health outcomes.