AI-ENABLED MEAL PLANNING: BALANCING NUTRITIONAL NEEDS AND FOOD PREFERENCES THROUGH OPTIMIZATION ALGORITHMS

Abstract

In the evolving landscape of personalized nutrition, AI-enabled meal planning represents a transformative approach to optimizing dietary choices by balancing nutritional needs with individual food preferences. This research presents a novel framework for AI-powered meal planning, leveraging advanced optimization algorithms to address the challenge of aligning dietary requirements with personal taste. Our approach integrates various data sources, including nutritional databases, individual health profiles, and food preference surveys, to generate customized meal plans that promote health while accommodating user preferences. The core of our methodology involves the application of multi-objective optimization algorithms, such as Genetic Algorithms and Particle Swarm Optimization, to efficiently explore the vast solution space of potential meal combinations. These algorithms are designed to handle the complex constraints associated with dietary restrictions, nutrient balance, and personal taste preferences. The optimization process also incorporates machine learning techniques to refine recommendations based on feedback and evolving dietary needs. Our framework was validated through a series of experiments with diverse user profiles, demonstrating its effectiveness in creating balanced meal plans that are both nutritionally adequate and tailored to individual tastes. Results indicate significant improvements in user satisfaction and adherence to dietary guidelines compared to traditional meal planning methods. This research contributes to the field of health informatics by providing a robust AI-based solution for personalized meal planning, with implications for improving dietary habits and enhancing overall well-being.