Paper Info
Title
Solving Dietary Planning Problem using Particle Swarm Optimization with Genetic Operators
Abstract
Dietary planning problem is considered as Multi-dimensional Knapsack Problem and confirmed to be a NP-hard problem. There are different ways on how to generate a dietary plan and it includes different constraints such as having a variety of foods, meeting the required total calories, satisfying different nutrients and others. Particle swarm optimization is a promising method to solve different kinds of optimization problem due to its fast convergence, few parameters needed and ability to find good solutions to the problem. PSO using constriction coefficient method was applied in this study and genetic operators were integrated to explore the search space and improved the quality of the solution. Experimental results show that the proposed algorithm was able to generate a varied diet plans for adults wherein it satisfies the specified constraints and PSO with genetic operators was able to evolve better solutions compare to original PSO.
Year
DOI
Venue
2019
10.1145/3310986.3311005
Proceedings of the 3rd International Conference on Machine Learning and Soft Computing
Keywords
DocType
ISBN
Particle swarm optimization, constriction coefficient, dietary planning, genetic operators
Conference
978-1-4503-6612-0
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Edmarlyn M. Porras100.34
Arnel C. Fajardo200.68
Ruji P. Medina315.79