Kannan, S.; Diwekar, U. An Enhanced Particle Swarm Optimization (PSO) Employing Quasi-Random Numbers. Preprints2024, 2024030944. https://doi.org/10.20944/preprints202403.0944.v1
APA Style
Kannan, S., & Diwekar, U. (2024). An Enhanced Particle Swarm Optimization (PSO) Employing Quasi-Random Numbers. Preprints. https://doi.org/10.20944/preprints202403.0944.v1
Chicago/Turabian Style
Kannan, S. and Urmila Diwekar. 2024 "An Enhanced Particle Swarm Optimization (PSO) Employing Quasi-Random Numbers" Preprints. https://doi.org/10.20944/preprints202403.0944.v1
Abstract
This paper introduces an innovative Particle Swarm Optimization (PSO) Algorithm in-corporating Sobol and Halton Random number samplings. It evaluates the enhanced PSO's performance against conventional PSO employing Monte Carlo Random Number Samplings. The comparison involves assessing the algorithms across nine benchmark problems and the renowned Travelling Salesman Problem (TSP). The re-sults reveal consistent enhancements achieved by the enhanced PSO utilizing Sob-ol/Halton samplings across the benchmark problems. Particularly noteworthy are the substantial improvements demonstrated by the enhanced PSO in solving the Travel-ling Salesman Problem. These findings underscore the efficacy of employing Sobol and Halton random number generation methods in enhancing algorithm efficiency.
Keywords
Enhanced PSO; SOBOL; Halton; Quasi-random numbers
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
