Version 1
: Received: 14 August 2024 / Approved: 15 August 2024 / Online: 16 August 2024 (08:10:08 CEST)
How to cite:
Martínez-Guerrero, E.; Eulogio, P. L.; Miranda-Romagnoli, P.; Noriega-Papaqui, R.; Seck-Tuoh-Mora, J. C. HE-DEPSO Algorithm for Textures Optimization with Application in Particle Physics. Preprints2024, 2024081171. https://doi.org/10.20944/preprints202408.1171.v1
Martínez-Guerrero, E.; Eulogio, P. L.; Miranda-Romagnoli, P.; Noriega-Papaqui, R.; Seck-Tuoh-Mora, J. C. HE-DEPSO Algorithm for Textures Optimization with Application in Particle Physics. Preprints 2024, 2024081171. https://doi.org/10.20944/preprints202408.1171.v1
Martínez-Guerrero, E.; Eulogio, P. L.; Miranda-Romagnoli, P.; Noriega-Papaqui, R.; Seck-Tuoh-Mora, J. C. HE-DEPSO Algorithm for Textures Optimization with Application in Particle Physics. Preprints2024, 2024081171. https://doi.org/10.20944/preprints202408.1171.v1
APA Style
Martínez-Guerrero, E., Eulogio, P. L., Miranda-Romagnoli, P., Noriega-Papaqui, R., & Seck-Tuoh-Mora, J. C. (2024). HE-DEPSO Algorithm for Textures Optimization with Application in Particle Physics. Preprints. https://doi.org/10.20944/preprints202408.1171.v1
Chicago/Turabian Style
Martínez-Guerrero, E., Roberto Noriega-Papaqui and Juan Carlos Seck-Tuoh-Mora. 2024 "HE-DEPSO Algorithm for Textures Optimization with Application in Particle Physics" Preprints. https://doi.org/10.20944/preprints202408.1171.v1
Abstract
Within the phenomenology of particle physics, the theoretical model of 4-zero textures is validated using a chi-square criterion that compares experimental data with the computational results of the model. Traditionally, analytical methods that often imply simplifications, combined with computational analysis, have been used to validate texture models. In this paper, we propose a new meta-heuristic variant of the differential evolution algorithm that incorporates aspects of the particle swarm optimization algorithm called "HE-DEPSO" to obtain chi-squared values are less than a bound value, which exhaustive and traditional algorithms cannot obtain. The results show that the proposed algorithm can optimize the chi-square function according to the required criteria. We compare simulated data with experimental data in the allowed search region, thereby validating the 4-zero texture model.
Keywords
Optimization; High energy physics; Differential evolution; Particle swarm optimization
Subject
Computer Science and Mathematics, Other
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.
