Genetic Algorithm for Optimal Response Time Scheduling of Electric Vehicle Model

Zouhaira Abdellaoui, Houda Meddeb

2025

Abstract

Genetic Algorithms (GAs) are widely recognized for their ability to solve complex optimization problems. Gas are an effective computational tool designed to identify optimal solutions for optimization issues in electrical vehicle. In this context, we have developed GA for optimizing the response time based on static scheduling suspension model of SAE Benchmark electric vehicles. The implemented architecture consists of multiple nodes connected via the Real- Time middleware Data Distribution Service (DDS) and the protocol FlexRay in order to benefit from their high speed and QoS.

Paper Citation

in Harvard Style

Abdellaoui Z. and Meddeb H. (2025). Genetic Algorithm for Optimal Response Time Scheduling of Electric Vehicle Model. In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-742-9, SciTePress, pages 476-483. DOI: 10.5220/0013276200003928

in Bibtex Style

@conference{enase25,
author={Zouhaira Abdellaoui and Houda Meddeb},
title={Genetic Algorithm for Optimal Response Time Scheduling of Electric Vehicle Model},
booktitle={Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2025},
pages={476-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013276200003928},
isbn={978-989-758-742-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Genetic Algorithm for Optimal Response Time Scheduling of Electric Vehicle Model
SN - 978-989-758-742-9
AU - Abdellaoui Z.
AU - Meddeb H.
PY - 2025
SP - 476
EP - 483
DO - 10.5220/0013276200003928
PB - SciTePress