Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup

Kiraz, Berna; Asta, Shahriar; Özcan, Ender; Köle, Muhammet; Etaner-Uyar, A. Sima

Abstract

Tuning a race car to improve its performance by adopting an effective setup is crucial and an extremely challenging task. The Open Racing Car Simulator, referred to as TORCS, is a well-known simulator in which a race car requires a configuration of twenty two real-valued parameters for an optimal setup. In this study, various modern (meta)heuristic techniques, such as, evolutionary algorithms, swarm intelligence algorithm and selection hyper-heuristics, are evaluated using TORCS to solve the car setup optimisation problem across a range of tracks. An in-depth performance comparison and analysis of those techniques on the car setup optimisation problem are provided with a discussion on their strengths and weaknesses. The empirical results indicate the success of Covariance Matrix Adaptation Evolutionary Strategy for the car setup optimisation problem.

Citation

Kiraz, B., Asta, S., Özcan, E., Köle, M., & Etaner-Uyar, A. S. (2023). Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup. In Engineering Applications of Modern Metaheuristics (1-18). Springer. https://doi.org/10.1007/978-3-031-16832-1_1

• Online Publication Date: Dec 5, 2022
• Publication Date: 2023
• Deposit Date: Oct 9, 2024
• Publisher: Springer
• Pages: 1-18
• Series Title: Studies in Computational Intelligence
• Series Number: 1069
• Series ISSN: 1860-9503
• Book Title: Engineering Applications of Modern Metaheuristics
• ISBN: 9783031168314
• DOI: https://doi.org/10.1007/978-3-031-16832-1_1
• Public URL: https://nottingham-repository.worktribe.com/output/30153103
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-031-16832-1_1