Car Setup Optimization Using Multi-Objective Swarm Algorithms

Título: Car Setup Optimization Using Multi-Objective Swarm Algorithms

Autores: Oliveira Junior, M. C.; Lacerda, M. G. P.; Barboza, E. A.; Cordeiro, P. R. G.; Bastos-Filho, C. J. A.

Resumo: In this paper, we propose to use two multi-objective swarm optimization algorithms, called MOPSO-CDR and MOABC, to tackle the car setup optimization problem. We aim to find the best set of parameters for the car in order to improve its performance during the races. We used The Open Racing Car Simulator (TORCS) in our simulations and we compared our results to the ones presented in the 2010 Car Setup Optimization Competition. We demonstrated that the MOABC can achieve similar results when compared to the state-of-art algorihms. The MOPSO-CDR outperformed all the previous approaches, including the MOABC algorithm for this problem.

Palavras-chave: Multi-Objective Optimization; Swarm Intelligence; Particle Swarm Optimization; Artificial Bee Colony; Car Setup Optimization

Páginas: 8

Código DOI: 10.21528/CBIC2011-26.3

Artigo em pdf: st_26.3.pdf

Arquivo BibTex: st_26.3.bib