Optimization and artificial intelligence: An in-depth analysis of multi-objective optimization, sampling methods, and regression algorithms applied to structural design

Autor(es)

Gomes, Guilherme Ferreira ; Bendine, Kouider ; Pereira, Joao Luiz Junho

• Tipo de documento: Artigo Científico
• Fonte: MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES; v. N/A, p. 28-pg., 2025-03-18.

Resumo

This study addresses the challenge of structural optimization in Formula SAE chassis, focusing on balancing lightweight design with structural integrity. By integrating parametric optimization with AIdriven metamodeling, the research compares four multi-objective optimization algorithms-Non-Sorted Genetic Algorithm II, Multi-objective Lichtenberg Algorithm, Multi-objective Sunflower Optimization, and Multi-objective Particle Swarm Optimization-aiming to minimize chassis mass and maximize stiffness. The results show that AI-driven metamodeling significantly reduces computational cost, cutting optimization time by over 99%, while maintaining accuracy comparable to direct Finite Element simulations. This work provides a framework for enhanced automotive and structural optimization. (AU)

• Processo FAPESP: 22/10683-7 - Meu benchmark de conjuntos de dados é desafiador o suficiente?
• Beneficiário: João Luiz Junho Pereira
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 23/10419-0 - Seleção multi-objetivo ótima de conjunto de dados para avaliação não enviesada e eficiente de algoritmos de aprendizagem de máquina
• Beneficiário: João Luiz Junho Pereira
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado