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

Guilherme Ferreira Gomes ^[1] ; Kouider Bendine ^[2] ; Joao Luiz Junho Pereira ^[1]
1. [1] Universidade Federal de Itajubá
  
  Universidade Federal de Itajubá
  
  Brasil
2. [2] Materials Research and Technology (MRT) Department Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg
Localización: Mechanics based design of structures and machines, ISSN 1539-7734, Vol. 53, Nº. 8, 2025, págs. 5822-5849
Idioma: inglés
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Resumen
- 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.