J Heuristics (2009) 15: 177–196 DOI 10.1007/s10732-007-9069-4 Design of a motorcycle frame using neuroacceleration strategies in MOEAs Jorge E. Rodríguez · Andrés L. Medaglia · Carlos A. Coello Coello Received: 12 February 2007 / Revised: 16 November 2007 / Accepted: 21 December 2007 / Published online: 16 January 2008 © Springer Science+Business Media, LLC 2008 Abstract Designing a low-budget lightweight motorcycle frame with superior dy- namic and mechanical properties is a complex engineering problem. This complexity is due in part to the presence of multiple design objectives—mass, structural stress and rigidity—, the high computational cost of the finite element (FE) simulations used to evaluate the objectives, and the nature of the design variables in the frame’s geometry (discrete and continuous). Therefore, this paper presents a neuroaccelera- tion strategy for multiobjective evolutionary algorithms (MOEAs) based on the com- bined use of real (FE simulations) and approximate fitness function evaluations. The proposed approach accelerates convergence to the Pareto optimal front (POF) com- prised of nondominated frame designs. The proposed MOEA uses a mixed geno- type to encode discrete and continuous design variables, and a set of genetic oper- ators applied according to the type of variable. The results show that the proposed neuro-accelerated MOEAs, NN-NSGA II and NN-MicroGA, improve upon the per- formance of their original counterparts, NSGA II and MicroGA. Thus, this neuroac- celeration strategy is shown to be effective and probably applicable to other FE-based engineering design problems. Keywords Multiobjective evolutionary algorithms · Finite element analysis · Neural networks · Motorcycle · Engineering design · Multiobjective optimization J.E. Rodríguez · A.L. Medaglia ( ) Centro de Optimización y Probabilidad Aplicada, Departamento de Ingeniería Industrial, Universidad de los Andes, Carrera 1 N. 18A 10, Bogota, Colombia e-mail: amedagli@uniandes.edu.co J.E. Rodríguez e-mail: je.rodriguez58@egresados.uniandes.edu.co C.A. Coello Coello CINVESTAV-IPN, Departamento de Computación, Ave. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, México, D.F., 07300, Mexico e-mail: ccoello@cs.cinvestav.mx