Design of a motorcycle frame using neuroacceleration strategies in MOEAs |
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Authors: | Jorge E. Rodríguez Andrés L. Medaglia Carlos A. Coello Coello |
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Affiliation: | (1) Centro de Optimización y Probabilidad Aplicada, Departamento de Ingeniería Industrial, Universidad de los Andes, Carrera 1 N. 18A 10, Bogota, Colombia;(2) CINVESTAV-IPN, Departamento de Computación, Ave. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, México, D.F., 07300, Mexico |
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Abstract: | Designing a low-budget lightweight motorcycle frame with superior dynamic 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 neuroacceleration strategy for multiobjective evolutionary algorithms (MOEAs) based on the combined use of real (FE simulations) and approximate fitness function evaluations. The proposed approach accelerates convergence to the Pareto optimal front (POF) comprised of nondominated frame designs. The proposed MOEA uses a mixed genotype to encode discrete and continuous design variables, and a set of genetic operators 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 performance of their original counterparts, NSGA II and MicroGA. Thus, this neuroacceleration strategy is shown to be effective and probably applicable to other FE-based engineering design problems. |
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Keywords: | Multiobjective evolutionary algorithms Finite element analysis Neural networks Motorcycle Engineering design Multiobjective optimization |
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