Design of a motorcycle frame using neuroacceleration strategies in MOEAs
Design of a motorcycle frame using neuroacceleration strategies in MOEAs
复制标题
使用 MOEA 中的神经加速策略设计摩托车车架
DOI：
10.1007/s10732-007-9069-4
复制
发表时间：
2009
影响因子：
2.7
通讯作者：
C. Coello
中科院分区：
文献类型：
--
作者：
Jorge E. Rodríguez;A. Medaglia;C. Coello
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.
