Title: Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame
Authors: Nantiwat Pholdee; Sujin Bureerat; Ali Rıza Yıldız
Addresses: Faculty of Engineering, Sustainable Infrastructure Research and Development Center, Department of Mechanical Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand ' Faculty of Engineering, Sustainable Infrastructure Research and Development Center, Department of Mechanical Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand ' Department of Mechanical Engineering, Bursa Technical University, Bursa 16190, Turkey
Abstract: In this paper, a many-objective hybrid real-code population-based incremental learning and differential evolution algorithm (MnRPBILDE) is proposed based on the concept of objective function space reduction. The method is then implemented on real engineering design problems. The topology, shape and sizing design of a simplified automotive floor-frame structure are formulated and used as test problems. A variety of well-established multi-objective evolutionary algorithms (MOEAs) including the original version of MnRPBILDE are employed to solve the test problems while the results are compared based on hypervolume and C indicators. The results indicate that our proposed algorithm outperforms the other MOEAs. The proposed algorithm is effective and efficient for many-objective optimisations of a car floor-frame structure.
Keywords: vehicle design; floor frame design; multi-objective optimisation; population-based incremental learning; differential evolution; topology optimisation; vehicle floor frames; objective function space reduction; engineering design; multiobjective evolutionary algorithm; MOEA; automobile industry; automotive manufacturing.
International Journal of Vehicle Design, 2017 Vol.73 No.1/2/3, pp.20 - 53
Available online: 22 Feb 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article