An integrated multi-objective optimization method with application to train crashworthiness design
- PDF / 2,425,870 Bytes
- 20 Pages / 595.276 x 790.866 pts Page_size
- 102 Downloads / 211 Views
INDUSTRIAL APPLICATION PAPER
An integrated multi-objective optimization method with application to train crashworthiness design Lin Hou 1,2 & Honghao Zhang 1,2 & Yong Peng 1,2,3 & Shiming Wang 1,2 & Song Yao 1,2,3 & Zhixiang Li 1,2 & Gongxun Deng 1,2 Received: 25 June 2020 / Revised: 14 August 2020 / Accepted: 2 October 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The collision performance of crashworthy structures can be improved using the optimization design. However, conflicting objective functions and the non-uniqueness of solutions hinder the Pareto optimum selection. This study constructs a hybrid optimization approach (TA-FBGV) integrating the multi-objective optimization and the multiple criteria decision-making (MCDM). TA-FBGV combines the two-phase differential evolution (ToPDE) method, indicator-based multi-objective evolutionary algorithm (AR-MOEA), fuzzy best worst (F-BW) strategy, grey relational analysis (GRA), and VIsekriterijumsko KOmpromisno Rangiranje (VIKOR) integrating method (G-VIKOR) to address difficulties of the Pareto optimum selection. The ToPDE method is employed to generate uniform samples. AR-MOEA is used to produce Pareto optimal solutions. Weighting values of competing objectives are calculated through the F-BW strategy. G-VIKOR is applied to choose the final Pareto optimum from the Pareto front. Subsequently, the multiobjective optimization of the train underframe structure is performed using TA-FBGV. The results show that the Pareto optimum obtained by G-VIKOR is a good compromising solution which locates near the knee point provided by the minimum distance selection method (TMDSM). This implies that the G-VIKOR approach can achieve a good trade-off among conflictive objectives. Compared with the initial model, although the energy absorption of the Pareto optimum is decreased, the initial peak crushing force and structural mass are reduced. Optimization results indicate that the TA-FBGV approach is efficient to select the Pareto optimum for the structure optimization design. Keywords Multi-objective optimization . Evolutionary algorithm . Fuzzy best worst strategy . Multiple criteria decision-making . Train crashworthiness
Nomenclature a Vehicle impact acceleration AR-MOEA The indicator-based multi-objective evolutionary algorithm BO Best-to-others Responsible Editor: Erdem Acar * Yong Peng [email protected] 1
Key Laboratory of Traffic Safety on Track (Central South University), Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
2
Joint International Research Laboratory of Key Technology for Rail Traffic Safety, Central South University, Changsha 410000, China
3
National & Local Joint Engineering Research Center of Safety Technology for Rail Vehicle, Central South University, Changsha 410000, China
CEM COPRAS d dy DB DM DO DoE EA Fave F-BW FE GD GRA IGD IPCF M MCDM ME
Crash energy management Complex proportional assessment Impact stroke Wheel/rail lateral relative displac
Data Loading...
