A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle

Abstract This paper deals with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. A combined multi-objective imperialist competitive algorithm and Monte Carlo method is developed and used for the robust multi-objective optimization of the rail vehicle system. This robust optimization of rail vehicle safety considers simultaneously the derailment angle and its standard deviation where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the rail vehicle safety to the design parameters uncertainties compared to the determinist one and to the literature results.

[1]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[2]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[3]  Ali Sadollah,et al.  Water cycle algorithm for solving constrained multi-objective optimization problems , 2015, Appl. Soft Comput..

[4]  Noureddine Bouhaddi,et al.  Robust optimization of the non-linear behaviour of a vibrating system , 2009 .

[5]  Hi Sung Lee,et al.  Assessment of running safety of railway vehicles using multibody dynamics , 2010 .

[6]  António Araújo,et al.  Monte Carlo uncertainty simulation of surface emissivity at ambient temperature obtained by dual spectral infrared radiometry , 2014 .

[7]  Najlawi Bilel,et al.  An improved imperialist competitive algorithm for multi-objective optimization , 2016 .

[8]  Ajmi Houidi,et al.  Multiobjective robust design optimization of rail vehicle moving in short radius curved tracks based on the safety and comfort criteria , 2013, Simul. Model. Pract. Theory.

[9]  Abdul Hanan Abdullah,et al.  MOICA: A novel multi-objective approach based on imperialist competitive algorithm , 2013, Appl. Math. Comput..

[10]  John McPhee,et al.  Optimization of curving performance of rail vehicles , 2005 .

[11]  M. Motevalli,et al.  Using Monte-Carlo approach for analysis of quantitative and qualitative operation of reservoirs system with regard to the inflow uncertainty , 2015 .

[12]  Brian Boswell,et al.  Application of Markov modelling and Monte Carlo simulation technique in failure probability estimation — A consideration of corrosion defects of internally corroded pipelines , 2016 .

[13]  Mian Li,et al.  Robust optimization using hybrid differential evolution and sequential quadratic programming , 2015 .

[14]  R. Bhattacharyya,et al.  Bond graph modeling of a railway truck on curved track , 2009, Simul. Model. Pract. Theory.

[15]  Mohamed Nejlaoui,et al.  Analytical modeling of rail vehicle safety and comfort in short radius curved tracks , 2009 .

[16]  Pradeep Jangir,et al.  Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems , 2016, Applied Intelligence.

[17]  Mehdi Kalantari,et al.  Multi-objective robust optimisation of unidirectional carbon/glass fibre reinforced hybrid composites under flexural loading , 2016 .