Surrogate-Assisted Evolutionary Optimizers for Multiobjective Design of a Torque Arm Structure

Abstract:

This paper presents two surrogate-assisted optimization strategies for structural constrained multiobjective optimization. The optimization strategies are based on hybridization of multiobjective population-based incremental learning (MOPBIL) and radial-basis function (RBF) interpolation. The first strategy uses MOPBIL for generating training points while the second strategy uses a Latin hypercube sampling (LHS) technique. The design case study is the shape and sizing design of a torque arm structure. A design problem is set to minimize structural mass and displacement while constraints include stresses due to three different load cases. Structural analysis is carried out by means of a finite element approach. The design problem is then tackled by the proposed surrogate-assisted design strategies. Numerical results show that the use of MOPBIL for generating training points is superior to the use of LHS based on a hypervolume indicator and root mean square error (RMSE).

• Periodical: Applied Mechanics and Materials (Volumes 101-102)
• Pages: 324-328
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.101-102.324
• Online since: September 2011
• Authors: Nantiwat Pholdee, Sujin Bureerat
• Keywords: Latin Hypercube Sampling, Multiobjective Evolutionary Algorithms, Multiobjective Population-Based Incremental Learning, Structural Constrained Optimization, Surrogate Model

References

[1] S. Srisomporn and S. Bureerat: IEEE TRANSACTIONS ON COMPONENTS AND PACKAGING TECHNOLOGIES Vol. 31(2008), pp.351-360.

[2] P. Breitkopf, H. Naceur, A. Rassineux and P. Villon: Computers and Structures Vol. 83 (2005), pp.1411-1428.

DOI: 10.1016/j.compstruc.2004.07.011

[3] K. T. Chiang and F. P. Chang: International Communications in Heat and Mass Transfer, Vol. 33 (2006), pp.836-845.

[4] H. Naceur, S. Ben-Elechi, J. L. Batoz and C. Knopf-Lenoir: Materials and Design Vol. 29 (2008), pp.781-790.

DOI: 10.1016/j.matdes.2007.01.018

[5] W. Hu, L. G. Yao, and Z. Z Hua: Journal of materials processing technology Vol. 197(2008), p.77–88.

[6] S. Bureerat and S. Srisomporn: Engineering Optimization Vol. 42(2010), pp.305-343.

[7] S. Kanyakam and S. Bureerat: submitted to 2007 IEEE Congress on Evolutionary Computation, Singapore, 25-28 September 2007, pp.4162-4169.

[8] C. Noilublao and S. Bureerat: Evolutionary Computation In-Tech (2009), pp.487-580.

[9] S. Bureerat and K. Sriworamas: Advances in Soft Computing Vol. 39(2007), pp.223-232.