Paper Title:
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).

  Info
Periodical
Chapter
Chapter 2: Simulation and Engineering Optimization
Edited by
Di Zheng, Yiqiang Wang, Yi-Min Deng, Aibing Yu and Weihua Li
Pages
324-328
DOI
10.4028/www.scientific.net/AMM.101-102.324
Citation
N. Pholdee, S. Bureerat, "Surrogate-Assisted Evolutionary Optimizers for Multiobjective Design of a Torque Arm Structure", Applied Mechanics and Materials, Vols. 101-102, pp. 324-328, 2012
Online since
September 2011
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Price
$32.00
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