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Improved Trust Region Based MPS Method for High-Dimensional Expensive Black-Box Problems

[+] Author Affiliations
George H. Cheng, Adel Younis, Kambiz Haji Hajikolaei, G. Gary Wang

Simon Fraser University, Surrey, BC, Canada

Paper No. DETC2013-12665, pp. V03BT03A013; 10 pages
doi:10.1115/DETC2013-12665
From:
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3B: 39th Design Automation Conference
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5589-8
  • Copyright © 2013 by ASME

abstract

Mode Pursuing Sampling (MPS) was developed as a global optimization algorithm for optimization problems involving expensive black box functions. MPS has been found to be effective and efficient for problems of low dimensionality, i.e., the number of design variables is less than ten. A previous conference publication integrated the concept of trust regions into the MPS framework to create a new algorithm, TRMPS, which dramatically improved performance and efficiency for high dimensional problems. However, although TRMPS performed better than MPS, it was unproven against other established algorithms such as GA. This paper introduces an improved algorithm, TRMPS2, which incorporates guided sampling and low function value criterion to further improve algorithm performance for high dimensional problems. TRMPS2 is benchmarked against MPS and GA using a suite of test problems. The results show that TRMPS2 performs better than MPS and GA on average for high dimensional, expensive, and black box (HEB) problems.

Copyright © 2013 by ASME

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