0
Research Papers: Design Automation

Multi-Objective Optimization With Multiple Spatially Distributed Surrogates

[+] Author and Article Information
Kalyan Shankar Bhattacharjee

School of Engineering and IT,
The University of New South Wales,
Canberra, ACT 2600, Australia
e-mail: k.bhattacharjee@student.adfa.edu.au

Hemant Kumar Singh

School of Engineering and IT,
The University of New South Wales,
Canberra, ACT 2600, Australia
e-mail: h.singh@adfa.edu.au

Tapabrata Ray

School of Engineering and IT,
The University of New South Wales,
Canberra, ACT 2600, Australia
e-mail: t.ray@adfa.edu.au

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received December 12, 2015; final manuscript received June 25, 2016; published online July 18, 2016. Assoc. Editor: Gary Wang.

J. Mech. Des 138(9), 091401 (Jul 18, 2016) (10 pages) Paper No: MD-15-1821; doi: 10.1115/1.4034035 History: Received December 12, 2015; Revised June 25, 2016

In engineering design optimization, evaluation of a single solution (design) often requires running one or more computationally expensive simulations. Surrogate assisted optimization (SAO) approaches have long been used for solving such problems, in which approximations/surrogates are used in lieu of computationally expensive simulations during the course of search. Existing SAO approaches often use the same type of approximation model to represent all objectives and constraints in all regions of the search space. The selection of a type of surrogate model over another is nontrivial and an a priori choice limits flexibility in representation. In this paper, we introduce a multi-objective evolutionary algorithm (EA) with multiple adaptive spatially distributed surrogates. Instead of a single global surrogate, local surrogates of multiple types are constructed in the neighborhood of each offspring solution and a multi-objective search is conducted using the best surrogate for each objective and constraint function. The proposed approach offers flexibility of representation by capitalizing on the benefits offered by various types of surrogates in different regions of the search space. The approach is also immune to illvalidation since approximated and truly evaluated solutions are not ranked together. The performance of the proposed surrogate assisted multi-objective algorithm (SAMO) is compared with baseline nondominated sorting genetic algorithm II (NSGA-II) and NSGA-II embedded with global and local surrogates of various types. The performance of the proposed approach is quantitatively assessed using several engineering design optimization problems. The numerical experiments demonstrate competence and consistency of SAMO.

FIGURES IN THIS ARTICLE
<>
Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

Nondominated front for the median HV run (a) ZDT1, (b) Welded Beam, (c) CNC Machining, (d) Tool Spindle Design, (e) Metal Cutting, (f) PHEV Design, (g) Crash Safety Design, (h) Bulk Carrier Design

Grahic Jump Location
Fig. 2

Mean HV convergence (a) ZDT1, (b) Welded Beam, (c) CNC Machining, (d) Tool Spindle Design, (e) Metal Cutting, (f) PHEV Design, (g) Crash Safety Design, (h) Bulk Carrier Design

Grahic Jump Location
Fig. 3

Performance profile: (a) median (inverse of) HV statistics and (b) median IGD statistics

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In