Multiple Surrogate Assisted Many-objective Optimization for Computationally Expensive Engineering Design

[+] Author and Article Information
Kalyan Shankar Bhattacharjee

School of Engineering and IT, The University of New South Wales, Canberra, Australia

Hemant K. Singh

School of Engineering and IT, The University of New South Wales, Canberra, Australia

Ray Tapabrata

School of Engineering and IT, The University of New South Wales, Canberra, Australia

1Corresponding author.

ASME doi:10.1115/1.4039450 History: Received September 11, 2017; Revised February 15, 2018


Engineering design often involves problems with multiple conflicting performance criteria, commonly referred to as multi-objective optimization problems (MOP). MOPs are known to be particularly challenging if the number of objectives is more than three. This has motivated recent attempts to solve MOPs with more than three objectives, which are now more specifically referred to as “many-objective” optimization problems (MaOPs). Evolutionary algorithms used to solve such problems are known to require numerous design evaluations prior to convergence. This is not practical for engineering applications involving computationally expensive evaluations such as computational fluid dynamics, finite element analysis etc. While the use of surrogates has been commonly studied for single-objective optimization, there is scarce literature on its use for MOPs/MaOPs. This paper attempts to bridge this research gap by introducing a surrogate assisted optimization algorithm for solving MOP/MaOP within a limited computing budget. The algorithm relies on principles of decomposition and adaptation of reference vectors for effective search. The flexibility of function representation is offered through the use of multiple types of surrogate models. Furthermore, to efficiently deal with constrained MaOPs, marginally infeasible solutions are promoted during initial phases of the search. The performance of the proposed algorithm is benchmarked with the state-of-the-art approaches using a range of problems for up to 10 objective problems. Thereafter, a case study involving vehicle design is presented highlighting the competence of the proposed approach. 1 Introduction

Copyright (c) 2018 by ASME
Your Session has timed out. Please sign back in to continue.






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