Practical design optimization problems require use of computationally expensive “black-box” functions. The Pareto set pursuing (PSP) method, for solving multi-objective optimization problems with expensive black-box functions, was originally developed for continuous variables. In this paper, modifications are made to allow solution of problems with mixed continuous-discrete variables. A performance comparison strategy for nongradient-based multi-objective algorithms is discussed based on algorithm efficiency, robustness, and closeness to the true Pareto front with a limited number of function evaluations. Results using several methods, along with the modified PSP, are given for a suite of benchmark problems and two engineering design ones. The modified PSP is found to be competitive when the total number of function evaluations is limited, but faces an increased computational challenge when the number of design variables increases.
Skip Nav Destination
e-mail: zok@sfu.ca
e-mail: hva1@sfu.ca
e-mail: amirreza_ziai@sfu.ca
e-mail: gary_wang@sfu.ca
e-mail: cmenon@sfu.ca
Article navigation
July 2010
Research Papers
On the Performance of the PSP Method for Mixed-Variable Multi-Objective Design Optimization
Zeeshan Omer Khokhar,
Zeeshan Omer Khokhar
MENRVA Research Group, School of Engineering Science,
e-mail: zok@sfu.ca
Simon Fraser University
, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
Search for other works by this author on:
Hengameh Vahabzadeh,
Hengameh Vahabzadeh
Product Design and Optimization Laboratory, Mechatronic Systems Engineering,
e-mail: hva1@sfu.ca
Simon Fraser University
, 250-13450 102 Avenue, Surrey, BC V3T0A3, Canada
Search for other works by this author on:
Amirreza Ziai,
Amirreza Ziai
MENRVA Research Group, School of Engineering Science,
e-mail: amirreza_ziai@sfu.ca
Simon Fraser University
, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
Search for other works by this author on:
G. Gary Wang,
G. Gary Wang
Product Design and Optimization Laboratory, Mechatronic Systems Engineering,
e-mail: gary_wang@sfu.ca
Simon Fraser University
, 250-13450 102 Avenue, Surrey, BC V3T0A3, Canada
Search for other works by this author on:
Carlo Menon
Carlo Menon
MENRVA Research Group, School of Engineering Science,
e-mail: cmenon@sfu.ca
Simon Fraser University
, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
Search for other works by this author on:
Zeeshan Omer Khokhar
MENRVA Research Group, School of Engineering Science,
Simon Fraser University
, 8888 University Drive, Burnaby, BC V5A 1S6, Canadae-mail: zok@sfu.ca
Hengameh Vahabzadeh
Product Design and Optimization Laboratory, Mechatronic Systems Engineering,
Simon Fraser University
, 250-13450 102 Avenue, Surrey, BC V3T0A3, Canadae-mail: hva1@sfu.ca
Amirreza Ziai
MENRVA Research Group, School of Engineering Science,
Simon Fraser University
, 8888 University Drive, Burnaby, BC V5A 1S6, Canadae-mail: amirreza_ziai@sfu.ca
G. Gary Wang
Product Design and Optimization Laboratory, Mechatronic Systems Engineering,
Simon Fraser University
, 250-13450 102 Avenue, Surrey, BC V3T0A3, Canadae-mail: gary_wang@sfu.ca
Carlo Menon
MENRVA Research Group, School of Engineering Science,
Simon Fraser University
, 8888 University Drive, Burnaby, BC V5A 1S6, Canadae-mail: cmenon@sfu.ca
J. Mech. Des. Jul 2010, 132(7): 071009 (11 pages)
Published Online: July 7, 2010
Article history
Received:
September 25, 2009
Revised:
April 6, 2010
Online:
July 7, 2010
Published:
July 7, 2010
Citation
Khokhar, Z. O., Vahabzadeh, H., Ziai, A., Wang, G. G., and Menon, C. (July 7, 2010). "On the Performance of the PSP Method for Mixed-Variable Multi-Objective Design Optimization." ASME. J. Mech. Des. July 2010; 132(7): 071009. https://doi.org/10.1115/1.4001599
Download citation file:
Get Email Alerts
Related Articles
Application of Constrained Multi-Objective Optimization to the Design
of Offshore Structure Hulls
J. Offshore Mech. Arct. Eng (February,2009)
Reducible Uncertain Interval Design by Kriging Metamodel Assisted Multi-Objective Optimization
J. Mech. Des (January,2011)
Vector Evaluated Particle Swarm Optimization (VEPSO) of Supersonic Ejector for Hydrogen Fuel Cells
J. Fuel Cell Sci. Technol (August,2010)
An Efficient Pareto Set Identification Approach for Multiobjective Optimization on Black-Box Functions
J. Mech. Des (September,2005)
Related Proceedings Papers
Related Chapters
A Learning-Based Adaptive Routing for QoS-Aware Data Collection in Fixed Sensor Networks with Mobile Sinks
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
A Collaborative Framework for Distributed Multiobjective Combinatorial Optimization
International Conference on Computer and Computer Intelligence (ICCCI 2011)
A Multi-Objective Optimization Model for Developing Emergency Evacuation Plans (PSAM-0466)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)