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Pareto front approximation with adaptive weighted sum method in multiobjective simulation optimization

Authors:

Hong WanAuthors Info & Claims

WSC '09: Winter Simulation Conference

Pages 623 - 633

Published: 13 December 2009 Publication History

Abstract

This work proposes a new method for approximating the Pareto front of a multi-objective simulation optimization problem (MOP) where the explicit forms of the objective functions are not available. The method iteratively approximates each objective function using a metamodeling scheme and employs a weighted sum method to convert the MOP into a set of single objective optimization problems. The weight on each single objective function is adaptively determined by accessing newly introduced points at the current iteration and the non-dominated points so far. A trust region algorithm is applied to the single objective problems to search for the points on the Pareto front. The numerical results show that the proposed algorithm efficiently generates evenly distributed points for various types of Pareto fronts.

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Cited By

Chang TWatson LLarson JNeveu NThacker WDeshpande SLux T(2022)Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization ProblemsACM Transactions on Mathematical Software10.1145/352925848:3(1-34)Online publication date: 10-Sep-2022
https://dl.acm.org/doi/10.1145/3529258
Lange PWeller RZachmann GCai WMeng TWilsey PJin K(2017)GDSProceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3064911.3064917(185-196)Online publication date: 16-May-2017
https://dl.acm.org/doi/10.1145/3064911.3064917
Datta SGhosh ASanyal KDas S(2017)A Radial Boundary Intersection aided interior point method for multi-objective optimizationInformation Sciences: an International Journal10.1016/j.ins.2016.09.062377:C(1-16)Online publication date: 20-Jan-2017
https://dl.acm.org/doi/10.1016/j.ins.2016.09.062
Show More Cited By

Pareto front approximation with adaptive weighted sum method in multiobjective simulation optimization
1. Information systems
  1. Data management systems
    1. Database administration
2. Theory of computation
  1. Design and analysis of algorithms

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Information & Contributors

Information

Published In

cover image ACM Conferences

WSC '09: Winter Simulation Conference

December 2009

3211 pages

ISBN:9781424457717

General Chair:
Ann Dunkin
Palo Alto Unified School District
,
Program Chair:
Ricki G. Ingalls
Oklahoma State University

Sponsors

IIE: Institute of Industrial Engineers
INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling
SCS: Society for Modeling and Simulation International

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Winter Simulation Conference

Publication History

Published: 13 December 2009

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WSC09

Sponsor:

IIE
INFORMS-SIM
SIGSIM
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WSC09: Winter Simulation Conference

December 13 - 16, 2009

Texas, Austin

Acceptance Rates

WSC '09 Paper Acceptance Rate 137 of 256 submissions, 54%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Citations

Cited By

Chang TWatson LLarson JNeveu NThacker WDeshpande SLux T(2022)Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization ProblemsACM Transactions on Mathematical Software10.1145/352925848:3(1-34)Online publication date: 10-Sep-2022
https://dl.acm.org/doi/10.1145/3529258
Lange PWeller RZachmann GCai WMeng TWilsey PJin K(2017)GDSProceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3064911.3064917(185-196)Online publication date: 16-May-2017
https://dl.acm.org/doi/10.1145/3064911.3064917
Datta SGhosh ASanyal KDas S(2017)A Radial Boundary Intersection aided interior point method for multi-objective optimizationInformation Sciences: an International Journal10.1016/j.ins.2016.09.062377:C(1-16)Online publication date: 20-Jan-2017
https://dl.acm.org/doi/10.1016/j.ins.2016.09.062
Lange PWeller RZachmann GFujimoto RUnger BCarothers C(2016)Knowledge Discovery for Pareto Based Multiobjective Optimization in SimulationProceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/2901378.2901380(35-46)Online publication date: 15-May-2016
https://dl.acm.org/doi/10.1145/2901378.2901380
Hunter SFeldman G(2015)Optimal sampling laws for bi-objective simulation optimization on finite setsProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2889120(3749-3757)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2889120
Feldman GHunter SPasupathy R(2015)Multi-objective simulation optimization on finite setsProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2889104(3610-3621)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2889104
Deshpande SWatson L(2014)Multiobjective optimization using direct search techniquesProceedings of the High Performance Computing Symposium10.5555/2663510.2663528(1-8)Online publication date: 13-Apr-2014
https://dl.acm.org/doi/10.5555/2663510.2663528
Arriaga-González JValenzuela-Rendón M(2013)Reinforcement Learning Method for Portfolio Optimal Frontier SearchProceedings of the 12th Mexican International Conference on Advances in Soft Computing and Its Applications - Volume 826610.1007/978-3-642-45111-9_22(245-255)Online publication date: 24-Nov-2013
https://dl.acm.org/doi/10.1007/978-3-642-45111-9_22
Villarreal-Marroquín MCastro JCabrera-Ríos M(2011)A multicriteria simulation optimization method for injection moldingProceedings of the Winter Simulation Conference10.5555/2431518.2431805(2395-2407)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2431805

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