A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization

Fei Liu; Qingfu Zhang

doi:10.1007/978-3-031-27250-9_2

A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization

Fei Liu^*
, Qingfu Zhang

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

Multiobjective discrete optimization via simulation (MDOvS) has received considerable attention from both academics and industry due to its wide application. This paper proposes a two-stage fast convergent search algorithm for MDOvS. In its first stage, the multiobjective optimization problem under consideration is decomposed into several single-objective optimization subproblems, and a Pareto retrospective approximation method is used to generate an approximated optimal solution for each subproblem. In the second stage, from the solutions generated in the first stage, a multiobjective local stochastic search with a revised simulation allocation rule is used to explore the entire Pareto front. Our experimental studies show that the proposed method outperforms the state-of-the-art MO-COMPASS on a set of test instances with noisy evaluations and a bi-objective bus scheduling problem. Our proposed method is up to ten times faster than MO-COMPASS. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization
Subtitle of host publication	12th International Conference, EMO 2023, Proceedings
Editors	Michael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, Iryna Yevseyeva
Publisher	Springer, Cham
Pages	17-28
ISBN (Electronic)	978-3-031-27250-9
ISBN (Print)	978-3-031-27249-3
DOIs	https://doi.org/10.1007/978-3-031-27250-9_2
Publication status	Published - Mar 2023
Event	12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023) - Hybrid, Leiden, Netherlands Duration: 20 Mar 2023 → 24 Mar 2023 https://emo2023.liacs.leidenuniv.nl/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13970 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023)
Place	Netherlands
City	Leiden
Period	20/03/23 → 24/03/23
Internet address	https://emo2023.liacs.leidenuniv.nl/

Research Keywords

Decomposition
Integer-ordered
Multiobjective optimization
Simulation optimization
Stochastic

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Liu, F., & Zhang, Q. (2023). A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization. In M. Emmerich, A. Deutz, H. Wang, A. V. Kononova, B. Naujoks, K. Li, K. Miettinen, & I. Yevseyeva (Eds.), Evolutionary Multi-Criterion Optimization: 12th International Conference, EMO 2023, Proceedings (pp. 17-28). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13970 LNCS). Springer, Cham. https://doi.org/10.1007/978-3-031-27250-9_2

Liu, Fei ; Zhang, Qingfu. / A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization. Evolutionary Multi-Criterion Optimization: 12th International Conference, EMO 2023, Proceedings. editor / Michael Emmerich ; André Deutz ; Hao Wang ; Anna V. Kononova ; Boris Naujoks ; Ke Li ; Kaisa Miettinen ; Iryna Yevseyeva. Springer, Cham, 2023. pp. 17-28 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization",

abstract = "Multiobjective discrete optimization via simulation (MDOvS) has received considerable attention from both academics and industry due to its wide application. This paper proposes a two-stage fast convergent search algorithm for MDOvS. In its first stage, the multiobjective optimization problem under consideration is decomposed into several single-objective optimization subproblems, and a Pareto retrospective approximation method is used to generate an approximated optimal solution for each subproblem. In the second stage, from the solutions generated in the first stage, a multiobjective local stochastic search with a revised simulation allocation rule is used to explore the entire Pareto front. Our experimental studies show that the proposed method outperforms the state-of-the-art MO-COMPASS on a set of test instances with noisy evaluations and a bi-objective bus scheduling problem. Our proposed method is up to ten times faster than MO-COMPASS. {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

keywords = "Decomposition, Integer-ordered, Multiobjective optimization, Simulation optimization, Stochastic",

author = "Fei Liu and Qingfu Zhang",

year = "2023",

month = mar,

doi = "10.1007/978-3-031-27250-9\_2",

language = "English",

isbn = "978-3-031-27249-3",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer, Cham",

pages = "17--28",

editor = "Michael Emmerich and Andr{\'e} Deutz and Hao Wang and Kononova, \{Anna V.\} and Boris Naujoks and Ke Li and Kaisa Miettinen and Iryna Yevseyeva",

booktitle = "Evolutionary Multi-Criterion Optimization",

note = "12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023) ; Conference date: 20-03-2023 Through 24-03-2023",

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Liu, F & Zhang, Q 2023, A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization. in M Emmerich, A Deutz, H Wang, AV Kononova, B Naujoks, K Li, K Miettinen & I Yevseyeva (eds), Evolutionary Multi-Criterion Optimization: 12th International Conference, EMO 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13970 LNCS, Springer, Cham, pp. 17-28, 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023), Leiden, Netherlands, 20/03/23. https://doi.org/10.1007/978-3-031-27250-9_2

A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization. / Liu, Fei ; Zhang, Qingfu.
Evolutionary Multi-Criterion Optimization: 12th International Conference, EMO 2023, Proceedings. ed. / Michael Emmerich; André Deutz; Hao Wang; Anna V. Kononova; Boris Naujoks; Ke Li; Kaisa Miettinen; Iryna Yevseyeva. Springer, Cham, 2023. p. 17-28 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13970 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization

AU - Liu, Fei

AU - Zhang, Qingfu

PY - 2023/3

Y1 - 2023/3

N2 - Multiobjective discrete optimization via simulation (MDOvS) has received considerable attention from both academics and industry due to its wide application. This paper proposes a two-stage fast convergent search algorithm for MDOvS. In its first stage, the multiobjective optimization problem under consideration is decomposed into several single-objective optimization subproblems, and a Pareto retrospective approximation method is used to generate an approximated optimal solution for each subproblem. In the second stage, from the solutions generated in the first stage, a multiobjective local stochastic search with a revised simulation allocation rule is used to explore the entire Pareto front. Our experimental studies show that the proposed method outperforms the state-of-the-art MO-COMPASS on a set of test instances with noisy evaluations and a bi-objective bus scheduling problem. Our proposed method is up to ten times faster than MO-COMPASS. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

AB - Multiobjective discrete optimization via simulation (MDOvS) has received considerable attention from both academics and industry due to its wide application. This paper proposes a two-stage fast convergent search algorithm for MDOvS. In its first stage, the multiobjective optimization problem under consideration is decomposed into several single-objective optimization subproblems, and a Pareto retrospective approximation method is used to generate an approximated optimal solution for each subproblem. In the second stage, from the solutions generated in the first stage, a multiobjective local stochastic search with a revised simulation allocation rule is used to explore the entire Pareto front. Our experimental studies show that the proposed method outperforms the state-of-the-art MO-COMPASS on a set of test instances with noisy evaluations and a bi-objective bus scheduling problem. Our proposed method is up to ten times faster than MO-COMPASS. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

KW - Decomposition

KW - Integer-ordered

KW - Multiobjective optimization

KW - Simulation optimization

KW - Stochastic

UR - https://www.scopus.com/pages/publications/85151049580

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U2 - 10.1007/978-3-031-27250-9_2

DO - 10.1007/978-3-031-27250-9_2

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-031-27249-3

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 17

EP - 28

BT - Evolutionary Multi-Criterion Optimization

A2 - Emmerich, Michael

A2 - Deutz, André

A2 - Wang, Hao

A2 - Kononova, Anna V.

A2 - Naujoks, Boris

A2 - Li, Ke

A2 - Miettinen, Kaisa

A2 - Yevseyeva, Iryna

PB - Springer, Cham

T2 - 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023)

Y2 - 20 March 2023 through 24 March 2023

ER -

Liu F , Zhang Q. A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization. In Emmerich M, Deutz A, Wang H, Kononova AV, Naujoks B, Li K, Miettinen K, Yevseyeva I, editors, Evolutionary Multi-Criterion Optimization: 12th International Conference, EMO 2023, Proceedings. Springer, Cham. 2023. p. 17-28. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2023 Mar 9. doi: 10.1007/978-3-031-27250-9_2

A Two-Stage Algorithm for Integer Multiobjective Simulation Optimization

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