On Multiobjective Knapsack Problems with Multiple Decision Makers

Zhen Song; Wenjian Luo; Xin Lin; Zeneng She; Qingfu Zhang

doi:10.1109/SSCI51031.2022.10022188

On Multiobjective Knapsack Problems with Multiple Decision Makers

Zhen Song
, Wenjian Luo^*
, Xin Lin
, Zeneng She
, Qingfu Zhang

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology
Peng Cheng Laboratory
University of Science and Technology of China
City University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Many real-world optimization problems require optimizing multiple conflicting objectives simultaneously, and such problems are called multiobjective optimization problems (MOPs). As a variant of the classical knapsack problems, multi-objective knapsack problems (MOKPs), exist widely in the real-world applications, e.g., cargo loading, project and investment selection. There is a special class of MOKPs called multiparty multiobjective knapsack problems (MPMOKPs), which involve multiple decision makers (DMs) and each DM only cares about some of all the objectives. To the best of our knowledge, little work has been conducted to address MPMOKPs. In this paper, a set of benchmarks which have common Pareto optimal solutions for MPMOKPs is proposed. Besides, we design a SPEA2-based algorithm, called SPEA2-MP to solve MPMOKPs, which aims at finding the common Pareto optimal solutions to satisfy multiple decision makers as far as possible. Experimental results on the benchmarks have demonstrated the effectiveness of the proposed algorithm.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Editors	Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	156-163
Number of pages	8
ISBN (Electronic)	9781665487689
DOIs	https://doi.org/10.1109/SSCI51031.2022.10022188
State	Published - 2022
Externally published	Yes
Event	2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - Singapore, Singapore Duration: 4 Dec 2022 → 7 Dec 2022

Publication series

Name	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

Conference

Conference	2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Country/Territory	Singapore
City	Singapore
Period	4/12/22 → 7/12/22

Keywords

Multiobjective optimization
evolutionary computation
knapsack problem
multiparty multiobjective optimization

Access to Document

10.1109/SSCI51031.2022.10022188

Cite this

Song, Z., Luo, W., Lin, X., She, Z., & Zhang, Q. (2022). On Multiobjective Knapsack Problems with Multiple Decision Makers. In H. Ishibuchi, C.-K. Kwoh, A.-H. Tan, D. Srinivasan, C. Miao, A. Trivedi, & K. Crockett (Eds.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 156-163). (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI51031.2022.10022188

Song, Zhen ; Luo, Wenjian ; Lin, Xin et al. / On Multiobjective Knapsack Problems with Multiple Decision Makers. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. editor / Hisao Ishibuchi ; Chee-Keong Kwoh ; Ah-Hwee Tan ; Dipti Srinivasan ; Chunyan Miao ; Anupam Trivedi ; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 156-163 (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022).

@inproceedings{97be2090bb674c8a97ce2563380e27f0,

title = "On Multiobjective Knapsack Problems with Multiple Decision Makers",

abstract = "Many real-world optimization problems require optimizing multiple conflicting objectives simultaneously, and such problems are called multiobjective optimization problems (MOPs). As a variant of the classical knapsack problems, multi-objective knapsack problems (MOKPs), exist widely in the real-world applications, e.g., cargo loading, project and investment selection. There is a special class of MOKPs called multiparty multiobjective knapsack problems (MPMOKPs), which involve multiple decision makers (DMs) and each DM only cares about some of all the objectives. To the best of our knowledge, little work has been conducted to address MPMOKPs. In this paper, a set of benchmarks which have common Pareto optimal solutions for MPMOKPs is proposed. Besides, we design a SPEA2-based algorithm, called SPEA2-MP to solve MPMOKPs, which aims at finding the common Pareto optimal solutions to satisfy multiple decision makers as far as possible. Experimental results on the benchmarks have demonstrated the effectiveness of the proposed algorithm.",

keywords = "Multiobjective optimization, evolutionary computation, knapsack problem, multiparty multiobjective optimization",

author = "Zhen Song and Wenjian Luo and Xin Lin and Zeneng She and Qingfu Zhang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 ; Conference date: 04-12-2022 Through 07-12-2022",

year = "2022",

doi = "10.1109/SSCI51031.2022.10022188",

language = "英语",

series = "Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "156--163",

editor = "Hisao Ishibuchi and Chee-Keong Kwoh and Ah-Hwee Tan and Dipti Srinivasan and Chunyan Miao and Anupam Trivedi and Keeley Crockett",

booktitle = "Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022",

address = "美国",

}

Song, Z, Luo, W, Lin, X, She, Z & Zhang, Q 2022, On Multiobjective Knapsack Problems with Multiple Decision Makers. in H Ishibuchi, C-K Kwoh, A-H Tan, D Srinivasan, C Miao, A Trivedi & K Crockett (eds), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Institute of Electrical and Electronics Engineers Inc., pp. 156-163, 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Singapore, Singapore, 4/12/22. https://doi.org/10.1109/SSCI51031.2022.10022188

On Multiobjective Knapsack Problems with Multiple Decision Makers. / Song, Zhen; Luo, Wenjian; Lin, Xin et al.
Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. ed. / Hisao Ishibuchi; Chee-Keong Kwoh; Ah-Hwee Tan; Dipti Srinivasan; Chunyan Miao; Anupam Trivedi; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2022. p. 156-163 (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On Multiobjective Knapsack Problems with Multiple Decision Makers

AU - Song, Zhen

AU - Luo, Wenjian

AU - Lin, Xin

AU - She, Zeneng

AU - Zhang, Qingfu

PY - 2022

Y1 - 2022

N2 - Many real-world optimization problems require optimizing multiple conflicting objectives simultaneously, and such problems are called multiobjective optimization problems (MOPs). As a variant of the classical knapsack problems, multi-objective knapsack problems (MOKPs), exist widely in the real-world applications, e.g., cargo loading, project and investment selection. There is a special class of MOKPs called multiparty multiobjective knapsack problems (MPMOKPs), which involve multiple decision makers (DMs) and each DM only cares about some of all the objectives. To the best of our knowledge, little work has been conducted to address MPMOKPs. In this paper, a set of benchmarks which have common Pareto optimal solutions for MPMOKPs is proposed. Besides, we design a SPEA2-based algorithm, called SPEA2-MP to solve MPMOKPs, which aims at finding the common Pareto optimal solutions to satisfy multiple decision makers as far as possible. Experimental results on the benchmarks have demonstrated the effectiveness of the proposed algorithm.

AB - Many real-world optimization problems require optimizing multiple conflicting objectives simultaneously, and such problems are called multiobjective optimization problems (MOPs). As a variant of the classical knapsack problems, multi-objective knapsack problems (MOKPs), exist widely in the real-world applications, e.g., cargo loading, project and investment selection. There is a special class of MOKPs called multiparty multiobjective knapsack problems (MPMOKPs), which involve multiple decision makers (DMs) and each DM only cares about some of all the objectives. To the best of our knowledge, little work has been conducted to address MPMOKPs. In this paper, a set of benchmarks which have common Pareto optimal solutions for MPMOKPs is proposed. Besides, we design a SPEA2-based algorithm, called SPEA2-MP to solve MPMOKPs, which aims at finding the common Pareto optimal solutions to satisfy multiple decision makers as far as possible. Experimental results on the benchmarks have demonstrated the effectiveness of the proposed algorithm.

KW - Multiobjective optimization

KW - evolutionary computation

KW - knapsack problem

KW - multiparty multiobjective optimization

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U2 - 10.1109/SSCI51031.2022.10022188

DO - 10.1109/SSCI51031.2022.10022188

M3 - 会议稿件

AN - SCOPUS:85147795009

T3 - Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

SP - 156

EP - 163

BT - Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

A2 - Ishibuchi, Hisao

A2 - Kwoh, Chee-Keong

A2 - Tan, Ah-Hwee

A2 - Srinivasan, Dipti

A2 - Miao, Chunyan

A2 - Trivedi, Anupam

A2 - Crockett, Keeley

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

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ER -

Song Z, Luo W, Lin X, She Z, Zhang Q. On Multiobjective Knapsack Problems with Multiple Decision Makers. In Ishibuchi H, Kwoh CK, Tan AH, Srinivasan D, Miao C, Trivedi A, Crockett K, editors, Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 156-163. (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022). doi: 10.1109/SSCI51031.2022.10022188

On Multiobjective Knapsack Problems with Multiple Decision Makers

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