Evolutionary Approach to Multiparty Multiobjective Optimization Problems with Common Pareto Optimal Solutions

Wenjie Liu; Wenjian Luo; Xin Lin; Miqing Li; Shengxiang Yang

doi:10.1109/CEC48606.2020.9185747

Evolutionary Approach to Multiparty Multiobjective Optimization Problems with Common Pareto Optimal Solutions

Wenjie Liu, Wenjian Luo, Xin Lin, Miqing Li, Shengxiang Yang

Computer Science

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

6 Citations (Scopus)

Abstract

Some real-world optimization problems involve multiple decision makers holding different positions, each of whom has multiple conflicting objectives. These problems are defined as multiparty multiobjective optimization problems (MPMOPs). Although evolutionary multiobjective optimization has been widely studied for many years, little attention has been paid to multiparty multiobjective optimization in the field of evolutionary computation. In this paper, a class of MPMOPs, that is, MPMOPs having common Pareto optimal solutions, is addressed. A benchmark for MPMOPs, obtained by modifying an existing dynamic multiobjective optimization benchmark, is provided, and a multiparty multiobjective evolutionary algorithm to find the common Pareto optimal set is proposed. The results of experiments conducted using the benchmark show that the proposed multiparty multiobjective evolutionary algorithm is effective.

Original language	English
Title of host publication	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9781728169293
DOIs	https://doi.org/10.1109/CEC48606.2020.9185747
Publication status	Published - Jul 2020
Event	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Conference

Conference	2020 IEEE Congress on Evolutionary Computation, CEC 2020
Country/Territory	United Kingdom
City	Virtual, Glasgow
Period	19/07/20 → 24/07/20

Bibliographical note

Funding Information:
This work is partly supported by the National Natural Science Foundation of China (No. 61573327). (Corresponding author: Wenjian Luo.)

Publisher Copyright:
© 2020 IEEE.

Keywords

evolutionary computation
Multiobjective optimization
multiparty multiobjective optimization

ASJC Scopus subject areas

Control and Optimization
Decision Sciences (miscellaneous)
Artificial Intelligence
Computer Vision and Pattern Recognition
Hardware and Architecture

Access to Document

10.1109/CEC48606.2020.9185747

Cite this

Liu, W., Luo, W., Lin, X., Li, M., & Yang, S. (2020). Evolutionary Approach to Multiparty Multiobjective Optimization Problems with Common Pareto Optimal Solutions. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings Article 9185747 (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC48606.2020.9185747

Liu, Wenjie ; Luo, Wenjian ; Lin, Xin et al. / Evolutionary Approach to Multiparty Multiobjective Optimization Problems with Common Pareto Optimal Solutions. 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2020. (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings).

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title = "Evolutionary Approach to Multiparty Multiobjective Optimization Problems with Common Pareto Optimal Solutions",

abstract = "Some real-world optimization problems involve multiple decision makers holding different positions, each of whom has multiple conflicting objectives. These problems are defined as multiparty multiobjective optimization problems (MPMOPs). Although evolutionary multiobjective optimization has been widely studied for many years, little attention has been paid to multiparty multiobjective optimization in the field of evolutionary computation. In this paper, a class of MPMOPs, that is, MPMOPs having common Pareto optimal solutions, is addressed. A benchmark for MPMOPs, obtained by modifying an existing dynamic multiobjective optimization benchmark, is provided, and a multiparty multiobjective evolutionary algorithm to find the common Pareto optimal set is proposed. The results of experiments conducted using the benchmark show that the proposed multiparty multiobjective evolutionary algorithm is effective.",

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

author = "Wenjie Liu and Wenjian Luo and Xin Lin and Miqing Li and Shengxiang Yang",

note = "Funding Information: This work is partly supported by the National Natural Science Foundation of China (No. 61573327). (Corresponding author: Wenjian Luo.) Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Congress on Evolutionary Computation, CEC 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/CEC48606.2020.9185747",

language = "English",

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publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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Liu, W, Luo, W, Lin, X, Li, M & Yang, S 2020, Evolutionary Approach to Multiparty Multiobjective Optimization Problems with Common Pareto Optimal Solutions. in 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings., 9185747, 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2020 IEEE Congress on Evolutionary Computation, CEC 2020, Virtual, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/CEC48606.2020.9185747

Evolutionary Approach to Multiparty Multiobjective Optimization Problems with Common Pareto Optimal Solutions. / Liu, Wenjie; Luo, Wenjian; Lin, Xin et al.
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2020. 9185747 (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings).

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

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Liu W, Luo W, Lin X, Li M, Yang S. Evolutionary Approach to Multiparty Multiobjective Optimization Problems with Common Pareto Optimal Solutions. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers (IEEE). 2020. 9185747. (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). doi: 10.1109/CEC48606.2020.9185747

Evolutionary Approach to Multiparty Multiobjective Optimization Problems with Common Pareto Optimal Solutions

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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