An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems

Meiji Cui; Li Li; Shuwei Zhu; Mengchu Zhou

doi:10.1109/ICNSC52481.2021.9702169

An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems

Meiji Cui, Li Li, Shuwei Zhu, Mengchu Zhou

Electrical and Computer Engineering

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

3 Scopus citations

Abstract

Large-scale multi-objective and many-objective problems are widely existing in the real-world. These problems are extremely challenging to deal with as a result of exponentially expanded search space as well as complicated conflicting objectives. Most existing algorithms focus either on large-scale decision variables or multiple objectives solely while few algorithms consider both of them. In this paper, we propose an improved competitive swarm optimization (ICSO) dedicated to deal with large-scale search space. Moreover, we incorporate ICSO into the MultiGPO framework, an efficient framework for many-objective problems, and name it as MultiGPO_ICSO. To validate the performance of MultiGPO_ICSO, we test all algorithms on LSMOP with dimensions varying from 100 to 500. Compared with other algorithms, MultiGPO_ICSO shows competitive performance on most problems with limited computational resources. Therefore, MultiGPO_ICSO is suitable to deal with large-scale multi-objective and many-objective problems.

Original language	English (US)
Title of host publication	ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control
Subtitle of host publication	Industry 4.0 and AI
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665440486
DOIs	https://doi.org/10.1109/ICNSC52481.2021.9702169
State	Published - 2021
Event	18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021 - Xiamen, China Duration: Dec 3 2021 → Dec 5 2021

Publication series

Name	ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI

Conference

Conference	18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021
Country/Territory	China
City	Xiamen
Period	12/3/21 → 12/5/21

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Statistics, Probability and Uncertainty
Control and Optimization
Sensory Systems

Keywords

Large-scale optimization
Pareto Dominance
competitive swarm optimization
many-objective optimization
multi-objective optimization

Access to Document

10.1109/ICNSC52481.2021.9702169

Cite this

Cui, M., Li, L., Zhu, S., & Zhou, M. (2021). An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems. In ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI (ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNSC52481.2021.9702169

Cui, Meiji ; Li, Li ; Zhu, Shuwei et al. / An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems. ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI. Institute of Electrical and Electronics Engineers Inc., 2021. (ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI).

@inproceedings{3c02c85fb2e142eea4e13b986e2e1808,

title = "An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems",

abstract = "Large-scale multi-objective and many-objective problems are widely existing in the real-world. These problems are extremely challenging to deal with as a result of exponentially expanded search space as well as complicated conflicting objectives. Most existing algorithms focus either on large-scale decision variables or multiple objectives solely while few algorithms consider both of them. In this paper, we propose an improved competitive swarm optimization (ICSO) dedicated to deal with large-scale search space. Moreover, we incorporate ICSO into the MultiGPO framework, an efficient framework for many-objective problems, and name it as MultiGPO_ICSO. To validate the performance of MultiGPO_ICSO, we test all algorithms on LSMOP with dimensions varying from 100 to 500. Compared with other algorithms, MultiGPO_ICSO shows competitive performance on most problems with limited computational resources. Therefore, MultiGPO_ICSO is suitable to deal with large-scale multi-objective and many-objective problems.",

keywords = "Large-scale optimization, Pareto Dominance, competitive swarm optimization, many-objective optimization, multi-objective optimization",

author = "Meiji Cui and Li Li and Shuwei Zhu and Mengchu Zhou",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021 ; Conference date: 03-12-2021 Through 05-12-2021",

year = "2021",

doi = "10.1109/ICNSC52481.2021.9702169",

language = "English (US)",

series = "ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI",

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

booktitle = "ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control",

address = "United States",

}

Cui, M, Li, L, Zhu, S & Zhou, M 2021, An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems. in ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI. ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI, Institute of Electrical and Electronics Engineers Inc., 18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021, Xiamen, China, 12/3/21. https://doi.org/10.1109/ICNSC52481.2021.9702169

An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems. / Cui, Meiji; Li, Li; Zhu, Shuwei et al.
ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI. Institute of Electrical and Electronics Engineers Inc., 2021. (ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI).

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

TY - GEN

T1 - An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems

AU - Cui, Meiji

AU - Li, Li

AU - Zhu, Shuwei

AU - Zhou, Mengchu

PY - 2021

Y1 - 2021

N2 - Large-scale multi-objective and many-objective problems are widely existing in the real-world. These problems are extremely challenging to deal with as a result of exponentially expanded search space as well as complicated conflicting objectives. Most existing algorithms focus either on large-scale decision variables or multiple objectives solely while few algorithms consider both of them. In this paper, we propose an improved competitive swarm optimization (ICSO) dedicated to deal with large-scale search space. Moreover, we incorporate ICSO into the MultiGPO framework, an efficient framework for many-objective problems, and name it as MultiGPO_ICSO. To validate the performance of MultiGPO_ICSO, we test all algorithms on LSMOP with dimensions varying from 100 to 500. Compared with other algorithms, MultiGPO_ICSO shows competitive performance on most problems with limited computational resources. Therefore, MultiGPO_ICSO is suitable to deal with large-scale multi-objective and many-objective problems.

AB - Large-scale multi-objective and many-objective problems are widely existing in the real-world. These problems are extremely challenging to deal with as a result of exponentially expanded search space as well as complicated conflicting objectives. Most existing algorithms focus either on large-scale decision variables or multiple objectives solely while few algorithms consider both of them. In this paper, we propose an improved competitive swarm optimization (ICSO) dedicated to deal with large-scale search space. Moreover, we incorporate ICSO into the MultiGPO framework, an efficient framework for many-objective problems, and name it as MultiGPO_ICSO. To validate the performance of MultiGPO_ICSO, we test all algorithms on LSMOP with dimensions varying from 100 to 500. Compared with other algorithms, MultiGPO_ICSO shows competitive performance on most problems with limited computational resources. Therefore, MultiGPO_ICSO is suitable to deal with large-scale multi-objective and many-objective problems.

KW - Large-scale optimization

KW - Pareto Dominance

KW - competitive swarm optimization

KW - many-objective optimization

KW - multi-objective optimization

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M3 - Conference contribution

AN - SCOPUS:85126682410

T3 - ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI

BT - ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

Y2 - 3 December 2021 through 5 December 2021

ER -

Cui M, Li L, Zhu S, Zhou M. An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems. In ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI. Institute of Electrical and Electronics Engineers Inc. 2021. (ICNSC 2021 - 18th IEEE International Conference on Networking, Sensing and Control: Industry 4.0 and AI). doi: 10.1109/ICNSC52481.2021.9702169

An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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