How to Choose Solutions for Applying Momentum in Evolutionary Multi-Objective Optimization

Longcan Chen; Lie Meng Pang; Qingfu Zhang; Hisao Ishibuchi

doi:10.1109/SMC58881.2025.11342773

How to Choose Solutions for Applying Momentum in Evolutionary Multi-Objective Optimization

Longcan Chen
, Lie Meng Pang
, Qingfu Zhang
, Hisao Ishibuchi

Department of Computer Science

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

Abstract

Momentum is a technique that adds the momentum moves from the earlier iterations into the current update to accelerate convergence. While the momentum technique has been widely used in single-objective optimization, its application in evolutionary multi-objective optimization (EMO) has not gained much attention. Since EMO algorithms are population-based algorithms, how to choose solutions for applying momentum becomes an important issue. Inspired by Polyak's momentum method and Nesterov's momentum method in single-objective optimization, we propose four different momentum methods for EMO. Our findings demonstrate that the performance of EMOAs with momentum is strongly affected by the choice of solutions to which momentum moves are applied. © 2025 IEEE.

Original language	English
Title of host publication	2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Proceedings
Publisher	IEEE
Pages	3489-3495
Number of pages	7
ISBN (Electronic)	979-8-3315-3358-8
DOIs	https://doi.org/10.1109/SMC58881.2025.11342773
Publication status	Published - Oct 2025
Event	2025 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2025): Navigating Frontiers: Smart Systems for a Dynamic World - Austria Center Vienna, Vienna, Austria Duration: 5 Oct 2025 → 8 Oct 2025 https://www.ieeesmc2025.org/

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)	1062-922X
ISSN (Electronic)	2577-1655

Conference

Conference	2025 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2025)
Abbreviated title	SMC 2025
Place	Austria
City	Vienna
Period	5/10/25 → 8/10/25
Internet address	https://www.ieeesmc2025.org/

Funding

This work was supported by National Natural Science Foundation of China (Grant No. 62376115, 62250710682), Guangdong Provincial Key Laboratory (Grant No. 2020B121201001).

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10.1109/SMC58881.2025.11342773

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Chen, L., Pang, L. M., Zhang, Q., & Ishibuchi, H. (2025). How to Choose Solutions for Applying Momentum in Evolutionary Multi-Objective Optimization. In 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Proceedings (pp. 3489-3495). (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). IEEE. https://doi.org/10.1109/SMC58881.2025.11342773

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title = "How to Choose Solutions for Applying Momentum in Evolutionary Multi-Objective Optimization",

abstract = "Momentum is a technique that adds the momentum moves from the earlier iterations into the current update to accelerate convergence. While the momentum technique has been widely used in single-objective optimization, its application in evolutionary multi-objective optimization (EMO) has not gained much attention. Since EMO algorithms are population-based algorithms, how to choose solutions for applying momentum becomes an important issue. Inspired by Polyak's momentum method and Nesterov's momentum method in single-objective optimization, we propose four different momentum methods for EMO. Our findings demonstrate that the performance of EMOAs with momentum is strongly affected by the choice of solutions to which momentum moves are applied. {\textcopyright} 2025 IEEE.",

author = "Longcan Chen and Pang, \{Lie Meng\} and Qingfu Zhang and Hisao Ishibuchi",

year = "2025",

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booktitle = "2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Proceedings",

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note = "2025 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2025) : Navigating Frontiers: Smart Systems for a Dynamic World, SMC 2025 ; Conference date: 05-10-2025 Through 08-10-2025",

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Chen, L, Pang, LM, Zhang, Q & Ishibuchi, H 2025, How to Choose Solutions for Applying Momentum in Evolutionary Multi-Objective Optimization. in 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Proceedings. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, IEEE, pp. 3489-3495, 2025 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2025), Vienna, Austria, 5/10/25. https://doi.org/10.1109/SMC58881.2025.11342773

How to Choose Solutions for Applying Momentum in Evolutionary Multi-Objective Optimization. / Chen, Longcan; Pang, Lie Meng; Zhang, Qingfu et al.
2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Proceedings. IEEE, 2025. p. 3489-3495 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

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

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T1 - How to Choose Solutions for Applying Momentum in Evolutionary Multi-Objective Optimization

AU - Chen, Longcan

AU - Pang, Lie Meng

AU - Zhang, Qingfu

AU - Ishibuchi, Hisao

PY - 2025/10

Y1 - 2025/10

N2 - Momentum is a technique that adds the momentum moves from the earlier iterations into the current update to accelerate convergence. While the momentum technique has been widely used in single-objective optimization, its application in evolutionary multi-objective optimization (EMO) has not gained much attention. Since EMO algorithms are population-based algorithms, how to choose solutions for applying momentum becomes an important issue. Inspired by Polyak's momentum method and Nesterov's momentum method in single-objective optimization, we propose four different momentum methods for EMO. Our findings demonstrate that the performance of EMOAs with momentum is strongly affected by the choice of solutions to which momentum moves are applied. © 2025 IEEE.

AB - Momentum is a technique that adds the momentum moves from the earlier iterations into the current update to accelerate convergence. While the momentum technique has been widely used in single-objective optimization, its application in evolutionary multi-objective optimization (EMO) has not gained much attention. Since EMO algorithms are population-based algorithms, how to choose solutions for applying momentum becomes an important issue. Inspired by Polyak's momentum method and Nesterov's momentum method in single-objective optimization, we propose four different momentum methods for EMO. Our findings demonstrate that the performance of EMOAs with momentum is strongly affected by the choice of solutions to which momentum moves are applied. © 2025 IEEE.

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

T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SP - 3489

EP - 3495

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Chen L, Pang LM, Zhang Q, Ishibuchi H. How to Choose Solutions for Applying Momentum in Evolutionary Multi-Objective Optimization. In 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Proceedings. IEEE. 2025. p. 3489-3495. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/SMC58881.2025.11342773

How to Choose Solutions for Applying Momentum in Evolutionary Multi-Objective Optimization

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