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Open AccessArticle
Branch-Parallel Simulated Annealing for Energy-Efficient Multi-Compressor Operation
by
Min-Jae Kim
Min-Jae Kim
Min-Jae Kim received his B.S. degree in Computer Science and Engineering from Busan National Busan, [...]
Min-Jae Kim received his B.S. degree in Computer Science and Engineering from Busan National University, Busan, Republic of Korea, in 2025, and is now pursuing an M.S. degree in Artificial Intelligence at the same university. His research topics mainly include metaheuristic optimization, reinforcement learning, and physical AI.
,
Ho-Jin Choi
Ho-Jin Choi
Ho-Jin Choi received his B.S. degree in Computer Science and Engineering from Pusan National Busan, [...]
Ho-Jin Choi received his B.S. degree in Computer Science and Engineering from Pusan National University, Busan, Republic of Korea, and is currently pursuing an integrated M.S.–Ph.D. degree in Artificial Intelligence at the same university. His research topics mainly include combinatorial optimization, optimization based on machine learning, and physical AI.
Nobuyoshi Komuro
Nobuyoshi Komuro
Nobuyoshi KOMURO received his B.E., M.E., and Ph.D. degrees in Information Science from Ibaraki in a [...]
Nobuyoshi KOMURO received his B.E., M.E., and Ph.D. degrees in Information Science from Ibaraki University, Hitachi-shi, Ibaraki, Japan, in 2000, 2002, and, 2005, respectively. From 2005 to 2009, he was with the School of Computer Science, Tokyo University of Technology. Since April 2009, he has been with Chiba University, where he is currently an Associate Professor with the Institute of Management and Information Technologies. From May 2012 to April 2013, he was a visiting scholar at Rutgers University, NJ, USA. His research interests include code division multiple access, wireless sensor network protocol, and indoor positioning.
Jaeyoung Han
Jaeyoung Han
Jaeyoung Han received his B.S., M.S., and Ph.D. degrees in Mechanical Engineering from Chungnam of a [...]
Jaeyoung Han received his B.S., M.S., and Ph.D. degrees in Mechanical Engineering from Chungnam National University, Daejeon, Republic of Korea, with the Ph.D. completed in 2018. He is currently a Professor in the Department of Future Automotive Engineering at Kongju National University, Cheonan, Republic of Korea. His research interests include the development of fuel cell and battery system models, optimization-based design programs, and advanced analysis technologies for green hydrogen production and eco-friendly mobility systems.
Won-Suk Kim
Won-Suk Kim
Won-Suk Kim is currently an assistant professor at the School of Computer Science and Engineering at [...]
Won-Suk Kim is currently an assistant professor at the School of Computer Science and Engineering at Pusan National University, Republic of Korea. He received his B.S. and Ph.D. degrees in the School of Computer Science and Engineering from Pusan National University in 2010 and 2017, respectively. His current interests are digital twin, edge computing, and object sensing.
1
The Department of Computer Engineering, Pusan National University, Busan 46241, Republic of Korea
2
Institute of Management and Information Technologies, Chiba University, Chiba 263-8522, Japan
3
The Department of Future Automotive Engineering, Kongju National University, Cheonan 31080, Republic of Korea
4
Institute of Intelligent Vehicle, Kongju National University, 1223-24 Cheonandaero, Seobuk-gu, Cheonan 31080, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2026, 15(1), 214; https://doi.org/10.3390/electronics15010214 (registering DOI)
Submission received: 6 November 2025
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Revised: 28 December 2025
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Accepted: 31 December 2025
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Published: 2 January 2026
Abstract
The overall energy efficiency of a multi-compressor system varies greatly depending on its operating strategy. In most industrial facilities, the strategy is still determined empirically by operators, which often fails to achieve optimal energy efficiency. The optimization of multi-compressor operation is inherently complex, as it must simultaneously consider various operational factors such as on/off combinations, type-specific flow capacities, and flow constraints. This study proposes a parallel-search simulated annealing algorithm that employs a branch-based exploration mechanism. In the proposed algorithm, multiple search branches independently explore the solution space and periodically exchange information, enabling a broader search and faster convergence than conventional simulated annealing. Simulation results show that the proposed approach reduces total power consumption by about 8% compared with existing heuristic methods while maintaining stable and consistent performance across large-scale scenarios.
Share and Cite
MDPI and ACS Style
Kim, M.-J.; Choi, H.-J.; Komuro, N.; Han, J.; Kim, W.-S.
Branch-Parallel Simulated Annealing for Energy-Efficient Multi-Compressor Operation. Electronics 2026, 15, 214.
https://doi.org/10.3390/electronics15010214
AMA Style
Kim M-J, Choi H-J, Komuro N, Han J, Kim W-S.
Branch-Parallel Simulated Annealing for Energy-Efficient Multi-Compressor Operation. Electronics. 2026; 15(1):214.
https://doi.org/10.3390/electronics15010214
Chicago/Turabian Style
Kim, Min-Jae, Ho-Jin Choi, Nobuyoshi Komuro, Jaeyoung Han, and Won-Suk Kim.
2026. "Branch-Parallel Simulated Annealing for Energy-Efficient Multi-Compressor Operation" Electronics 15, no. 1: 214.
https://doi.org/10.3390/electronics15010214
APA Style
Kim, M.-J., Choi, H.-J., Komuro, N., Han, J., & Kim, W.-S.
(2026). Branch-Parallel Simulated Annealing for Energy-Efficient Multi-Compressor Operation. Electronics, 15(1), 214.
https://doi.org/10.3390/electronics15010214
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