An evolutionary optimization-learning hybrid algorithm for energy resource management
Created by W.Langdon from
gp-bibliography.bib Revision:1.8276
- @Article{DBLP:journals/swevo/QiJCBM25,
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author = "Rui Qi and Ya-Hui Jia and Wei-Neng Chen and
Ying Bi and Yi Mei",
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title = "An evolutionary optimization-learning hybrid algorithm
for energy resource management",
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journal = "Swarm and Evolutionary Computation",
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year = "2025",
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volume = "92",
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pages = "101831",
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month = feb,
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keywords = "genetic algorithms, genetic programming, Energy
resources management, Heuristic learning, Large-scale
global optimization",
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ISSN = "2210-6502",
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timestamp = "Mon, 03 Mar 2025 22:23:34 +0100",
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biburl = "
https://dblp.org/rec/journals/swevo/QiJCBM25.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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appendix_url = "
https://github.com/qiruiqwe/ERM/blob/main/Appendix.pdf",
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DOI = "
doi:10.1016/j.swevo.2024.101831",
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size = "11 pages",
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abstract = "Energy resource management (ERM) is important to an
energy system. Effective management is hard to achieve
because of the ubiquitous uncertainty of distributed
energy resources and the massive number of
participants, especially small storage devices (SSDs).
Evolutionary computation algorithms have been applied
to the ERM problem, but the high-dimensional nature of
this problem makes them inefficient. In this paper, we
propose an evolutionary optimization-learning hybrid
algorithm to solve the ERM problem effectively and
efficiently. A novel hybrid encoding scheme is proposed
with two parts, optimization and learning. In the
optimization part, an SSD integration strategy is
designed to treat all SSDs as a whole, thereby
significantly reducing the dimensions related to SSDs.
In the learning part, the genetic programming algorithm
is adopted to learn SSD state allocation rules
automatically. Based on the hybrid encoding scheme, a
delicately orchestrated evolution process is proposed
to evolve these two parts simultaneously. Comparisons
on a real-world distribution network located in Spain
show that the proposed algorithm has outperformed the
state-of-the-art algorithms.",
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notes = "School of Future Technology, South China University of
Technology, Guangzhou 511442, China",
- }
Genetic Programming entries for
Rui Qi
Ya-Hui Jia
Wei-Neng Chen
Ying Bi
Yi Mei
Citations