An evolutionary optimization-learning hybrid algorithm for energy resource management

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@Article{DBLP:journals/swevo/QiJCBM25,

• author = "Rui Qi and Ya-Hui Jia and Wei-Neng Chen and Ying Bi and Yi Mei",
• title = "An evolutionary optimization-learning hybrid algorithm for energy resource management",
• journal = "Swarm and Evolutionary Computation",
• year = "2025",
• volume = "92",
• pages = "101831",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Energy resources management, Heuristic learning, Large-scale global optimization",
• ISSN = "2210-6502",
• timestamp = "Mon, 03 Mar 2025 22:23:34 +0100",
• biburl = "https://dblp.org/rec/journals/swevo/QiJCBM25.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494625009202",
• appendix_url = "https://github.com/qiruiqwe/ERM/blob/main/Appendix.pdf",
• DOI = "doi:10.1016/j.swevo.2024.101831",
• size = "11 pages",
• 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. 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.",
• 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