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Cited by 191

The DOI matches its Crossref record.

Comparative Study of Modern Heuristic Algorithms to Service Restoration in Distribution Systems

Toune, S., Fudo, H., Genji, T., Fukuyama, Y., Nakanishi, Y.·2001·IEEE Power Engineering Review
DOI 10.1109/mper.2001.4311192
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Abstract

This article presents a comparative study for four modem heuristic algorithms (MHAs) to service restoration in distribution systems: reactive tabu search, tabu search, parallel simulated annealing, and genetic algorithm. Since service restoration is an emergency control in distribution control centers to restore out-of-service areas as soon as possible, it requires fast computation and high quality solutions for customers' satisfaction. The problem can be formulated as a combinatorial optimization problem to divide the out-of-service area to each power source. The effectiveness of the MHAs is compared against each other on typical service restoration problems.

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Metadata

Authors
Toune, S., Fudo, H., Genji, T., Fukuyama, Y., & Nakanishi, Y.
Venue
IEEE Power Engineering Review
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2001-11
Volume
21
Issue
11
Pages
64-64
ISSN
  • 0272-1724 (ISSN-L)
  • 1558-1705
DOI
10.1109/mper.2001.4311192
OpenAlex
W2102373925
Cited by
191
References
0
FWCI
2.51
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Comparative Study of Modern Heuristic Algorithms to Service Restoration in Distribution Systems. https://app.cochat.ai/papers/10.1109/mper.2001.4311192
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Toune, S., Fudo, H., Genji, T., Fukuyama, Y., & Nakanishi, Y. (2001). Comparative Study of Modern Heuristic Algorithms to Service Restoration in Distribution Systems. IEEE Power Engineering Review. https://doi.org/10.1109/mper.2001.4311192

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.