@inproceedings{91c3cf03015c4c5284de97dd76fc6d24,
title = "Solving the ED problem with ACO algorithm modified by mRMR and local search method",
abstract = "This paper presents an efficient Max-Relevance and Min-Redundancy (mRMR) based ant colony algorithm (ACO) method, called mACOR, for solving the economic dispatch (ED) problem. The mRMR algorithms are applied to reduce the redundancy among units in the power system and improve the ability of solving large-scale optimizations. And a random hill-climbing algorithm is employed to improve local search ability and avoid falling into local optima. Two objectives, the total coal consumption and the amount of pollution, were assigned different weights to transform the bi-objective problem to single objective problem. The effectiveness of the proposed approach is validated in the case study of 15 units, which plays better performance compared with the existing ACOR and PSO methods.",
keywords = "Ant colony optimization, economic dispatch, hill-climbing algorithm, mRMR method",
author = "Jiahui Yu and Pan, \{Ji An\} and Yuezu Lv",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Chinese Automation Congress, CAC 2020 ; Conference date: 06-11-2020 Through 08-11-2020",
year = "2020",
month = nov,
day = "6",
doi = "10.1109/CAC51589.2020.9326683",
language = "English",
series = "Proceedings - 2020 Chinese Automation Congress, CAC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2131--2136",
booktitle = "Proceedings - 2020 Chinese Automation Congress, CAC 2020",
address = "United States",
}