TY - GEN
T1 - Solving the ED problem with ACO algorithm modified by mRMR and local search method
AU - Yu, Jiahui
AU - Pan, Ji An
AU - Lv, Yuezu
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/6
Y1 - 2020/11/6
N2 - 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.
AB - 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.
KW - Ant colony optimization
KW - economic dispatch
KW - hill-climbing algorithm
KW - mRMR method
UR - http://www.scopus.com/inward/record.url?scp=85100914416&partnerID=8YFLogxK
U2 - 10.1109/CAC51589.2020.9326683
DO - 10.1109/CAC51589.2020.9326683
M3 - Conference contribution
AN - SCOPUS:85100914416
T3 - Proceedings - 2020 Chinese Automation Congress, CAC 2020
SP - 2131
EP - 2136
BT - Proceedings - 2020 Chinese Automation Congress, CAC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Chinese Automation Congress, CAC 2020
Y2 - 6 November 2020 through 8 November 2020
ER -