Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer
In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. Th...
Published in: | EXPERT SYSTEMS WITH APPLICATIONS |
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Main Authors: | , , , , , , |
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Language: | English |
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PERGAMON-ELSEVIER SCIENCE LTD
2024
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Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001185590700001 |
author |
Ahmadipour Masoud; Othman Muhammad Murtadha; Bo Rui; Javadi Mohammad Sadegh; Ridha Hussein Mohammed; Alrifaey Moath |
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Ahmadipour Masoud; Othman Muhammad Murtadha; Bo Rui; Javadi Mohammad Sadegh; Ridha Hussein Mohammed; Alrifaey Moath Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer Computer Science; Engineering; Operations Research & Management Science |
author_facet |
Ahmadipour Masoud; Othman Muhammad Murtadha; Bo Rui; Javadi Mohammad Sadegh; Ridha Hussein Mohammed; Alrifaey Moath |
author_sort |
Ahmadipour |
spelling |
Ahmadipour, Masoud; Othman, Muhammad Murtadha; Bo, Rui; Javadi, Mohammad Sadegh; Ridha, Hussein Mohammed; Alrifaey, Moath Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer EXPERT SYSTEMS WITH APPLICATIONS English Article In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals' procedure of exploration and exploitation in AOAOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter's (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works. PERGAMON-ELSEVIER SCIENCE LTD 0957-4174 1873-6793 2024 235 10.1016/j.eswa.2023.121212 Computer Science; Engineering; Operations Research & Management Science WOS:001185590700001 https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001185590700001 |
title |
Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer |
title_short |
Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer |
title_full |
Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer |
title_fullStr |
Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer |
title_full_unstemmed |
Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer |
title_sort |
Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer |
container_title |
EXPERT SYSTEMS WITH APPLICATIONS |
language |
English |
format |
Article |
description |
In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals' procedure of exploration and exploitation in AOAOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter's (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works. |
publisher |
PERGAMON-ELSEVIER SCIENCE LTD |
issn |
0957-4174 1873-6793 |
publishDate |
2024 |
container_volume |
235 |
container_issue |
|
doi_str_mv |
10.1016/j.eswa.2023.121212 |
topic |
Computer Science; Engineering; Operations Research & Management Science |
topic_facet |
Computer Science; Engineering; Operations Research & Management Science |
accesstype |
|
id |
WOS:001185590700001 |
url |
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001185590700001 |
record_format |
wos |
collection |
Web of Science (WoS) |
_version_ |
1809678908038053888 |