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A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution

This study introduces a novel meta-heuristic optimization algorithm known as quasi-oppositional modified Jaya (QOMJaya) to solve different multi-objective optimal power flow (MOOPF) problems. An intelligence strategy called quasi-oppositional based learning is incorporated into the proposed algorith...

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Main Authors: Warid, Warid, Hizam, Hashim, Mariun, Norman, Abdul Wahab, Noor Izzri
Format: Article
Language:English
Published: Elsevier 2018
Online Access:http://psasir.upm.edu.my/id/eprint/72943/1/JAYA.pdf
http://psasir.upm.edu.my/id/eprint/72943/
https://www.sciencedirect.com/science/article/abs/pii/S1568494618300450
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spelling my.upm.eprints.729432021-02-09T18:03:39Z http://psasir.upm.edu.my/id/eprint/72943/ A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution Warid, Warid Hizam, Hashim Mariun, Norman Abdul Wahab, Noor Izzri This study introduces a novel meta-heuristic optimization algorithm known as quasi-oppositional modified Jaya (QOMJaya) to solve different multi-objective optimal power flow (MOOPF) problems. An intelligence strategy called quasi-oppositional based learning is incorporated into the proposed algorithm to enhance its convergence property, exploration capability, and solution optimality. Significant modifications to the basic Jaya algorithm are done to create a modified Jaya (MJaya) algorithm that can handle the MOOPF problem. A fuzzy decision-making strategy is proposed and incorporated into the Jaya algorithm as selection criteria for best and worst solutions. A new criterion for comparing updated and current candidate solutions is proposed. The concept of Pareto optimality is used to extract a set of non-dominated solutions. A crowding distance measure approach is utilized to maintain the diversity of Pareto optimality. In addition, a novel external elitist repository is developed to conserve discovered non-dominated solutions and to produce true and well-distributed Pareto optimal fronts. The proposed algorithm is scrutinized and validated using the modified IEEE 30-bus test system. Simulation results reveal the proposed algorithm’s ability to produce real and well-distributed Pareto optimum fronts for all considered multi-objective optimization cases. Furthermore, the obtained results disclose the superiority of the proposed QOMJaya algorithm over both the proposed MJaya algorithm and several previous algorithms in terms of solution optimality and feasibility. Elsevier 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/72943/1/JAYA.pdf Warid, Warid and Hizam, Hashim and Mariun, Norman and Abdul Wahab, Noor Izzri (2018) A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution. Applied Soft Computing Journal, 65. 360 - 373. ISSN 1568-4946 https://www.sciencedirect.com/science/article/abs/pii/S1568494618300450 10.1016/j.asoc.2018.01.039
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This study introduces a novel meta-heuristic optimization algorithm known as quasi-oppositional modified Jaya (QOMJaya) to solve different multi-objective optimal power flow (MOOPF) problems. An intelligence strategy called quasi-oppositional based learning is incorporated into the proposed algorithm to enhance its convergence property, exploration capability, and solution optimality. Significant modifications to the basic Jaya algorithm are done to create a modified Jaya (MJaya) algorithm that can handle the MOOPF problem. A fuzzy decision-making strategy is proposed and incorporated into the Jaya algorithm as selection criteria for best and worst solutions. A new criterion for comparing updated and current candidate solutions is proposed. The concept of Pareto optimality is used to extract a set of non-dominated solutions. A crowding distance measure approach is utilized to maintain the diversity of Pareto optimality. In addition, a novel external elitist repository is developed to conserve discovered non-dominated solutions and to produce true and well-distributed Pareto optimal fronts. The proposed algorithm is scrutinized and validated using the modified IEEE 30-bus test system. Simulation results reveal the proposed algorithm’s ability to produce real and well-distributed Pareto optimum fronts for all considered multi-objective optimization cases. Furthermore, the obtained results disclose the superiority of the proposed QOMJaya algorithm over both the proposed MJaya algorithm and several previous algorithms in terms of solution optimality and feasibility.
format Article
author Warid, Warid
Hizam, Hashim
Mariun, Norman
Abdul Wahab, Noor Izzri
spellingShingle Warid, Warid
Hizam, Hashim
Mariun, Norman
Abdul Wahab, Noor Izzri
A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution
author_facet Warid, Warid
Hizam, Hashim
Mariun, Norman
Abdul Wahab, Noor Izzri
author_sort Warid, Warid
title A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution
title_short A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution
title_full A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution
title_fullStr A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution
title_full_unstemmed A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution
title_sort novel quasi-oppositional modified jaya algorithm for multi-objective optimal power flow solution
publisher Elsevier
publishDate 2018
url http://psasir.upm.edu.my/id/eprint/72943/1/JAYA.pdf
http://psasir.upm.edu.my/id/eprint/72943/
https://www.sciencedirect.com/science/article/abs/pii/S1568494618300450
_version_ 1691734448498802688
score 13.211869

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