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Power loss reduction by chaotic based predator-prey brain storm optimization algorithm

International Journal of Applied Power Engineering (IJAPE) · Vol. 9 No. 3 (2020) · pp. 218–222

DOI
10.11591/ijape.v9.i3.pp218-222
ISSN
2722-2624, 2252-8792
Published
2020-12-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In this paper chaotic predator-prey brain storm optimization (CPB) algorithm is proposed to solve optimal reactive power problem. In this work predator-prey brain storm optimization position cluster centers to perform as predators, consequently it will move towards better and better positions, while the remaining ideas perform as preys; hence get away from their adjacent predators. In the projected CPB algorithm chaotic theory has been applied in the modeling of the algorithm. In the proposed algorithm main properties of chaotic such as ergodicity and irregularity used to make the algorithm to jump out of the local optimum as well as to determine optimal parameters CPB algorithm has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.

Collaboration network of the authors

4 authors · 5 co-authorship links

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M.Surya KalavathiAffiliation unknown3
B. Ravindhranath ReddyAffiliation unknown3
B.Ravindranath ReddyAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijape.iaescore.com/index.php/IJAPE/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Power loss reduction by chaotic based predator-prey brain storm optimization algorithm</dc:title>
	<dc:creator>Lenin, Kanagasabai</dc:creator>
	<dc:description xml:lang="en-US">In this paper chaotic predator-prey brain storm optimization (CPB) algorithm is proposed to solve optimal reactive power problem. In this work predator-prey brain storm optimization position cluster centers to perform as predators, consequently it will move towards better and better positions, while the remaining ideas perform as preys; hence get away from their adjacent predators. In the projected CPB algorithm chaotic theory has been applied in the modeling of the algorithm. In the proposed algorithm main properties of chaotic such as ergodicity and irregularity used to make the algorithm to jump out of the local optimum as well as to determine optimal parameters CPB algorithm has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijape.iaescore.com/index.php/IJAPE/article/view/20245</dc:identifier>
	<dc:identifier>10.11591/ijape.v9.i3.pp218-222</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Applied Power Engineering (IJAPE); Vol 9, No 3: December 2020; 218-222</dc:source>
	<dc:source>2722-2624</dc:source>
	<dc:source>2252-8792</dc:source>
	<dc:source>10.11591/ijape.v9.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijape.iaescore.com/index.php/IJAPE/article/view/20245/12907</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>