Real power loss reduction by enriched great frigatebird, grey forecast and constellation exploration optimization algorithms
Abstract In this paper Enriched Great frigatebird optimization (EGFO) algorithm, Grey forecast algorithm (GFA) and Constellation exploration optimization (CEO) algorithm are applied to solve the power loss decreasing problem. Basically Enriched Great frigatebird optimization (EGFO) algorithm is designed based on the immigration activity and bout action to acquire food from other birds. In EGFO algorithm Canis lupaster algorithm stalking activity and Virus infection algorithms, Robust, Feeble mode (to improve both exploration and exploitation) are integrated. Grey forecast algorithm (GFA) is a modest optimization algorithm with resilient examination ability. Reproduction procedure will create a function that estimates the law (exponential) for predicting the provisional population in descendant’s population through consecutive generations of the population sequence as a time sequence. Constellation exploration optimization (CEO) algorithm is stimulated by faunae examining behaviour in actual natural life. The constellation exploration optimization (CEO) algorithm is executed by means of a set of contender representatives (population) which is entitled as the constellation, and every representative is entitled as an associate. Proposed EGFO, GFA and CEO algorithms are validated in G01–G24 benchmark functions and IEEE test systems..
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Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
International journal of systems assurance engineering and management - 14(2023), 5 vom: 12. Aug., Seite 1933-1954 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kanagasabai, Lenin [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Anmerkungen: |
© The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s13198-023-02032-w |
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| funding: |
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| PPN (Katalog-ID): |
OLC2145212647 |
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| 520 | |a Abstract In this paper Enriched Great frigatebird optimization (EGFO) algorithm, Grey forecast algorithm (GFA) and Constellation exploration optimization (CEO) algorithm are applied to solve the power loss decreasing problem. Basically Enriched Great frigatebird optimization (EGFO) algorithm is designed based on the immigration activity and bout action to acquire food from other birds. In EGFO algorithm Canis lupaster algorithm stalking activity and Virus infection algorithms, Robust, Feeble mode (to improve both exploration and exploitation) are integrated. Grey forecast algorithm (GFA) is a modest optimization algorithm with resilient examination ability. Reproduction procedure will create a function that estimates the law (exponential) for predicting the provisional population in descendant’s population through consecutive generations of the population sequence as a time sequence. Constellation exploration optimization (CEO) algorithm is stimulated by faunae examining behaviour in actual natural life. The constellation exploration optimization (CEO) algorithm is executed by means of a set of contender representatives (population) which is entitled as the constellation, and every representative is entitled as an associate. Proposed EGFO, GFA and CEO algorithms are validated in G01–G24 benchmark functions and IEEE test systems. | ||
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| 650 | 4 | |a Grey forecast | |
| 650 | 4 | |a Constellation exploration | |
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