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Real power loss reduction by enriched great frigatebird, grey forecast and constellation exploration optimization algorithms

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  • Lenin Kanagasabai

    (Prasad V.Potluri Siddhartha Institute of Technology)

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.

Suggested Citation

  • Lenin Kanagasabai, 2023. "Real power loss reduction by enriched great frigatebird, grey forecast and constellation exploration optimization algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1933-1954, October.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:5:d:10.1007_s13198-023-02032-w
    DOI: 10.1007/s13198-023-02032-w
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    References listed on IDEAS

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    1. Amir H.S. Garmabaki & P.K. Kapur & Anu G. Aggarwal & V.S.S. Yadavali, 2014. "The impact of bugs reported from operational phase on successive software releases," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 14(4), pages 423-440.
    2. Himanshu Sharma & Abhishek Tandon & P. K. Kapur & Anu G. Aggarwal, 2019. "Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 973-983, October.
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