Real Power Loss Reduction and Voltage Stability Enrichment by Extreme Learning Machine Based Phoenicoparrus Search, Quantum-Inspired Myrmicinae, Herpestes, Philander Olrogi and Chaotic Based Simien Fox Optimization Algorithms

Abstract

In this paper Extreme learning machine based Phoenicoparrus search (ELMPS) optimization algorithm, quantum-inspired Myrmicinae evolutionary algorithm (QIMEA), Herpestes optimization (HO) algorithm, Philander olrogi optimization (PO) algorithm and Chaotic based Simien Fox optimization (CSFO) algorithm has been applied to solve the power loss lessening problem. Phoenicoparrus search (PS) optimization algorithm is stimulated by the Phoenicoparrus itinerant and scavenging behaviour. Then the extreme learning machine based Phoenicoparrus search (ELMPS) optimization algorithm is designed. Quantum-inspired Myrmicinae evolutionary algorithm (QIMEA) design is imitated by the actions of Myrmicinae. In HO algorithm with the reference to the rate of breed centre of group and sex, Herpestes group individuals will perform the movement. Then Philander olrogi optimization algorithm has been designed based on the track and preying actions of Philander olrogi. Based on the natural actions of Simien Fox proposed CSFO algorithm is designed. Chaotic sequences are integrated into the Simien Fox optimization algorithm and it will enhance the exploration and exploitation. Legitimacy of proposed algorithms is corroborated in standard IEEE test systems. True power loss lessening, voltage divergence curtailing, and voltage constancy index augmentation has been attained

Please cite this article as:

Kanagasabai L. Real power loss reduction and voltage stability enrichment by Extreme learning machine based Phoenicoparrus search, quantum-inspired Myrmicinae, Herpestes, Philander olrogi and chaotic based Simien Fox optimization algorithms. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2024, no. 5 (116), pp. 33--55. EDN: OSBUFS

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