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L. R. Srinivas 0001

L. Ravi Srinivas 0001

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affiliation: Gudlavalleru Engineering College, Department of Electrical and Electronics Engineering, India

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2020 – today

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2022
[j9]
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  - journals/soco/AmarendraSR22
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A. Amarendra, L. R. Srinivas, R. Srinivasa Rao:
Power system security enhancement in FACTS devices based on Yin-Yang pair optimization algorithm. Soft Comput. 26(13): 6265-6291 (2022)
2021
[j8]
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A. Amarendra, L. Ravi Srinivas, R. Srinivasa Rao:
Security Enhancement in Power System Using FACTS Devices and Atom Search Optimization Algorithm. EAI Endorsed Trans. Energy Web 8(36): e9 (2021)
[j7]
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L. Ravi Srinivas, B. Mahesh Babu, S. S. Tulasi Ram:
DVR-based power quality enhancement using adaptive particle swarm optimisation technique. Int. J. Bio Inspired Comput. 18(2): 92-104 (2021)

2010 – 2019

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2015
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  - conf/iciis/0001RK15
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L. R. Srinivas, M. Ramu, S. Tara Kalyani:
Implementation of hybrid differential evolution to solve unit commitment. ICIIS 2015: 440-445
2013
[j6]
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K. Vaisakh, L. R. Srinivas, Kala Meah:
Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis. Appl. Soft Comput. 13(12): 4579-4593 (2013)
2011
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K. Vaisakh, L. R. Srinivas:
Evolving ant colony optimization based unit commitment. Appl. Soft Comput. 11(2): 2863-2870 (2011)
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K. Vaisakh, L. R. Srinivas:
Evolving ant direction differential evolution for OPF with non-smooth cost functions. Eng. Appl. Artif. Intell. 24(3): 426-436 (2011)
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K. Vaisakh, L. R. Srinivas:
Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis. Expert Syst. Appl. 38(3): 2046-2062 (2011)
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K. Vaisakh, L. R. Srinivas:
Adaptive PSODV algorithm for OPF with non-smooth cost functions and statistical analysis. Simul. Model. Pract. Theory 19(9): 1824-1846 (2011)
2010
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K. Vaisakh, L. R. Srinivas:
Unit Commitment by Evolving Ant Colony Optimization. Int. J. Swarm Intell. Res. 1(3): 67-77 (2010)

2000 – 2009

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2009
[c1]
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K. Vaisakh, L. R. Srinivas:
Unit Commitment by Genetic Evolving Ant Colony Optimization. NaBIC 2009: 1162-1167

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