Pavan Kumar Pagadala

Dr. Pavan Kumar Pagadala

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Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 March 2022 in Wireless Personal Communications
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Journal Article
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NMN M Saravana Kumar N M
N M Saravana Kumar N M
PPPavan Kumar Pagadala
Pavan Kumar Pagadala
VVVijayakumar V.
Vijayakumar V.
AKA. Kavinya
A. Kavinya

27 December 2023 in Revue d'intelligence artificielle
Verified
Journal Article
Open access
PPPavan Kumar Pagadala
Pavan Kumar Pagadala
SPSree Lakshmi Pinapatruni
Sree Lakshmi Pinapatruni
CKChanda Raj Kumar
Chanda Raj Kumar
SKSrinivas Katakam
Srinivas Katakam
LPLalitha Surya Kumari Peri
Lalitha Surya Kumari Peri
DRDasari Anantha Reddy
Dasari Anantha Reddy

1 January 2023 in Computational Intelligence and Neuroscience
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Journal Article
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PPPavan Kumar Pagadala
Pavan Kumar Pagadala
PKP Lalitha Surya Kumari
P Lalitha Surya Kumari
DTDeepak Thakur
Deepak Thakur
VBVivek Bhardwaj
Vivek Bhardwaj
MSMohammad Shahid
Mohammad Shahid
AKAbiot Ketema
Abiot Ketema
Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well‐suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat‐based SA‐GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature‐based SA‐GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA‐GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

8 February 2021 in Wireless Personal Communications
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Journal Article
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PPPavan Kumar Pagadala
Pavan Kumar Pagadala
NMN M Saravana Kumar N M
N M Saravana Kumar N M

23 August 2024 in Revue d'intelligence artificielle
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Journal Article
Open access
IGIndu Gorrepati
Indu Gorrepati
PPPavan Kumar Pagadala
Pavan Kumar Pagadala