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2021 | OriginalPaper | Chapter

A New Approach for Momentum Particle Swarm Optimization

Authors : Rohan Mohapatra, Rohan R. Talesara, Saloni Govil, Snehanshu Saha, Soma S. Dhavala, TSB Sudarshan

Published in: Advances in Machine Learning and Computational Intelligence

Publisher: Springer Singapore

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Abstract

In this paper, a new approach to momentum particle swarm optimization is proposed. We design a particle swarm optimizer that converges faster than the currently available momentum particle swarm optimizers. This simulates gradient descent with momentum and is inspired from the back-propagation algorithm with momentum in neural networks. The proposed optimizing algorithm constantly gives faster convergence time in all the available test optimization functions (constrained or unconstrained). This paper contains graphs and results that summarize the algorithm’s performance in contrast with weighted particle swarm optimizer and momentum particle swarm optimizer.

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Metadata
Title
A New Approach for Momentum Particle Swarm Optimization
Authors
Rohan Mohapatra
Rohan R. Talesara
Saloni Govil
Snehanshu Saha
Soma S. Dhavala
TSB Sudarshan
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Machine Learning and Computational Intelligence

Print ISBN: 978-981-15-5242-7

Electronic ISBN: 978-981-15-5243-4

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-5243-4_5

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