An image registration approach to study the convergence of particle swarm optimization algorithm with non-linear inertia weight variation

Particle swarm optimization (PSO) algorithm is a swarm based metaheuristic method to solve multimodal optimization problems. The inertia weight parameter in the algorithm is very important as it balances the exploration and exploitation of the algorithm. Many variations of the parameter have been re...

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Bibliographic Details
Published in:	2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT) pp. 1 - 5
Main Authors:	Saxena, Sanjeev, Pohit, M.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.07.2017
Subjects:	Convergence Correlation Fitness Function Image registration Inertia Weight Monitoring Optimization Particle swarm optimization
Online Access:	Get full text
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Summary:	Particle swarm optimization (PSO) algorithm is a swarm based metaheuristic method to solve multimodal optimization problems. The inertia weight parameter in the algorithm is very important as it balances the exploration and exploitation of the algorithm. Many variations of the parameter have been reported in the literature where a linearly decreasing inertia weight was found to be the best choice for most of the problems. In this work we have used several non-linear variations in the inertia weight (not used earlier) and developed the algorithm for the image registration problem of two mutually translated images. For each run of the algorithm, the increments of fitness function and hence the convergence of PSO is carefully monitored and compared with standard parameters.
DOI:	10.1109/ICCCNT.2017.8204075