An advanced particle swarm optimization based on good-point set and application to motion estimation

Authors:
Xiang-pin Liu;Shi-bin Xuan;Feng Liu
Affiliations:
College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, China;College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, China,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, China;College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

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Abstract

In this paper, an advanced particle swarm optimization based on good-point set theory is proposed to reduce the deviation of the two random numbers selected in velocity updating formula. Good-point set theory can choose better points than random selection, which can accelerate the convergence of algorithm. The proposed algorithm was applied to the motion estimation in digital video processing. The simulation results show that new methods can improve the estimation accuracy, and the performance of the proposed algorithm is better than previous estimation methods.