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.
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Liu, Xp., Xuan, Sb., Liu, F. (2013). An Advanced Particle Swarm Optimization Based on Good-Point Set and Application to Motion Estimation. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_57
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DOI: https://doi.org/10.1007/978-3-642-39482-9_57
Publisher Name: Springer, Berlin, Heidelberg
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