Abstract
Particle swarm optimization (PSO) is prone to fall into the premature convergence, and convergence precision is low and other shortcomings in solving complex problems. This paper presents an algorithm based on the adaptive inertia weight of the second-order oscillation particle swarm optimization (SOPSO); the algorithm combines SOPSO and adaptive inertia weight advantages, and it is good solution to the above-mentioned problem. Finally, the simulation on four test functions and the application on the key frame extraction from human motion capture data show that the algorithm not only has a strong search capability, and the convergence precision and stability have been effectively improved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kennedy J (2010) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, US, pp 760–766
Leung SYS, Tang Y, Wong WK (2012) A hybrid particle swarm optimization and its application in neural networks. Expert Syst Appl 39(1):395–405
Yang X, Yuan J, Yuan J, Mao H (2007) A modified particle swarm optimizer with dynamic adaptation. Appl Math Comput 189(2):1205–1213
Ouyang C, Li X, Wang N (2010) A best wavelet packet basis image compression algorithm based on PSO. In: 2010 fourth international conference on genetic and evolutionary computing. IEEE, pp 11–13
Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 congress on evolutionary computation, vol 1. IEEE, pp 101–106
Yan ZC, Luo YS (2014) A particle swarm optimization algorithm based on simulated annealing. In: Advanced materials research, vol 989. pp 2301–2305
Zhang Y, Wu Y, Wu S, Song Y (2012) Multilevel thresholding based on exponential cross entropy and niche chaotic particle swarm optimization. In: Advances in computer, communication, control and automation. Springer, Berlin, pp 617–624
Li J, Sun XX, Li SB, Li R (2008) Improved particle swarm optimization based on genetic hybrid genes. Comput Eng 2:1021–1025
Dorigo M, de Oca MAM, Engelbrecht A (2008) Particle swarm optimization. Scholarpedia 3(11):1486
Bratton D, Kennedy J (2007) Defining a standard for particle swarm optimization. In: Swarm intelligence symposium, 2007. SIS 2007. IEEE, pp 120–127
Hu JX, Zeng JC (2007) Two-order oscillating particle swarm optimization. J Syst Simul 19(5):997–999
Qin Z, Yu F, Shi Z, Wang Y (2006) Adaptive inertia weight particle swarm optimization. In: Artificial intelligence and soft computing–ICAISC 2006. Springer, Berlin, pp 450–459
Zhao H, Feng L (2014) An improved adaptive dynamic particle swarm optimization algorithm. J Netw 9(2):488–494
Pohlheim H (2010) Geatbx: example functions (single and multi-objective functions) 2 parametric optimization. GEATbx Database
Zhang Q, Xue X, Zhou D, Wei X (2014) Motion Key-frames extraction based on amplitude of distance characteristic curve. Int J Comput Intell Syst 7(3):506–514
Bulut E, Capin T (2007) Key frame extraction from motion capture data by curve saliency. In: Computer animation and social agents, p 119
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, D., Wang, L., Wei, J. (2016). An Improved Particle Swarm Optimization and Application. In: Huang, B., Yao, Y. (eds) Proceedings of the 5th International Conference on Electrical Engineering and Automatic Control. Lecture Notes in Electrical Engineering, vol 367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48768-6_112
Download citation
DOI: https://doi.org/10.1007/978-3-662-48768-6_112
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-48766-2
Online ISBN: 978-3-662-48768-6
eBook Packages: EngineeringEngineering (R0)