Image segmentation algorithm based on wavelet mutation inertia adaptive particle swarm optimization

Particle swarm optimization (PSO) is a new evolutionary computing method while it can not get good optimization performance because it easy to get stuck into local optima. This paper proposes a novel algorithm named improved PSO which combine proposed inertia adaptive PSO with partial particles Morlet mutation basing on basic PSO. Applies proposed algorithm and fuzzy entropy to image segmentation which uses proposed algorithm to explore fuzzy parameters of image maximum fuzzy entropy, and gets the optimum fuzzy parameter combination, then obtains the segmentation threshold of image. The experiment results of the new algorithm compare with other two algorithms show that the proposed algorithm has the capability of good segmentation performance, robust, low time cost and self adaptive.

[1]  Wu Jiekang,et al.  A Hybrid Method for Optimal Scheduling of Short-Term Electric Power Generation of Cascaded Hydroelectric Plants Based on Particle Swarm Optimization and Chance-Constrained Programming , 2008, IEEE Transactions on Power Systems.

[2]  Hak-Keung Lam,et al.  Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Leandro dos Santos Coelho,et al.  Fuzzy Identification Based on a Chaotic Particle Swarm Optimization Approach Applied to a Nonlinear Yo-yo Motion System , 2007, IEEE Transactions on Industrial Electronics.

[4]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..

[5]  Settimo Termini,et al.  A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory , 1972, Inf. Control..

[6]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[7]  Chin-Teng Lin,et al.  A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  T.O. Ting,et al.  A novel approach for unit commitment problem via an effective hybrid particle swarm optimization , 2006, IEEE Transactions on Power Systems.

[9]  Linyi Li,et al.  Fuzzy entropy image segmentation based on particle swarm optimization , 2008 .

[10]  Hitoshi Iba,et al.  Particle swarm optimization with Gaussian mutation , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[11]  Xingsheng Gu,et al.  A dynamic inertia weight particle swarm optimization algorithm , 2008 .

[12]  Po-Hung Chen,et al.  Pumped-Storage Scheduling Using Evolutionary Particle Swarm Optimization , 2008, IEEE Transactions on Energy Conversion.

[13]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[14]  M. Senthil Arumugam,et al.  On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems , 2008, Appl. Soft Comput..

[15]  B. Kulkarni,et al.  An ant colony approach for clustering , 2004 .