Abstract An improved particle swarm optimization algorithm for vector quantization is proposed. The Concept of comprehensive learning in comprehensive learning particle swarm optimization (CLPSO) is adopted and merged into the learning strategies of original particle swarm optimization (PSO). The mapping between a particle and its example particle is built. And the particle can learn from the mapped dimensions in the example particle instead of the corresponding dimensions. Thus, the local search ability is greatly enhanced as well as the diversity of the swarm is effectively maintained. The experimental results show that the algorithm can effectively alleviate the problem of premature convergence and obtain good reconstruction image quality.
[1] Linde Y, Buzo A, Gray R. An Algorithm for Vector Quantizer Design. IEEE Trans on Communications,1980, 28(1):84-95 [2] Vasuki A, Vanathi P T. A Review of Vector Quantization Techniques. Potentials, 2006, 25(4): 39-47 [3] de Stefano C, D’Elia C, Marcelli A, et al. Improving Dynamic Learning Vector Quantization // Proc of the IEEE International Conference on Pattern Recognition. Hong Kong, China, 2006: 804-807 [4] Chen Qian, Yang Jiangang, Gou Jin, et al. Image Compression Method Using Improved PSO Vector Quantization // Proc of the International Conference on Advances in Natural Computation. Changsha, China, 2005: 490-495 [5] Han C C, Chen Y N, Lo C C.A Novel Approach for VQ Using a Neural Network, Mean Shift, and Principal Component Analysis // Proc of the Intelligent Vehicles Symposium.Tokyo, Japan, 2006: 244-249 [6] Kennedy J, Everhart R. Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Networks. Perth, Australia, 1995: 1942-1948 [7] Chen C Y, Ye F. Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis // Proc of the IEEE International Conference on Networking, Sensing and Control. Taipei, China, 2004, Ⅱ: 789-794 [8] Liang J J, Qin A K, Suganthan P N, et al. Particle Swarm Optimization Algorithms with Novel Learning Strategies // Proc of the IEEE International Conference on Systems, Man and Cybernetics. Ottawa, Canada, 2004, Ⅳ: 3659-3664 [9] Xu Shenheng, Rahmat-Samii Y. Boundary Conditions in Particle Swarm Optimization Revisited. IEEE Trans on Antennas and Propagation, 2007, 55(3): 760-765 [10] Liu Yu, Qin Zheng, Shi Zhewen. Compact Particle Swarm Optimization Algorithm. Journal of Xi'an Jiaotong University, 2006, 40(8): 883-887 (in Chinese) (刘 宇,覃 征,史哲文.简约粒子群优化算法.西安交通大学学报, 2006, 40(8): 883-887) [11] Shi Y, Eberhart R C. A Modified Particle Swarm Optimizer // Proc of the IEEE International Conference of Evolutionary Computation. Anchorage, USA, 1998: 69-73 [12] Clerc M, Kennedy J. The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Trans on Evolutionary Computation, 2002, 6(1): 58-73 [13] Liu Hongbo, Wang Xiukun, Tan Guozhen. Convergence Analysis of Particle Swarm Optimization and Its Improved Algorithm Based on Chaos. Control and Decision, 2006, 21(6): 636-640,645 (in Chinese) (刘洪波,王秀坤,谭国真.粒子群优化算法的收敛性分析及其混沌改进算法.控制与决策, 2006, 21(6): 636-640,645)
2008, Vol. 21 
