应用于矢量量化的改进粒子群优化算法  

An Improved Particle Swarm Optimization Algorithm for Vector Quantization

作　　者：李小捷[1] 许录平[1] 杨莉[2] 

机构地区：[1]西安电子科技大学电子工程学院,西安710071 [2]上海交通大学微电子学院,上海200030

出　　处：《模式识别与人工智能》2008年第3期285-289,共5页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.60472085);陕西省自然科学基金(No.2006F04)资助项目

摘　　要：针对粒子群优化算法(PSO)应用于矢量量化时,最优粒子对与其对应维度距离较大的粒子缺乏有效指导问题,提出适用于矢量量化的改进粒子群优化算法(IPSO_VQ).该算法通过建立粒子与榜样粒子的维度映射关系,以基于映射关系的维度学习代替对应维度学习关系,使粒子相关维度间的学习有一定相关性,增强算法局部搜索能力.同时,借鉴广泛学习粒子群优化(CLPSO)算法中的广泛学习思想,并将其应用于基本 PSO 中的全局最优位置学习部分,通过对多个粒子的广泛学习,增加种群的多样性.实验结果表明该算法有效避免种群早熟收敛,从而使解码恢复图像获得更高的主客观质量.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.

关 键 词：矢量量化 码书设计 进化计算 粒子群优化(PSO) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]