作者：张佳期，解倩倩

单位：国家知识产权局专利局专利审查协作天津中心，天津 300304

中图分类号：TP18                

文献标识码：A                

文章编号：1006-883X(2019)05-0025-04

收稿日期：2019-05-13

 

摘要：粒子群优化算法又称微粒群算法，是一种智能优化算法，主要用于优化函数、训练神经网络、以及其他进化算法的应用领域。本文简介了粒子群优化算法的发展历史及现状、主要分类，并以国内外专利申请数据为分析样本，从专利逐年变化的申请量和申请人分布等角度进行了分析和研究。

关键词：粒子群优化（微粒群优化）；算法； 神经网络

 

Analysis on Patent Technologies of Particle Swarm Optimization Algorithm

ZHANG Jia-qi, XIE Qian-qian

Patent Examination Cooperation (Tianjin) Center of the Patent Office, Tianjin 300304, China

Abstract: Particle Swarm Optimization (PSO) is an intelligent optimization algorithm, which is mainly used in function optimization, neural networks training, and other applications of evolutionary algorithms. In this paper, the development history, status quo and main classification of PSO algorithm are introduced briefly, analyzed and studied from the perspective of the number of patent applications and the distribution of applicants with the domestic and foreign patent application data as the analysis samples.

Key words: Particle Swarm Optimization (PSO); algorithm; Neural network

 

 

备注：2019年 第25卷 第05期