模式识别与人工智能
Monday, Jul. 28, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2012, Vol. 25 Issue (2): 213-219    DOI:
Orignal Article Current Issue| Next Issue| Archive| Adv Search |
WEI Zhen, WU Lei, GE Fang-Zhen, WANG Qiang
School of Computer and Information,Hefei University of Technology,Hefei 230009

Download: PDF (443 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  A hybrid PSO algorithm based on memetic framework (HM-PSO) is proposed. It helps the particles which have certain leaning capacity accelerate convergence rate by Lamarckian Learning based local search strategy and helps the particles which fall into the local optimum escape from local optimum by Tabu search. HM-PSO avoids falling into the local optimum by enhancing the diversity of swarm with accelerating convergence rate. The experimental results show that the improved Lamarckian Learning strategy is effective and feasible and HM-PSO is an effective optimization algorithm with better global search performance.
Key wordsHybrid PSO      Tabu Search      Lamarckian Learning     
Received: 22 November 2010     
ZTFLH: TP18  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
WEI Zhen
WU Lei
GE Fang-Zhen
WANG Qiang
Cite this article:   
WEI Zhen,WU Lei,GE Fang-Zhen等. [J]. , 2012, 25(2): 213-219.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2012/V25/I2/213
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn