模式识别与人工智能
Saturday, May. 3, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2018, Vol. 31 Issue (4): 322-334    DOI: 10.16451/j.cnki.issn1003-6059.201804004
Papers and Reports Current Issue| Next Issue| Archive| Adv Search |
Co-evolutionary Particle Swarm Optimization for Multitasking
CHENG Meiying1, QIAN Qian2, NI Zhiwei3, ZHU Xuhui3,4
1.Business School, Huzhou University, Huzhou 313000
2.School of Teacher Education, Huzhou University, Huzhou 313000
3.Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, School of Management, Hefei University of Technology, Hefei 230009
4.Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Ohio University, Athens 45701

Download: PDF (1134 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  

The traditional particle swarm optimization(PSO) and its improved version aim to tackle the single task. With the development of electronic business, online severs need to deal with a batch of requests simultaneously, i.e. multitasking. Different from the parallel computer, the implicit parallelism of PSO is fully exploited, and co-evolution theory is introduced for multitasking. In the multitasking environment, different tasks correspond to different subpopulations, and the useful information is transferred from one subpopulation to another with a certain probability. Thus, co-evolutionary PSO for multitasking(CPSOM) is proposed in this paper. To verify the effectiveness of the proposed algorithm, CPSOM is used to solve a batch of function test problems, feature selection problems and constrained engineering optimization problems. Experimental results show that the useful information can be autonomously transferred from one task to another in the CPSOM environment. Moreover, cooperation of different tasks enhance the solution quality and speed up the convergence.
Key words： Multitasking      Particle Swarm Optimization(PSO)      Co-evolution      Function Optimization      Attribute Selection      Engineering Application     
Received: 21 August 2017     
ZTFLH: TP 18  
Fund:

Supported by Major Program of National Natural Science Foundation of China(No.71490725), Training Program of Major Research Plan of National Natural Science Foundation of China(No.91546108)
Corresponding Authors: CHENG Meiying(Corresponding author), Ph.D., lecturer. Her research interests include swarm intelligent algorithm and data mining.   
About author:: QIAN Qian, master, lecturer. His research interests include evolutionary computation;NI Zhiwei, Ph.D., professor. His research interests include data mining, machine lear-ning and artificial intelligence;ZHU Xuhui, Ph.D. candidate. His research interest include intelligent computation and machine learning.
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
CHENG Meiying
QIAN Qian
NI Zhiwei
ZHU Xuhui
Cite this article:   
CHENG Meiying,QIAN Qian,NI Zhiwei等. Co-evolutionary Particle Swarm Optimization for Multitasking[J]. , 2018, 31(4): 322-334.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201804004      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I4/322
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