A multi-swarm synergetic optimizer for multi-knowledge extraction using rough set

Benxian Yue, Hongbo Liu, Ajith Abraham, Youakim Badr

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Finding reducts is one of the key problems in the increasing applications of rough set theory, which is also one of the bottlenecks of the rough set methodology. The population-based reduction approaches are attractive to find multiple reducts in the decision systems, which could be applied to generate multi-knowledge and to improve decision accuracy. In this paper, we design a multi-swarm synergetic optimization algorithm (MSSO) for rough set reduction and multi-knowledge extraction. It is a multi-swarm based search approach, in which different individual trends to be encoded to different reduct. The approach discovers the best feature combinations in an efficient way to observe the change of positive region as the particles proceed throughout the search space. The performance of our approach is evaluated and compared with Standard Particle Swarm Optimization (SPSO) and Genetic Algorithms (GA). Empirical results illustrate that the approach can be applied for multiple reduct problems and multi-knowledge extraction effectively.

Original languageEnglish (US)
Pages (from-to)501-517
Number of pages17
JournalNeural Network World
Issue number4
StatePublished - Sep 22 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Neuroscience(all)
  • Hardware and Architecture
  • Artificial Intelligence


Dive into the research topics of 'A multi-swarm synergetic optimizer for multi-knowledge extraction using rough set'. Together they form a unique fingerprint.

Cite this