Earticle

초록

영어

This paper proposes a new clustering algorithm that combines genetic algorithm and chaotic particle swarm optimization with fuzzy C- means (GCQPSO-FCM), in order to solve the issue that the fuzzy C- mean algorithm is sensitive to the initial value. First, make full use of genetic algorithms to calculate the optimal number of clusters of sample population and select a valid criterion function as a fitness function; Furthermore, introduce chaos strategy in particle swarm algorithm to improve the algorithm global search ability, also contribute to the particles are more easily jump out of local bondage. Two speed factors are defined to accelerate the convergence, which also improves the performance of the algorithm. Experimental results show that our improved GCQPSO-FCM algorithm is better in efficiency and quality than the original algorithm.

목차

Abstract
 1. Introduction
 2. The Quantum Particle Swarm Based on Chaotic Sequence
  2.1 Chaotic Sequence
  2.2 The Quantum Particle Swarm Algorithm with Chaos
 3. Optimization of Fuzzy C- means Algorithm
  3.1 FCM Algorithm
  3.2 Optimization of Particle Velocity
 4. GCQPSO-FCM Algorithm
  4.1 The Effective Criterion Function
  4.2 Algorithm Analysis
 5. Experimental Analysis
  5.1 Experiment Contents
  5.2 Experimental Analysis
 6. Conclusion
 References

키워드

저자

• Zhang Chun-na [ School of Software, University of Science and Technology Liaoning, Anshan Liaoning 114051, China ]
• Li Yi-ran [ College of Applied Technology, University of Science and Technology Liaoning, Anshan Liaoning 114011, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605