Earticle

초록

영어

A hybrid intelligent optimization algorithm based on quantum particle swarm is presented to solve the problem that the local search ability of traditional SFLA is poor and converges very slowly. The particle is quantized and introduced chaos mechanism in the algorithm in order to enhance the global search ability, using the escape strategy, the group is divided into three clusters and mutation operation on the cluster within individuals, not only improves the convergence speed and ensure the performance of the algorithm. Experiments show that the improved algorithm has the characteristics of strong optimization capability and performance is improved greatly in whether comparison of the baseline function or analysis of universal database, compared with the other two algorithms have obvious advantages.

목차

Abstract
 1. Introduction
 2. Shuffled Frog Leaping Algorithm
 3. Algorithm Analysis
  3.1. Chaotic Sequence
  3.2. Particles Escape
  3.3. Quantum Particle Swarm Optimization Algorithm based on Chaotic Sequence
  3.4. The Hybrid Algorithm
 4. Experimental Analysis
 5. Conclusion
 References

키워드

저자

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