Earticle

초록

영어

Aiming at the problem that the particle in the traditional particle swarm optimization algorithm is easy to fall into the local optimum and the convergence rate is slow, this paper proposed an improved particle swarm optimization algorithm. In particle swarm optimization algorithm, the advantages and disadvantages of the algorithm is directly decided by the performance of the particle, the paper introduced the chaos mechanism, enhance the ergodicity and particle will be quantized in the solution space, on the premise of ensuring diversity of solution, the particle get better global search ability. Meanwhile, based on the problem of slow convergence speed of the algorithm in the late, on the one hand to dynamically adjust the inertia weight of impact speed, makes the particle movement speed tend to be reasonable, on the other hand, using k-means algorithm to optimize progeny particle and get more reasonable clustering center, make the algorithm fast convergence. Experiments show that using improved Particle Swarm Optimization algorithm with high precision, strong stability and fast convergence.

목차

Abstract
 1. Introduction
 2. The Problems Associated with Particle Swarm Optimization Algorithm
  2.1. Basic Particle Swarm Optimization Algorithm
  2.2. Chaotic Sequence
  2.3. Inertia Weight
 3. The Quantum-Behaved Particle Swarm Hybrid Algorithm
  3.1. The Quantum-Behaved Particle Swarm
  3.2. Coding and Fitness Value Calculation
  3.3. The Shortest Path K - Means Clustering
  3.4. Algorithm Analysis
 4. Experimental Analysis
 5. Conclusions
 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 Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605