Earticle

초록

영어

Particle Swarm Optimization (PSO) is one of the most powerful algorithms for optimization. Traditional PSO algorithm tends to suffer from slow convergence and trapping into local optimum. In this paper, an improved PSO algorithm is proposed by combining dynamic fractional order technology and the wavelet mutation strategy. In the proposed method, a dynamic fractional order velocity update equation is designed to control the convergence rate. Furthermore, the wavelet mutation mechanism is employed to improve the swarm diversity and escape from the local optimums. The experimental results show that the proposed algorithm can provide fast convergence speed and high convergence precision based on the ten classic test functions.

목차

Abstract
 1. Introduction
 2. Proposed Algorithm
  2.1. PSO with Fractional Order Velocity
  2.2. Dynamic Fractional Order
  2.3. Wavelet Mutation
  2.4. Algorithm Procedure and Analysis
 3. Experimental Results
  3.1. Impact of Dynamic Fractional Order Velocity on the Convergence of the PSO
  3.2. Comparison and Analysis
 4. Conclusions
 References

키워드

저자

• Lingyun Zhou [ State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China, College of Computer Science, South-Central University for Nationalities, Wuhan, Hubei 430074, China ]
• Lixin Ding [ State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China ] Corresponding Author

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