원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.5
2016.05
pp.131-144
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보