Earticle

초록

영어

Particle swarm optimization(PSO) algorithm has the advantages of simplicity and easy implementation, but it exits the weaknesses of the being easy to fall into local minimum and premature convergence. In order to overcome these weaknesses of PSO algorithm, the inertia weight and learning factor are improved and the PSO algorithm is initialized by using chaotic optimization in order to propose an improved PSO(WLFCPSO) algorithm. In the proposed WLFCPSO algorithm, chaotic optimization strategy is used to initialize the parameters of PSO algorithm in order to obtain the more reasonable initialization parameters. The adaptive inertia weight adjustment strategy is used to control the adjustment ability of inertia weight in order to keep the diversity of the inertia weight. The dynamic linear adjustment strategy for learning factors is used to gradually reduce the cognitive ability of the individual and improve the global search ability of particles. In order to prove the effectiveness of the proposed WLFCPSO algorithm, several benchmark functions are selected. The experiment results show that the proposed WLFCPSO algorithm has the rapider convergence speed and higher solution precision for solving high-dimension function optimization problems.

목차

Abstract
 1. Introduction
 2. Particle Swarm Optimization
 3. Improvement of Basic PSO Algorithm
  3.1. Adaptive Inertia Weight Adjustment
  3.2. Chaotic Optimization for Initializing Parameters
  3.3. Improvement of Learning Factors:
 4. Principle and Flow of the WLFCPSO Algorithm
 5. Experimental Results and Comparative Analysis
 6. Conclusion
 References

키워드

저자

• Liu Yi [ School of Mechanical and Electronic Engineering, Wuhan Donghu University, Wuhan, 430212, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605