Earticle

초록

영어

In view of the problems of easily relapsing into local extremum and low convergence accuracy of fruit fly optimization algorithm (FOA), this paper proposes a adaptive fruit fly optimization algorithm based on velocity variable (VFOA). The idea of this algorithm is based on the flight characteristics of fruit fly, using particle swarm optimization (PSO) concept of particle velocity, based on fruit fly optimization algorithm, improved the convergence speed of fruit fly optimization algorithm by adding the particle velocity variable parameter. Finally, simulation comparison experiment tests are conducted on 13 benchmark functions, test results show that adaptive fruit fly optimization algorithm based on velocity variable VFOA compared to swarm intelligence algorithms of FOA, PSO, CS, and so on, the convergence speed and accuracy are improved obviously.

목차

Abstract
 1. Introduction
 2. Fruit Fly Optimization Algorithm
 3. Adaptive Fruit Fly Optimization Algorithm Based on Velocity Variable
 4. Experimental Results and Analysis
  4.1. Experimental Set Up
  4.2. Experiment Parameters Setting
  4.3. Algorithm Comparison
  4.4. The Higher Dimensional Function Test
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Mindi Lu [ School of Computer and Electronis Information, Guangxi University, Nanning, 530004, China ]
• Yongquan Zhou [ College of Information Science and Engineerring, Guangxi University for Nationnalities, Nanning, 530004, China ]
• Qifang Luo [ College of Information Science and Engineerring, Guangxi University for Nationnalities, Nanning, 530004, China ]
• Kang Huang [ School of Computer and Electronis Information, Guangxi University, Nanning, 530004, 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