Earticle

초록

영어

In order to overcome the drawbacks of standard shuffled frog leaping algorithm that converges slowly at the last stage and easily falls into local minima, this paper proposed two-phases learning shuffled frog leaping algorithm. The modified algorithm added the elite Gaussian learning strategy in the global information exchange phase, updated frog leaping rule and added the learning capability that the worst frog of current swarm learned from the best frog of other swarm. The learning capability of two-stage on the one hand increased the search range, on the other hand enhanced the diversity of population. Experiments were conducted on 13 classical benchmark functions, the simulation results demonstrated that the proposed approach improved the convergence rate and solution accuracy, when compared with common swarm intelligence algorithm and the latest improved shuffled frog leaping algorithm.

목차

Abstract
 1. Introduction
 2. Shuffled Frog Leaping Algorithm (SFLA)
  2.1. Memeplex Division
  2.2. Local Search
  2.3. Global Information Exchange
 3. Two-Phases Learning Shuffled Frog Leaping Algorithm (TLSFLA)
  3.1. Local Search
  3.2. Global Information Exchange
  3.3. Algorithm Flow
 4. Experimental Verifications
  4.1. Test Functions
  4.2. Comparison of TLSFLA with Other Standard Intelligent Algorithms
  4.3. Comparison of TLSFLA with Improved SFLA Variants
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Jia Zhao [ School of Information Engineering, Nanchang Institute of Technology, Nan Chang 330099, China ]
• Li Lv [ School of Information Engineering, Nanchang Institute of Technology, Nan Chang 330099, 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