Earticle

초록

영어

This paper built a optimization model and proposed an improved firefly optimization algorithm called CFA, which is based on firefly Cooperative. The main idea of CFA is to extend the single population FA to the interacting multi-swarms by cooperative Models. In this work, firstly, CFA algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by, firefly optimization algorithm(FA) are studied. Secondly, CFA algorithm used in data mining, clustering analysis on several typical data sets. The performance of typical data clustering results showed that the biological heuristic algorithm based on clustering analysis algorithm with the existing success of FA compared to faster convergence, and the clustering of higher quality.

목차

Abstract
 1. Introduction
 2. Standard AF algorithm
  2.1. Basic Firefly Algorithm
  2.2.The FA Algorithm Steps
 3. The Cooperative Firefly Algorithm(CFA)
 4. Experimental Result
  4.1 Benchmark Functions
  4.2 Results for the 10-D Problems
  4.3 Results for the 20-D Problems
 5. Data Clustering Experimental Results
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhao Hongwei [ School of Information Engineering, Shenyang University ]
• Tian Liwei [ School of Information Engineering, Shenyang University ]
• Wang Dongzheng [ Electronic Information and Engineering College, Dalian University of Technology ]

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