Earticle

초록

영어

Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria, but it is difficult to optimize to get a high precision due to the randomness of the bacterial behavior, which belongs to intelligence algorithm. This paper presents an extended BFO algorithm, namely the Cooperative Bacterial Foraging Optimization (CBFO), which significantly improves the original BFO in solving clustering problems. A novel clustering method based on the CBFO could be used for solving clustering problems. In this work, firstly, The efficiency and performance of the CBFO algorithm was evaluated using six widely-used benchmark functions, coming up with comparative results produced by BFO, then Particle Swarm Optimization (PSO) is studied. Secondly, the algorithm with CBFO algorithms is used for data clustering on several benchmark data sets. The performance of the algorithm based on CBFO is compared with BFO algorithms on clustering problem. The simulation results show that the proposed CBFO outperforms the other three algorithms in terms of accuracy, robustness and convergence speed.

목차

Abstract
 1. Introduction
 2. Standard BFO Algorithm
  2.1. Bacterial Chemotactic Behavior
  2.2. The Original BFO Algorithm Steps
 3. The Cooperative Bacterial Foraging Optimization (CBFO) Algorithm
 4. Experimental Result
  4.1 Benchmark Functions
  4.2 Results for the 20-D Problems
 5. Second a Data Clustering Experimental Results
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhao Hongwei [ School of information science and engineering, Northeastern University, School of Information Engineering, Shenyang University ]
• Tian Liwei [ School of Information Engineering, Shenyang University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605