Earticle

초록

영어

This paper presents an improved clustering algorithm with Ant Colony optimization (ACO) based on dynamical pheromones. Pheromone is an important factor for the performance of ACO algorithms. Two strategies based on adaptive pheromones which improved performance are introduced in this paper. One is to adjust the rate of pheromone evaporation dynamically, named as P , and the other is to adjust the strength of pheromone dynamically, named as Q . Two evaluation indices, Precision and Recall, are chosen to validity the improvement strategies. Numerical simulations demonstrate that the two strategies on pheromone can achieve better performance than basic ant colony algorithm and clustering algorithm with ant colony based on best solution kept.

목차

Abstract
 1. Introduction
 2. Clustering Algorithm with ACO based on Dynamic Pheromone
  2.1. Ant Colony System
  2.2. Clustering algorithm with ACO
  2.3. The strategy of dynamical pheromones
  2.4. Clustering algorithm with ant colony optimization based dynamical pheromones
 3. Simulation Experiment
  3.1. Datasets and parameters setting
  3.2. Comparison of results
  3.3. Discussion
 4. Conclusion and Future Work
 Acknowledgements
 References

키워드

저자

• Xiaoyong Liu [ Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, 510665, 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