|Table of Contents|

An Artificial Bee Colony Clustering Algorithm Based onthe Location Update Technology(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2015年04期
Page:
95-
Research Field:
数学
Publishing date:

Info

Title:
An Artificial Bee Colony Clustering Algorithm Based onthe Location Update Technology
Author(s):
Wang Jialing; Hu Benmu; Sun Yuehong
School of Mathematical Sciences，Nanjing Normal University，Nanjing 210023，China
Keywords:
location update technology; artificial bee colony algorithm; clustering analysis; exploitation ability
PACS:
TP391
DOI:
-
Abstract:
In this paper，an artificial bee colony（ABC）algorithm based on location update technology is proposed and applied to the problems of clustering analysis. The technology makes the algorithm fully use the information of current optimal solution and the worst solution to do further location update of current optimal solution after the search of onlookers. Experiments show that the ABC algorithm based on location update technology enhances the exploitation ability of applying the previous solutions to look for better solutions. The proposed algorithm also has better clustering performance compared with K-means algorithm，clustering algorithms based on particle swarm optimization and artificial bee colony.

References:

［1］KRISHMA K，MURTY M N. Genetic K-means algorithm［J］. IEEE transactions on systems，man，and cybernetics. 1999，29（3）：433-439.
［2］MAULIK U，BANDYOPADHAY S. Genetic algorithm-based clustering technique［J］. Pattern recognition，2000，33（9）：1455-1465.
［3］DENEUBOURG J L，GOSS S，FRANKS N. The?dynamics?of?collective?sorting：robot-like?ants?and?ant-like?robots［C］//Proceedings of the First International Conference on Simulation of Adaptive haviour，From Animals to Animals?J，MIT?Press，Cambridge?MA，1991：356-365.
［4］LUMER E，FAIETA B. Diversity and adaptation in populations of clustering ants［C］//Proceedings of the Third International Conference on Simulation of Adaptive Behavior：From Animals to Animats，Vol. 3，MIT Press/Bradford Books，Cambridge，MA，1994：501-508.
［5］OMRAN M，Salman A，Engelbrecht A P. Image Classification Using Particle Swarm Optimization［C］//Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning，Singapore，2002.
［6］KARABOGA D. An idea based on honey bee swarm for numerical optimization［M］. Erciyes University，Engineering Faculty Computer Engineering Department，2005.
［7］ZHANG C，OUYANG D，NING J. An artificial bee colony approach for clustering［J］. Expert Syst Appl，2010，37（7）：4761-4767.
［8］KARABOGA D，OZTURK C. Fuzzy clustering with artificial bee colony algorithm［J］. Sci Res Essay，2010，5（14）：1899-1902.
［9］KARABOGA D，OZTURK C. A novel clustering approach：Artificial Bee Colony algorithm［J］. Applied soft computing，2011，11（1）：652-657.
［10］TAN Q H，WU H J，HU B，et al. An Improved Artificial Bee Colony Algorithm for Clustering［C］//Proceedings of the 2014 conference companion on genetic and evolutionary computation companion，2014：19-20.
［11］OZTURK C，HANCER E，KARABOGA D. Improved clustering criterion for image clust-ering with artificial bee colony algorithm［J］. Pattern Anal Appl. 2014. http：//dx.doi.org/10.1007/s10044-014-0365-y（in press）.
［12］CELAL OZTURK，EMRAH HANCER，DERVIS KARABOGA. Dynamic clustering with improved binary artificial bee colony algorithm［J］. Applied soft computing，2015（28）：69-80.
［13］KAO Y T，ZAHARA E，KAO I W. A hybridized approach to data clustering［J］. Expert systems with applications，2008，34（3）：1754-1762.

Memo

Memo:
-
Last Update: 2015-12-30