Paper Info
Title
Adaptive Hybrid Differential Evolution Algorithm and Its Application in Fuzzy Clustering
Abstract
To improve the globe searching ability of differential evolution algorithm (DE), an adaptive hybrid differential evolution algorithm (AHDE) is proposed. The cross operator of the proposed algorithm is adjusted according to the computation process to enhance the globe convergence ability of the algorithm. Simulated annealing (SA) is adopted for its strong local search ability to overcome the premature convergence of DE. The test results of Several Benchmark functions show that AHDE can avoid premature effectively and its globe convergence ability is better than that of DE. A new fuzzy clustering method combined AHDE with Fuzzy C-Mean algorithm (FCM) is presented and experiment results show that the clustering method presented can avoid the limitation of converging to the local optimal point of FCM and the clustering results obtained are more rational than those from FCM.
Year
DOI
Venue
2009
10.1007/978-3-642-01510-6_75
ISNN (2)
Keywords
Field
DocType
fuzzy clustering,premature convergence,fuzzy c-mean algorithm,adaptive hybrid differential evolution,strong local search ability,globe convergence ability,clustering method,clustering result,new fuzzy clustering method,proposed algorithm,differential evolution algorithm,simulated annealing,local search,differential evolution,adaptive
Simulated annealing,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Mathematical optimization,Premature convergence,Correlation clustering,Computer science,Artificial intelligence,Local search (optimization),Cluster analysis,Machine learning
Conference
Volume
ISSN
Citations 
5552
0302-9743
0
PageRank 
References 
Authors
0.34
2
5
Name
Order
Citations
PageRank
Youlin Lu11119.33
Jianzhong Zhou251155.54
Hui Qin314415.35
Chaoshun Li4427.51
Yinghai Li5425.70