01626nas a2200229   4500000000100000000000100001008004100002260001200043653001700055653001000072653001500082653001300097653001800110100001600128700001600144245007200160856009800232300001000330490000600340520103600346022001401382       2012                            d  c12/201210aOptimization10aFuzzy10aClustering10aBi-sonar10aMetaheuristic1 aKoffka Khan1 aAshok Sahai00aA fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm  uhttp://www.ijimai.org/journal/sites/default/files/files/2012/11/ijimai20121_7_3_pdf_25064.pdf  a26-320 v13 aFuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results. 
 
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