Koffka Khan

A Fuzzy Bat Clustering Method for Ergonomic Screening of Office Workplaces

A method for screening of company workplaces with high ergonomic risk is developed. For clustering of company workplaces a fuzzy modification of bat algorithm is proposed. Using data gathered by a checklist from workplaces, information for ergonomic related health risks is extracted. Three clusters of workplaces with low, moderate and high ergonomic risk are determined. Using these clusters, workplaces with moderate and high ergonomic risk levels are screened and relevant solutions are proposed. By a case study this method is illustrated and validated. Important advantages of the method are reduction of computational effort and fast screening of workplaces with major ergonomic problems within a company.

A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm

Fuzzy 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.