A Hybrid Bumble Bees Mating Optimization - GRASP Algorithm for Clustering

Authors:
Yannis Marinakis;Magdalene Marinaki;Nikolaos Matsatsinis
Affiliations:
Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete, Chania, Greece 73100;Industrial Systems Control Laboratory, Department of Production Engineering and Management, Technical University of Crete, Chania, Greece 73100;Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete, Chania, Greece 73100
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

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Abstract

A new hybrid algorithm for clustering, which is based on the concepts of the Bumble Bees Mating Optimization (BBMO) and Greedy Randomized Adaptive Search Procedure (GRASP), is presented in this paper. The proposed algorithm is a two phase algorithm which combines a new algorithm called Bumble Bees Mating Optimization algorithm for the solution of the feature selection problem and a GRASP algorithm for the solution of the clustering problem. The performance of the algorithm is compared with other popular metaheuristic and nature inspired methods using datasets from the UCI Machine Learning Repository. The high performance of the proposed algorithm is achieved as the algorithm gives very good results and in some instances the percentage of the correct clustered samples is very high and is larger than 98%.