Application of honey-bee mating optimization algorithm on clustering.

*(English)*Zbl 1117.92059Summary: Cluster analysis is one of attractive data mining techniques used in many fields. One popular class of data clustering algorithms is the center based clustering algorithm K-means used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot be found with reasonable amount of computational effort. In order to overcome local optima problem lots of studies done in clustering.

Over the last decade, modeling the behavior of social insects, such as ants and bees, for the purpose of search and problem solving has been the context of the emerging area of swarm intelligence. Honey-bees are among the most closely studied social insects. Honey-bee mating may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of marriage in real honey-bees. Honey-bee has been used to model agent-based systems. We proposed application of honeybee mating optimization in clustering (HBMK-means). We compared HBMK-means with other heuristic algorithms in clustering, such as GA, SA, TS, and ACO, by implementing them on several well-known data sets. Our finding shows that the proposed algorithm works as the best one.

Over the last decade, modeling the behavior of social insects, such as ants and bees, for the purpose of search and problem solving has been the context of the emerging area of swarm intelligence. Honey-bees are among the most closely studied social insects. Honey-bee mating may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of marriage in real honey-bees. Honey-bee has been used to model agent-based systems. We proposed application of honeybee mating optimization in clustering (HBMK-means). We compared HBMK-means with other heuristic algorithms in clustering, such as GA, SA, TS, and ACO, by implementing them on several well-known data sets. Our finding shows that the proposed algorithm works as the best one.

##### MSC:

92D50 | Animal behavior |

90C59 | Approximation methods and heuristics in mathematical programming |

91C20 | Clustering in the social and behavioral sciences |

##### Software:

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\textit{M. Fathian} et al., Appl. Math. Comput. 190, No. 2, 1502--1513 (2007; Zbl 1117.92059)

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##### References:

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