Applying the Coral Reefs Optimization algorithm to clustering problems

Several clustering algorithms have been developed and applied to a great variety of problems in different fields. However, some of these algorithms have limitations. Bio-inspired algorithms have been applied to clustering problems aiming to overcome some of these limitations. In this paper, we apply the Coral Reefs Optimization (CRO) algorithm to clustering problems. The CRO algorithm has been originally proposed for classical optimization problems. In this paper, this algorithm will be adjusted to provide a good clustering partition for a dataset. In addition, we also propose three new modifications of this algorithm and an index to be used as objective function for the optimization techniques. In order to evaluate the effectiveness of the CRO algorithm and the proposed extensions when dealing with real data, we conduct a comparison analysis with another bio-inspired algorithm, a hybrid genetic algorithm proposed for solving clustering problems. In this analysis, two clustering validity measures are employed to measure the generated clusters by the bio-inspired algorithms. We also use two objective functions (TWCV and MX index) in the reproduction process of the analysed algorithms.