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A hybrid clustering algorithm based on multi-swarm constriction PSO and GRASP

Matsatsinis Nikolaos, Marinakis Ioannis, Marinaki Magdalini

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This paper presents a new hybrid algorithm, which is based on the concepts of Particle Swarm Optimization (PSO) and Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm is a two phase algorithm which combines a Multi-Swarm Constriction Particle Swarm Optimization algorithm for the solution of the feature selection problem and a GRASP algorithm for the solution of the clustering problem. In this paper in PSO, multiple swarms are used in order to give to the algorithm more exploration and exploitation abilities as the different swarms have the possibility to explore different parts of the solution space and, also, a constriction factor is used for controlling the behaviour of particles in each swarm. The performance of the algorithm is compared with other popular metaheuristic methods like classic genetic algorithms, tabu search, GRASP, ant colony optimization and particle swarm optimization. In order to assess the efficacy of the proposed algorithm, this methodology is evaluated on 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 corrected clustered samples is very high and is larger than 98%.

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