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Development of the Meta-Heuristic of PSOGA with K-means Algorithm


Y. Farhang


Vol. 17  No. 6  pp. 29-35


In this paper, a meta-heuristic approach was proposed for the hybridization of the K-means algorithm scheme. It obtained better results by developing a hybrid Genetic Algorithm-K-means (GA-K-means) and a hybrid Partial Swarm Optimization-K-means (PSO-K-means) method. In recent years, combinational optimization issues are introduced as critical problems in clustering algorithms to partition data in a way that optimizes the performance of clustering. K-means algorithm is one of the famous and more popular clustering algorithms which can be simply implemented and it can easily solve the optimization issue with less extra information. But the problems associated with K-means algorithm are high error rate, high intra cluster distance and low accuracy. In this regard, researchers have worked to improve the problem computationally, creating efficient solutions that lead to better data analysis through the K-means clustering algorithm. The aim of this study is to improve the accuracy of the K-means algorithm using hybrid and meta-heuristic methods. Finally, the meta-heuristic of Genetic Algorithm-Partial Swarm Optimization (GAPSO) and Partial Swarm Optimization-Genetic Algorithm (PSOGA) through the K-means algorithm were proposed. The approach adopted in this study successfully increased the accuracy rate of the clustering analysis and decreased its error rate and intra-cluster distance.


Hybrid Genetic Algorithm, K-means algorithm, Genetic Algorithm-Partial Swarm Optimization