Animal migration optimization (AMO) is one of the most recently introduced algorithms based on the behavior of animal swarm\nmigration. This paper presents an improved AMO algorithm (IAMO), which significantly improves the original AMO in solving\ncomplex optimization problems. Clustering is a popular data analysis and data mining technique and it is used in many fields.\nThe well-known method in solving clustering problems is K-means clustering algorithm; however, it highly depends on the initial\nsolution and is easy to fall into local optimum. To improve the defects of the K-means method, this paper used IAMO for the\nclustering problem and experiment on synthetic and real life data sets. The simulation results show that the algorithm has a better\nperformance than that of the K-means, PSO, CPSO, ABC, CABC, and AMO algorithm for solving the clustering problem.
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