An Improved Cat Swarm Optimization Algorithm Based on Opposition-Based Learning and Cauchy Operator for Clustering
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초록
Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set ofpatterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially inpartitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new metaheuristicalgorithm that has been applied to solve various optimization problems and it provides better resultsin comparison to other similar types of algorithms. However, this algorithm suffers from diversity and localoptima problems. To overcome these problems, we are proposing an improved version of the CSO algorithmby using opposition-based learning and the Cauchy mutation operator. We applied the opposition-basedlearning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator toprevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm wastested with several artificial and real datasets and compared with existing methods like K-means, particleswarm optimization, and CSO. The experimental results show the applicability of our proposed method.
Abstract
Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set ofpatterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially inpartitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new metaheuristicalgorithm that has been applied to solve various optimization problems and it provides better resultsin comparison to other similar types of algorithms. However, this algorithm suffers from diversity and localoptima problems. To overcome these problems, we are proposing an improved version of the CSO algorithmby using opposition-based learning and the Cauchy mutation operator. We applied the opposition-basedlearning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator toprevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm wastested with several artificial and real datasets and compared with existing methods like K-means, particleswarm optimization, and CSO. The experimental results show the applicability of our proposed method.
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