An Improved Cat Swarm Optimization Algorithm Based on Opposition-Based Learning and Cauchy Operator for Clustering

Yugal Kumar and G. Sahoo
Volume: 13, No: 4, Page: 1000 ~ 1013, Year: 2017
10.3745/JIPS.02.0022
Keywords: Cat Swarm Optimization, Cauchy Mutation Operator, Clustering, Opposition-Based Learning, Particle Swarm Optimization
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Abstract
Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta- heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

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Cite this article
IEEE Style
Yugal Kumar and G. Sahoo, "An Improved Cat Swarm Optimization Algorithm Based on Opposition-Based Learning and Cauchy Operator for Clustering," Journal of Information Processing Systems, vol. 13, no. 4, pp. 1000~1013, 2017. DOI: 10.3745/JIPS.02.0022.

ACM Style
Yugal Kumar and G. Sahoo, "An Improved Cat Swarm Optimization Algorithm Based on Opposition-Based Learning and Cauchy Operator for Clustering," Journal of Information Processing Systems, 13, 4, (2017), 1000~1013. DOI: 10.3745/JIPS.02.0022.