Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9273
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dc.contributor.authorKumar, Yugal-
dc.contributor.authorSingh, Pradeep Kumar-
dc.date.accessioned2023-01-17T11:04:59Z-
dc.date.available2023-01-17T11:04:59Z-
dc.date.issued2017-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9273-
dc.description.abstractThis paper presents a cat swarm optimization (CSO) algorithm for solving global optimization problems. In CSO algorithm, some modifications are incorporated to improve its performance and balance between global and local search. In tracing mode of the CSO algorithm, a new search equation is proposed to guide the search toward a global optimal solution. A local search method is incorporated to improve the quality of solution and overcome the local optima problem. The proposed algorithm is named as Improved CSO (ICSO) and the performance of the ICSO algorithm is tested on twelve benchmark test functions. These test functions are widely used to evaluate the performance of new optimization algorithms. The experimental results confirm that the proposed algorithm gives better results than the other algorithms. In addition, the proposed ICSO algorithm is also applied for solving the clustering problems. The performance of the ICSO algorithm is evaluated on five datasets taken from the UCI repository. The simulation results show that ICSO-based clustering algorithm gives better performance than other existing clustering algorithms.en_US
dc.language.isoenen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectCat swarm optimizationen_US
dc.subjectClusteringen_US
dc.subjectNumerical functionsen_US
dc.subjectMeta-heuristicsen_US
dc.titleImproved cat swarm optimization algorithm for solving global optimization problems and its application to clusteringen_US
dc.typeArticleen_US
Appears in Collections:Journal Articles



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