Abstract
Cat swarm optimization (CSO) is a novel meta-heuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two sub-modes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known meta-heuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection of support vector machines (SVMs). Experimental results demonstrate that the proposed MCSO algorithm provides better results in less time than basic CSO algorithms.
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Lin, KC., Huang, YH., Hung, J.C., Lin, YT. (2014). Modified Cat Swarm Optimization Algorithm for Feature Selection of Support Vector Machines. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_40
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DOI: https://doi.org/10.1007/978-94-017-8798-7_40
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