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Cat swarm optimization (CSO) is a novel metaheuristic for evolutionary optimization algorithms based on swarm intelligence which proposed in 2006. See Feature Selection of Support Vector Machine Based on Harmonious Cat Swarm Optimization.

According to Modified Cat Swarm Optimization Algorithm for Feature Selection of Support Vector Machines

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).

Can someone explain how exactly Cat Swarm Algorithm (CSO) is used for feature selection?

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  • $\begingroup$ Please, next time you copy and paste something from an article, paper, book, etc., please, use > to explicitly indicate that you are quoting. Note that if you just copy and paste content without quoting your post could be deleted because that's considered plagiarism. See ai.stackexchange.com/help/on-topic for more details. $\endgroup$ – nbro May 20 at 13:13
  • $\begingroup$ @nbro Noted with Thanks. $\endgroup$ – Pluviophile May 20 at 13:15

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