Feature Selection Using Self-Regulating Cat Swarm Optimization Algorithm for Big-Data Classification

In human cognitive psychology, the greatest planners adapt their strategies to their present situation and their appraisal of the best experiences of others. Based on this concept, we proposed an update to the Cat Swarm Optimisation (CSO) model, which we believe will enhance performance and promote convergence. The inspiration method focuses on using a cat's self-perception to guide its search direction based on its current position. The top-performing cat should focus solely on its own position without influence from others, simulating human confidence. Conversely, the remaining other cats are guided by their past successes and influenced by the global best position, emulating human reliance on personal experience. So, in this study, we revised the original CSO and suggested Self-Regulating Cat Swarm Optimisation (SR-CSO) as an enhancement over the CSO algorithm. The performance of SR-CSO is examined by combining the SRCSO with the Random Forest “RF” classifier for feature selection on large data. The findings reveal that it improves classification results for the six benchmark datasets used in the experiment. The experiment results were compared to the original CSO and Improved cat swarm optimization (ICSO), results show that the suggested SRCSO algorithm achieves much quicker convergence and higher accuracy than the basic CSO and ICSO.