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This paper presents a powerful swarm intelligence meta-heuristic optimizationalgorithm called Dynamic Cat Swarm Optimization. The formulation is throughmodifying the existing Cat Swarm Optimization. The original Cat SwarmOptimization suffers from the shortcoming of 'premature convergence', which isthe possibility of entrapment in local optima which usually happens due to theoff-balance between exploration and exploitation phases. Therefore, theproposed algorithm suggests a new method to provide a proper balance betweenthese phases by modifying the selection scheme and the seeking mode of thealgorithm. To evaluate the performance of the proposed algorithm, 23 classicaltest functions, 10 modern test functions (CEC 2019) and a real world scenarioare used. In addition, the Dimension-wise diversity metric is used to measurethe percentage of the exploration and exploitation phases. The optimizationresults show the effectiveness of the proposed algorithm, which ranks firstcompared to several well-known algorithms available in the literature.Furthermore, statistical methods and graphs are also used to further confirmthe outperformance of the algorithm. Finally, the conclusion as well as futuredirections to further improve the algorithm are discussed.

Dynamic Cat Swarm Optimization Algorithm for Backboard Wiring Problem