Shu-Chuan Chu's Inspiration from Feline Daily Behaviors Leads to Cat Swarm Optimization Algorithm

In 2006, Shu-Chuan Chu introduced the Cat Swarm Optimization (CSO) algorithm, inspired by the daily behavioral patterns of domestic cats. The algorithm's most distinctive characteristic is its ability to perform simultaneous local and global searches during the optimization process, resulting in exceptional convergence speed achieved through a carefully balanced exploration-exploitation mechanism. Furthermore, CSO represents a nature-inspired heuristic optimization algorithm based on feline behavioral phenomena. Cats demonstrate unique hunting and survival strategies in natural environments. Through observing feline behaviors, Shu-Chuan Chu identified specific patterns where individual cats maintain group coordination while acting independently. This behavioral pattern, termed "cat hormone behavior," forms the algorithmic foundation where each solution candidate (cat) follows position-updating rules mimicking real feline movements. In the CSO implementation, each solution candidate is treated as an individual cat. These candidates explore the search space while enhancing search effectiveness through communication and information sharing mechanisms coded via fitness-value comparisons. The algorithm employs two distinct modes: seeking mode (local search) and tracing mode (global search), with cats switching between these modes based on a predefined mixture ratio parameter. This dual-mode approach enables better adaptation to diverse search spaces, significantly improving the algorithm's efficiency and solution accuracy through strategic population division and mode-specific velocity update equations. In summary, Cat Swarm Optimization presents a highly promising optimization technique rooted in unique natural phenomena. The algorithm has demonstrated successful applications across various domains including engineering design, pattern recognition, and data clustering, with implementations typically involving position initialization, fitness evaluation, mode selection, and velocity-position updates in iterative cycles.