Cat Swarm Optimization Algorithm

In the cat swarm optimization algorithm, each cat represents a feasible solution to the optimization problem. The algorithm categorizes cat behavior into two distinct modes: the seeking mode (when cats are idle and observing their surroundings) and the tracking mode (when cats pursue dynamic targets). The algorithm implementation typically involves initializing a population of cats with random positions and velocities, then iteratively updating their states based on mode-specific operations including position updates, velocity adjustments, and fitness evaluations.