Cat Swarm Optimization Algorithm

In the cat swarm optimization algorithm, each cat represents a feasible solution candidate for the optimization problem. The algorithm divides feline behavior into two primary modes. The first is the seeking mode, activated when cats are in a resting state and scanning their environment. During this mode, cats explore new potential solutions within the search space by randomly perturbing their positions and evaluating fitness functions - typically implemented through position update equations with random perturbation factors and selection probabilities based on fitness comparisons. The second mode is the tracking mode, engaged when cats follow dynamic targets. In this phase, cats adapt their movements to pursue optimal solutions by updating velocities and positions using equations that incorporate target positions, historical best solutions, and acceleration coefficients. The balance between seeking and tracking modes - controlled by a mode selection ratio parameter - is critical to the algorithm's success. Through this algorithm, various optimization problems can be effectively solved, including engineering design optimization and economic resource allocation, with implementations typically featuring main functions for population initialization, fitness evaluation, mode switching, and solution updating over multiple iterations.