Abstract
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that
depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative
computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering
process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some
contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic
the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time.
It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as
pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism
for the sake of enhancing its clustering quality by avoiding local optima and finding global optima,