An Efficient Hybrid Artificial Immune Algorithm for Clustering

This paper presents a hybrid efficient method namely hybrid immune algorithm (HIA) based on artificial immune algorithm (AIA) and bacterial optimization for clustering problems. Four local searches on the basis of heuristic rules for the given clustering problem are designed and applied. This proposed method is implemented and tested on two real datasets. Further, its performance is compared with other well-known meta-heuristics, such as ACO, GA, simulated annealing (SA), and tabu search (TS). At last, paired comparison t-test is also applied to proof the efficiency of our proposed method. The associated outputs give very encouraging results.