Solving multiobjective clustering using an immune-inspired algorithm

Maoguo Gong, Lining Zhang, Licheng Jiao, Shuiping Gou

Abstract: In this study, we introduced a novel multiobjective optimization algorithm, Nondominated Neighbor Immune Algorithm (NNIA), to solve the multiobjective clustering problems. NNIA solves multiobjective optimization problems by using a nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators and elitism. The main novelty of NNIA is that the selection technique only selects minority isolated nondominated individuals in current population to clone proportionally to the crowding-distance values, recombine and mutate. As a result, NNIA pays more attention to the less-crowded regions in the current trade-off front. The experimental results on seven artificial data sets with different manifold structure and six real-world data sets show that the NNIA is an effective algorithm for solving multiobjective clustering problems, and the NNIA based multiobjective clustering technique is a cogent unsupervised learning method.