Multi-objective clustering analysis via combinatorial pigeon inspired optimization

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Multi-objective data clustering is an important issue in data mining, and the realization of data clustering using the multi-objective optimization technique is a significant topic. A combinatorial multi-objective pigeon inspired optimization (CMOPIO) with ring topology is proposed to solve the clustering problem in this paper. In the CMOPIO, a delta-locus based coding approach is employed to encode the pigeons. Thus, the length of pigeon representation and the dimension of the search space are significantly reduced. Thereby, the computational load can be effectively depressed. In this way, the pigeon inspired optimization (PIO) algorithm can be discretized with an auxiliary vector to address data clustering. Moreover, an index-based ring topology with the ability of contributing to maintain flock diversity is adopted to improve the CMOPIO performance. Comparative simulation results demonstrate the feasibility and effectiveness of our proposed CMOPIO for solving data clustering problems.