Affinity Propagation Based Improved Group Search Optimizer Clustering Algorithm

Abstract

Abstract: The essence of clustering is an optimization problem. It can be solved by swarm intelligent algorithms which are the popular research area in recent years. A novel Group Search Optimizer (GSO) algorithm named Fast Global Group Search Optimizer (FGGSO) was proposed. FGGSO improved the individuals' updating strategies of GSO, adopting the campaign strategy, destruction-construction strategy and accelerating-jumping strategy. By this means, the proposed algorithm improved the global and local search capability of the original GSO. Furthermore, based on this FGGSO algorithm, a novel improved AP algorithm was proposed. On account of deficiency of AP clustering unable to deal with a user given cluster number, FGGSO and AP were combined. Firstly, AP algorithm was used to obtain any candidate exemplars, and then the clustering result was optimized using FGGSO algorithm, so that a certain cluster number can be obtained. Experimental results show the effectiveness of the proposed algorithm.

Key words: clustering analysis, affinity propagation, group search optimizer, optimization

CLC Number:

TP18

Zhang Kang, Gu Xingsheng. Affinity Propagation Based Improved Group Search Optimizer Clustering Algorithm[J]. Journal of System Simulation, 2015, 27(9): 2066-2074.

References

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