Affinity Propagation Based Improved Group Search Optimizer Clustering Algorithm

Authors

Zhang Kang, Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education East China University of Science and Technology, Shanghai 200237, China;
Xingsheng Gu, Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education East China University of Science and Technology, Shanghai 200237, China;

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.

Recommended Citation

Kang, Zhang and Gu, Xingsheng (2020) "Affinity Propagation Based Improved Group Search Optimizer Clustering Algorithm," Journal of System Simulation: Vol. 27: Iss. 9, Article 19.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss9/19

First Page

2066

Revised Date

2015-08-03

DOI Link

https://doi.org/

Last Page

2074

CLC

TP18

Recommended Citation

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