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Dynamic multi-swarm PSO based on К-means clustering

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Category: Information technologies, systems analysis and administration

Last Updated on 08 February 2016

Published on 08 February 2016

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Authors:

Xiaojun Yu, Jiangsu University of Technology, Changzhou, Jiangsu, China

Abstract:

Purpose. Objective optimization is a very important area in scientific research and practical applications, many problems are related to the objective optimization. The research investigates combinational measures of Particle Swarm Optimization (PSO) and K-means clustering. The dynamic multi-swarm particle swarm optimization based on K-means clustering (KDMPSO) has been obtained, which is a hybrid clustering algorithm integrating PSO and K-means clustering, and it can nicely find global extreme in different problems.

Methodology. The comprehensive and in-depth analysis on PSO and K-means clustering was carried out, and improvement strategies have been found by adopting combinational measures of PSO and K-means clustering. For both continuous and discrete optimization problems, it has strong global search capacity; it effectively reduces the premature convergence of the traditional PSO.

Findings. Combination of the advantages of PSO and K-means clustering solves convergence to local optimum and inefficiency of traditional PSO algorithm in complex optimization problems, ensures that PSO is stable and can maintain the population diversity, avoids prematurity, and enhances the algorithm accuracy.

Originality. The multi-swarm PSO and K-means clustering were studied. In the iteration process, PSO is easy to get trapped in local optimal solution, causing the phenomenon of premature convergence, on the other hand, K-means is extensively used in clustering since it is easy to realize and it is also a highly-efficient algorithm with linear time complexity. For the first time the complementary combinational method of PSO and K-means clustering was considered.

Practical value. Since the optimization measures is widely used in business management, market analysis, engineering design and scientific exploration and other fields, the research results can be applied in in various fields. KDMPSO can effectively make up for the deficiency of the traditional PSO, and have achieved good results.

References:

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2. Satish Gajawada and Durga Toshniwal (2012), “Projected Clustering Using Particle Swarm Optimization”, Procedia Technology, vol.35, no. 4, pp. 360−364.

3. Sung-Kwun Oh, Wook-Dong Kim, Witold Pedrycz and Su-Chong Joo (2012), “Design of K-means Clustering-based Polynomial Radial Basis Function Neural Networks (PRBF NNS) realized with the aid of Particle Swarm Optimization and Differential Evolution”, Neurocomputing, vol. 78, no.1, pp. 121−132.

4. Shafiq Alam, Gillian Dobbie, Yun Sing Koh, Patricia Riddle and Saeed Ur Rehman (2014), “Research on Particle Swarm Optimization based clustering: a systematic review of literature and techniques”, Swarm and Evolutionary Computation, vol.17, no.8, pp. 1−13.

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6. Lige S. Hage and Ramsey F. Hamade (2013), “Segmentation of histology slides of cortical bone using Pulse Coupled Neural Networks optimized by Particle Swarm Optimization”, Computerized Medical Imaging and Graphics, vol.37, no.7-8, pp. 466−474.

7. Maryam Nasiri (2012), “Fetal electrocardiogram signal extraction by ANFIS trained with PSO Method”, International Journal of Electrical and Computer Engineering (IJECE), vol.2, no. 2, pp. 247−260.

8. Lakshmi Ravi (2012), “PSO based optimal power flow with hybrid distributed generators and UPFC”, TELKOMNIKA Indonesian Journal of Electrical Engineering, vol.10, no. 3, pp. 409−418.

9. Yau King Lam and Peter W.M. Tsang (2012), “Exploratory K-Means: a new simple and efficient algorithm for gene clustering”, Applied Soft Computing, vol.12, no.3, pp. 1149−1157.

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