With an ever-growing attention Particle Swarm Optimization (PSO) has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. For this purpose we use simultaneous perturbation stochastic approximation (SPSA), which is applied only to thegbest (not to the entire swarm) for a low-cost solution. Since the problem of poorgbest update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), two distinct SA approaches are then integrated into MD-PSO and tested over a set of unsupervised data clustering applications. Experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems.

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Authors and Affiliations

1. Tampere University of Technology, Tampere, Finland

   Serkan Kiranyaz & Moncef Gabbouj

2. Izmir University of Economics, Izmir, Turkey

   Turker Ince

Editors and Affiliations

1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

   Cecilia Di Chio

2. Department of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italy

   Stefano Cagnoni

3. Departmento Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain

   Carlos Cotta

4. Wilhelm Schickard-Institut für Informatik, Sand 1, Universität Tübingen, 72076, Tübingen, Germany

   Marc Ebner

5. Computer Science, Aston Triangle, Aston University, B4 7ET, Birmingham, UK

   Anikó Ekárt

6. Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Edif. 8G Acceso B, 46022, Valencia, Spain

   Anna I. Esparcia-Alcazar

7. Advanced Technology Centre Rolls-Royce, Singapore, Singapore

   Chi-Keong Goh

8. Departamento de Electrónica y Tnologia de los Computadores, University of Granada, 18071, Grenada, Spain

   Juan J. Merelo

9. Department of Mathematical Information Technology,, University of Jyväskylä, 4th Floor, P.O. Box 25, 40014, Agora, Finland

   Ferrante Neri

10. Lehrstuhl für Algorithm Engineering, TU Dortmund, Otto-Hahn-Straße 14, 44227, Dortmund, Germany

    Mike Preuß

11. Center for Computer Games Research, IT University of Copenhagen, Rued Langgaards Vej 7, 2300, Copenhagen S, Denmark

    Julian Togelius  & Georgios N. Yannakakis  & 

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