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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2011 Vol.1(5): 448-453 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.67

A Gaussian Firefly Algorithm

Sh. M. Farahani, A. A. Abshouri, B. Nasiri, and M. R. Meybodi

Abstract—Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behavior in nature. Each firefly movement is based on absorption of the other one. In this paper to stabilize firefly’s movement, it is proposed a new behavior to direct fireflies movement to global best if there was no any better solution around them. In addition to increase convergence speed it is proposed to use Gaussian distribution to move all fireflies to global best in each iteration. Proposed algorithm was tested on five standard functions that have ever used for testing the static optimization algorithms. Experimental results show better performance and more accuracy than standard Firefly algorithm.

Index Terms—Algorithm, optimization, Global search, Local search.

Sh. M. Farahani, A. A. Abshouri, B. Nasiri and M. R. Meybodi are with the Department of electronic, Computer and IT, Islamic Azad University, Iran, Qazvin. (e-mail: sh.farahani@qiau.ac.ir;a_amin@qiau.ac.ir; Nasiri.babak@qiau.ac.ir; mmeybodi@aut.ac.ir)


Cite: Sh. M. Farahani, A. A. Abshouri, B. Nasiri, and M. R. Meybodi, "A Gaussian Firefly Algorithm," International Journal of Machine Learning and Computing vol. 1, no. 5, pp.448-453, 2011.

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