A RADICLE STUDY OF FIRE-FLY ALGORITHM

Main Article Content

Sanjeet Mandal
Prof. Spoorthi Rakesh

Abstract

Firefly algorithm (FA) is a metaheuristic algorithm which was proposed by Dr. Xin-She Yang in 2008 at Cambridge University [1-2], inspired by the flashing behaviour of firefly insects. In this paper, we will see how firefly algorithm is being used in different engineering process applications and also how it is more efficient than others. Firefly algorithm is better than many other methods as it gives optimal solution with minimum time complexity. Our paper show how firefly algorithm is modified and used with other methods to implement and obtain solution for different problems. Firefly algorithm has advantages over other algorithm such as automatical subdivision and the ability of dealing with multimodality. Also the parameters in firefly algorithm can be tuned to control the randomness as iterations proceed, so that convergence can also be sped up by tuning these parameters. These above advantages makes it flexible to deal with continuous problems, clustering and classifications, and combinational as well.

Downloads

Download data is not yet available.

Article Details

Section
Articles

References

X. S. Yang, “Nature-Inspired Meta-Heuristic Algorithmsâ€, Luniver Press, Beckington, UK, 2008. 2. Xin-She Yang, “Firefly Algorithm: Recent Advances and Application Solving the Economic Emissions Load Dispatch problemâ€, Apostolopoulos and Vlachos, 18th August 2013. 3. K. S. Kumar, V. Tamilselvan, N. Murali, R. Rajaram, N. S. Sundaram, and T. Jayabarathi, “Economic load dispatch with emission constraints using various PSO algorithms,†WSEAS Transactions on Power Systems, vol. 3, no. 9, pp. 598–607, 2008. 4. Horng and Jiang, “Multilevel Image Thresholding Selectionâ€, Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing (UIC/ATC), 7th International Conference on 13th Dec 2010. 5. Srivastava, Mallikarjun and Yang, “Finding Optimal Test Sequence Generationâ€, Copyright © 2012 Elsevier, 2013. 6. Ian Sommerville, Software Engineering, eighth edition, Pearson Edition, Boston, 2009. 7. Aditya P. Mathur, Foundation of Software Testing, Pearson education, India, 2007. 8. Kumbharana and Pandey, “Solving Travelling Salesman Problemâ€, International Journal for Research in Science & Advanced Technologies Issue-2, Volume-2, 053-057, 2013. 9. David Bookstaber, “Simulated Annealing for Traveling Salesman Problemâ€, Springer, 1997. 10. Horng, “Vector Quantization for Image Compressionâ€, 2005 Elsevier, https://doi.org/10.1007/978-3-540-40046-2_7,Spri nger, Berlin, Heidelberg, 978-3-642-05820-2, 2012. 11. R.M. Gray, Vector quantization, IEEE Signal Process. Mag. 1 (2) (1984) 4–29. 12. D. Ailing, C. Guo, An adaptive vector quantization approach for image segmentation based on SOM network, Neurocomputing 149 (2015) 48–58. 13. Makespan K.C.Udaiyakumara*, M.Chandrasekaran b. “Application of Firefly Algorithm in Job Shop Scheduling Problem for Minimizationâ€, Elsevier, Volume 97, 2014, Pages 1798-1807 14. ABDESSLEM LAYEB, ZEYNEB BENAYAD, “A Novel Firefly algorithm Based Ant Colony Optimization for Solving Combinatorial Optimization Problemsâ€, International Journal of Computer Science and Applications, Technomathematics Research Foundation.

Most read articles by the same author(s)