Abstract
Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.
Keywords
- Genetic Algorithm
- Particle Swarm Optimization
- Global Optimum
- Particle Swarm Optimization Algorithm
- Swarm Intelligence
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)
Deb, K.: Optimisation for Engineering Design. Prentice-Hall, New Delhi (1995)
Gazi, K., Passino, K.M.: Stability analysis of social foraging swarms. IEEE Trans. Sys. Man. Cyber. Part B - Cybernetics 34, 539–557 (2004)
Goldberg, D.E.: Genetic Algorithms in Search, Optimisation and Machine Learning. Addison Wesley, Reading (1989)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R., Shi, Y.: Swarm intelligence. Academic Press, London (2001)
Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization. University Press, Princeton (2001)
Shilane, D., Martikainen, J., Dudoit, S., Ovaska, S.J.: A general framework for statistical performance comparison of evolutionary computation algorithms. Information Sciences: An Int. Journal 178, 2870–2879 (2008)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)
Yang, X.S.: Biology-derived algorithms in engineering optimization. In: Olarius, S., Zomaya, A. (eds.) Handbook of Bioinspired Algorithms and Applications, ch. 32. Chapman & Hall / CRC (2005)
Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Chichester (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, XS. (2009). Firefly Algorithms for Multimodal Optimization. In: Watanabe, O., Zeugmann, T. (eds) Stochastic Algorithms: Foundations and Applications. SAGA 2009. Lecture Notes in Computer Science, vol 5792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04944-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-04944-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04943-9
Online ISBN: 978-3-642-04944-6
eBook Packages: Computer ScienceComputer Science (R0)