Fireworks Algorithm for Optimization

  • Ying Tan
  • Yuanchun Zhu
Conference paper

DOI: 10.1007/978-3-642-13495-1_44

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)
Cite this paper as:
Tan Y., Zhu Y. (2010) Fireworks Algorithm for Optimization. In: Tan Y., Shi Y., Tan K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg

Abstract

Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed for global optimization of complex functions. In the proposed FA, two types of explosion (search) processes are employed, and the mechanisms for keeping diversity of sparks are also well designed. In order to demonstrate the validation of the FA, a number of experiments were conducted on nine benchmark test functions to compare the FA with two variants of particle swarm optimization (PSO) algorithms, namely Standard PSO and Clonal PSO. It turns out from the results that the proposed FA clearly outperforms the two variants of the PSOs in both convergence speed and global solution accuracy.

Keywords

natural computing swarm intelligence fireworks algorithm particle swarm optimization function optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ying Tan
    • 1
  • Yuanchun Zhu
    • 1
  1. 1.Key Laboratory of Machine Perception (MOE), Peking University Department of Machine Intelligence, School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

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