FIREWORKS ALGORITHM FOR UNCONSTRAINED FUNCTION OPTIMIZATION PROBLEMS

Evans BAIDOO

ebaidoo2.cos@st.knust.edu.gh
Kwame Nkrumah University of Science and Technology, Department of Computer Science, PMB, KNUST, (Ghana)

Abstract

Modern real world science and engineering problems can be classified as multi-objective optimisation problems which demand for expedient and efficient stochastic algorithms to respond to the optimization needs. This paper presents an object-oriented software application that implements a firework optimization algorithm for function optimization problems. The algorithm, a kind of parallel diffuse optimization algorithm is based on the explosive phenomenon of fireworks. The algorithm presented promising results when compared to other population or iterative based meta-heuristic algorithm after it was experimented on five standard benchmark problems. The software application was implemented in Java with interactive interface which allow for easy modification and extended experimentation. Additionally, this paper validates the effect of runtime on the algorithm performance.


Keywords:

Fireworks algorithm, Function optimization, Swarm intelligence, Mathematical programming, Natural computing

Bacanin, N., Tuba, M., & Stanarevic, N. (2012). Artificial Fish Swarm Algorithm for Unconstrained Optimization Problems. Applied Mathematics in Electrical and Computer Engineering, 405–410.
  Google Scholar

Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press Inc.
DOI: https://doi.org/10.1093/oso/9780195131581.001.0001   Google Scholar

Ding, K., Zheng, S. Q., & Tan, Y. (2013). A GPU-based Parallel Fireworks Algorithm for Optimization.
DOI: https://doi.org/10.1145/2463372.2463377   Google Scholar

Gecco'13: Proceedings of the 2013 Genetic and Evolutionary Computation Conference, 9–16.
  Google Scholar

Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471. https://doi.org/10.1007/s10898-007-9149-x
DOI: https://doi.org/10.1007/s10898-007-9149-x   Google Scholar

Kennedy, J., Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942–1948.
DOI: https://doi.org/10.1109/ICNN.1995.488968   Google Scholar

Li, J., Zheng, S., & Tan, Y. (2014). Adaptive Fireworks Algorithm. 2014 IEEE Congress on Evolutionary Computation (CEC), 3214–3221. https://doi.org/10.1109/CEC.2014.6900418
DOI: https://doi.org/10.1109/CEC.2014.6900418   Google Scholar

McCaffrey, J. (2016, September). Fireworks Algorithm Optimization. Retrieved from https://msdn.microsoft.com/en-us/magazine/dn857364.aspx
  Google Scholar

Mohan, B. C., & Baskaran, R. (2012). A survey: Ant Colony Optimization based recent research and implementation on several engineering domain. Expert Systems with Applications, 39(4), 4618-4627. doi:10.1016/j.eswa.2011.09.076
DOI: https://doi.org/10.1016/j.eswa.2011.09.076   Google Scholar

Ren, Y., & Wu, Y. (2013). An efficient algorithm for high-dimensional function optimization. Soft Computing, 17, 995-1004. https://doi.org/10.1007/s00500-013-0984-z
DOI: https://doi.org/10.1007/s00500-013-0984-z   Google Scholar

Tan, Y., & Zhu, Y. (2010). Fireworks Algorithm for Optimization. In: Y. Tan, Y. Shi, & K.C. Tan (Eds.), Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science (vol. 6145, pp. 355–364). Springer.
DOI: https://doi.org/10.1007/978-3-642-13495-1_44   Google Scholar

Tang, K. S., Man, K. F., Kwong, S., & He, Q. (1996). Genetic algorithms and their applications. IEEE Signal Processing Magazine, 13(6), 22-37. https://doi.org/10.1109/79.543973
DOI: https://doi.org/10.1109/79.543973   Google Scholar

Virtual Library of Simulation Experiments: Test Functions and Datasets (n.d.). Retrieved August, 2016, from https://www.sfu.ca/~ssurjano/optimization.html
  Google Scholar

Yuan, Z., de Oca, M. A. M., Birattari, M., & Stutzle, T. (2012). Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms. Swarm Intelligence, 6(1), 49–75. https://doi.org/10.1007/s11721-011-0065-9
DOI: https://doi.org/10.1007/s11721-011-0065-9   Google Scholar

Zheng, S. Q., Janecek, A., Li, J. Z., & Tan, Y. (2014). Dynamic Search in Fireworks Algorithm. 2014 IEEE Congress on Evolutionary Computation (Cec), 3222–3229.
DOI: https://doi.org/10.1109/CEC.2014.6900485   Google Scholar

Zheng, S., Janecek, A., & Tan, Y. (2013). Enhanced Fireworks Algorithm. 2013 IEEE Congress on Evolutionary Computation, 2069-2077. https://doi.org/10.1109/CEC.2013.6557813
DOI: https://doi.org/10.1109/CEC.2013.6557813   Google Scholar

Zheng, Y. J., Xu, X. L., & Ling, H. F. (2012). A hybrid fireworks optimization method with differential evolution operators. Neurocomputing, 148, 75–80. https://doi.org/10.1016/j.neucom.2012.08.075
DOI: https://doi.org/10.1016/j.neucom.2012.08.075   Google Scholar

Download


Published
2017-03-30

Cited by

BAIDOO, E. (2017). FIREWORKS ALGORITHM FOR UNCONSTRAINED FUNCTION OPTIMIZATION PROBLEMS. Applied Computer Science, 13(1), 61–74. https://doi.org/10.23743/acs-2017-06

Authors

Evans BAIDOO 
ebaidoo2.cos@st.knust.edu.gh
Kwame Nkrumah University of Science and Technology, Department of Computer Science, PMB, KNUST, Ghana

Statistics

Abstract views: 38
PDF downloads: 2


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.


Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.