Fireworks Algorithm for Unconstrained Function Optimization Problems

• Autorzy: Baidoo E.
• Języki publikacji: EN

Abstrakty

EN

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 ben-chmark problems. The software application was implemented in Java with interactive interface which allow for easy modification and extended expe-rimentation. Additionally, this paper validates the effect of runtime on the al-gorithm performance.

Słowa kluczowe

EN

Fireworks algorithm; Function optimization; swarm intelligence; Mathematical programming; Natural computing

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2017
• Tom: Vol. 13, no 1
• Strony: 61--74
• Opis fizyczny: Bibliogr. 16 poz., fig., tab.

Twórcy

autor

Baidoo E.

• Kwame Nkrumah University of Science and Technology, Department of Computer Science, PMB, KNUST, Ghana

Bibliografia

• 1. Bacanin, N., Tuba, M., & Stanarevic, N. (2012). Artificial Fish Swarm Algorithm for Unconstrained Optimization Problems. Applied Mathematics in Electrical and Computer Engineering, 405–410.
• 2. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press Inc.
• 3. Ding, K., Zheng, S. Q., & Tan, Y. (2013). A GPU-based Parallel Fireworks Algorithm for Optimization. Gecco'13: Proceedings of the 2013 Genetic and Evolutionary Computation Conference, 9–16.
• 4. 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. doi:10.1007/s10898-007-9149-x
• 5. Kennedy, J., Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942–1948.
• 6. Li, J., Zheng, S., & Tan, Y. (2014). Adaptive Fireworks Algorithm. 2014 IEEE Congress on Evolutionary Computation (CEC), 3214–3221. doi:10.1109/CEC.2014.6900418
• 7. McCaffrey, J. (2016, September). Fireworks Algorithm Optimization. Retrieved from https://msdn.microsoft.com/en-us/magazine/dn857364.aspx
• 8. 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
• 9. Ren, Y., & Wu, Y. (2013). An efficient algorithm for high-dimensional function optimization. Soft Computing, 17, 995-1004. doi:10.1007/s00500-013-0984-z
• 10. 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.
• 11. Tang, K. S., Man, K. F., Kwong, S., & He, Q. (1996). Genetic algorithms and their applications. IEEE Signal Processing Magazine, 13(6), 22-37. doi:10.1109/79.543973
• 12. Virtual Library of Simulation Experiments: Test Functions and Datasets (n.d.). Retrieved August, 2016, from https://www.sfu.ca/~ssurjano/optimization.html
• 13. 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. doi:10.1007/s11721-011-0065-9
• 14. 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.
• 15. Zheng, S., Janecek, A., & Tan, Y. (2013). Enhanced Fireworks Algorithm. 2013 IEEE Congress on Evolutionary Computation, 2069-2077. doi:10.1109/CEC.2013.6557813
• 16. Zheng, Y. J., Xu, X. L., & Ling, H. F. (2012). A hybrid fireworks optimization method with differential evolution operators. Neurocomputing, 148, 75–80. doi:10.1016/j.neucom.2012.08.075

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)