FIREWORKS ALGORITHM FOR UNCONSTRAINED FUNCTION OPTIMIZATION PROBLEMS
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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.
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References
Bacanin, N., Tuba, M., & Stanarevic, N. (2012). Artificial Fish Swarm Algorithm for Unconstrained Optimization Problems. Applied Mathematics in Electrical and Computer Engineering, 405–410.
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
Ding, K., Zheng, S. Q., & Tan, Y. (2013). A GPU-based Parallel Fireworks Algorithm for Optimization. DOI: https://doi.org/10.1145/2463372.2463377
Gecco'13: Proceedings of the 2013 Genetic and Evolutionary Computation Conference, 9–16.
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
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
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
McCaffrey, J. (2016, September). Fireworks Algorithm Optimization. Retrieved from https://msdn.microsoft.com/en-us/magazine/dn857364.aspx
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
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
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
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
Virtual Library of Simulation Experiments: Test Functions and Datasets (n.d.). Retrieved August, 2016, from https://www.sfu.ca/~ssurjano/optimization.html
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
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
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
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
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