FIREWORKS ALGORITHM FOR UNCONSTRAINED FUNCTION OPTIMIZATION PROBLEMS
Evans BAIDOO
ebaidoo2.cos@st.knust.edu.ghKwame 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 computingReferences
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
Authors
Evans BAIDOOebaidoo2.cos@st.knust.edu.gh
Kwame Nkrumah University of Science and Technology, Department of Computer Science, PMB, KNUST, Ghana
Statistics
Abstract views: 193PDF downloads: 8
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
- Maria TOMASIKOVA, Frantisek BRUMERČÍK, Aleksander NIEOCZYM, DESIGN AND DYNAMICS MODELING FOR ELECTRIC VEHICLE , Applied Computer Science: Vol. 13 No. 3 (2017)
- Sylwester KORGA, Marcin BARSZCZ, Krzysztof DZIEDZIC, DEVELOPMENT OF SOFTWARE FOR IDENTIFICATION OF FILAMENTS USED IN 3D PRINTING TECHNOLOGY , Applied Computer Science: Vol. 15 No. 1 (2019)
- Grzegorz RADZKI, Amila THIBBOTUWAWA, Grzegorz BOCEWICZ, UAVS FLIGHT ROUTES OPTIMIZATION IN CHANGING WEATHER CONDITIONS – CONSTRAINT PROGRAMMING APPROACH , Applied Computer Science: Vol. 15 No. 3 (2019)
- Krzysztof OSTROWSKI, AN EFFECTIVE METAHEURISTIC FOR TOURIST TRIP PLANNING IN PUBLIC TRANSPORT NETWORKS , Applied Computer Science: Vol. 14 No. 2 (2018)
- Robert KARPIŃSKI, Przemysław KRAKOWSKI, Józef JONAK, Anna MACHROWSKA, Marcin MACIEJEWSKI, COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES , Applied Computer Science: Vol. 19 No. 4 (2023)
- Puppala Praneeth, Majety Sathvika, Vivek Kommareddy, Madala Sarath, Saran Mallela, Koneru Suvarna Vani, Prasun Chkrabarti, CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Hanan M. SHUKUR, Shavan ASKAR, Subhi R.M. ZEEBAREE, THE UTILIZATION OF 6G IN INDUSTRY 4.0 , Applied Computer Science: Vol. 20 No. 2 (2024)
- Lucian LUPŞA-TĂTARU, NOVEL TECHNIQUE OF CUSTOMIZING THE AUDIO FADE-OUT SHAPE , Applied Computer Science: Vol. 14 No. 3 (2018)
- Raphael Olufemi AKINYEDE, Sulaiman Omolade ADEGBENRO, Babatola Moses OMILODI, A SECURITY MODEL FOR PREVENTING E-COMMERCE RELATED CRIMES , Applied Computer Science: Vol. 16 No. 3 (2020)
- Wojciech DANILCZUK, Arkadiusz GOLA, COMPUTER-AIDED MATERIAL DEMAND PLANNING USING ERP SYSTEMS AND BUSINESS INTELLIGENCE TECHNOLOGY , Applied Computer Science: Vol. 16 No. 3 (2020)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
