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Hybridization of Metaheuristic and Population-Based Algorithms with Neural Network Learning for Function Approximation

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Advances in Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1463))

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Abstract

This paper attempts to improve the learning representation of radial basis function neural network (RBFNN) through metaheuristic algorithm (MHA) and evolutionary algorithm (EA). Next, the ant colony optimization (ACO)-based and genetic algorithm (GA)-based approaches are employed to train RBFNN. The proposed hybridization of ACO-based and GA-based approaches (HAG) algorithm incorporates the complementarity of exploration and exploitation abilities to reach resolution optimization. The property of population diversity has higher chance to search the global optimal instead of being restricted to local optimal extremely in two benchmark problems. The experimental results have shown that ACO-based and GA-based approaches can be integrated intelligently and develop into a hybrid algorithm which aims for receiving the best precise learning expression among relevant algorithms in this paper. Additionally, method assessment results for two benchmark continuous test function experiments and show that the proposed HAG algorithm outperforms relevant algorithms in term of preciseness for learning of function approximation.

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References

  1. Al-Obaidi, M.A., Li, J.P., Kara-Zaïtri, C., Mujtaba, I.M.: Optimisation of reverse osmosis based wastewater treatment system for the removal of chlorophenol using genetic algorithms. Chem. Eng. J. 316, 91–100 (2017)

    Article  Google Scholar 

  2. Al-Roomi, A.R., El-Hawary, M.E.: Universal functions originator. Appl. Soft Comput. 94, 106417–106448 (2020)

    Article  Google Scholar 

  3. Ansari, M., Othman, F., El-Shafie, A.: Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant. Sci. Total Environ. 722, 137878–137890 (2020)

    Article  Google Scholar 

  4. Bilal, M.P., Zaheer, H., Garcia-Hernandez, L., Abraham, A.: Differential evolution: a review of more than two decades of research. Eng. Appl. Artif. Intell. 90, 103479–1034502 (2020)

    Article  Google Scholar 

  5. Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 2(2), 302–309 (1991)

    Article  Google Scholar 

  6. Chen, S., Wu, Y., Luk, B.L.: Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Trans. Neural Netw. 10(5), 1239–1243 (1999)

    Article  Google Scholar 

  7. Denker, J.S.: Neural network models of learning and adaptation. Physica D. 22, 216–232 (1986)

    Article  MathSciNet  Google Scholar 

  8. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996). https://doi.org/10.1109/3477.484436

    Article  Google Scholar 

  9. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley and Sons, New York (1973)

    MATH  Google Scholar 

  10. Dzalbs, I., Kalganova, T.: Accelerating supply chains with Ant Colony Optimization across a range of hardware solutions. Comput. Ind. Eng. 147, 106610–106623 (2020)

    Article  Google Scholar 

  11. Faramarzi, A., Heidarinejad, M., Stephens, B., Mirjalili, S.: Equilibrium optimizer: a novel optimization algorithm. Knowl. Based Syst. 191, 105190–105210 (2020)

    Article  Google Scholar 

  12. Ghafil, H.N., Jarmai, K.: Dynamic differential annealed optimization: new metaheuristic optimization algorithm for engineering applications. Appl. Soft Comput. 93, 106392–106410 (2020)

    Article  Google Scholar 

  13. Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Boston (1989)

    MATH  Google Scholar 

  14. Hamzaday, A., Baykasoglu, A., Akpinar, S.: Solving combinatorial optimization problems with single seekers society algorithm. Knowl. Based Syst. 201–202, 106036–106065 (2020)

    Article  Google Scholar 

  15. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Book  Google Scholar 

  16. Islam, J., Vasant, P.M., Negash, B.M., Laruccia, M.B., Myint, M., Watada, J.: A holistic review on artificial intelligence techniques for well placement optimization problem. Adv. Eng. Softw. 141, 102767–102786 (2020)

    Article  Google Scholar 

  17. Jakobsson, S., Andersson, B., Edelvik, F.: Rational radial basis function interpolation with applications to antenna design. J. Comput. Appl. Math. 233(4), 889–904 (2009)

    Article  MathSciNet  Google Scholar 

  18. Jugulum, R., Taguchi, S., et al.: Computer-Based Robust Engineering: Essentials for DFSS. ASQ Quality Press, Milwaukee (2004)

    Google Scholar 

  19. Kefayat, M., Ara, A.I., Niaki, S.A.N.: A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. Energ. Convers. Manage. 92, 149–161 (2015)

    Article  Google Scholar 

  20. Kozak, J., Boryczka, U.: Multiple boosting in the Ant colony decision forest meta-classifier. Knowl. Based Syst. 75, 141–151 (2015)

    Article  Google Scholar 

  21. Lin, Y.K., Yeh, C.T., Huang, P.S.: A hybrid ant-tabu algorithm for solving a multistate flow network reliability maximization problem. Appl. Soft Comput. 13, 3529–3543 (2013)

    Article  Google Scholar 

  22. Lin, C.F., Wu, C.C., Yang, P.H., Kuo, T.Y.: Application of Taguchi method in lightemitting diode backlight design for wide color gamut displays. J. Disp. Technol. 5(8), 323–330 (2009)

    Article  Google Scholar 

  23. Liu, B., Aliakbarian, H., Ma, Z., Vandenbosch, G.A.E., Gielen, G., Excell, P.: An efficient method for antenna design optimization based on evolutionary computation and machine learning techniques. IEEE Trans. Antennas Propag. 62(1), 7–18 (2014)

    Article  Google Scholar 

  24. Looney, C.G.: Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans. Knowl. Data Eng. 8(2), 211–226 (1996)

    Article  Google Scholar 

  25. Luan, J., Yao, Z., Zhao, F., Song, X.: A novel method to solve supplier selection problem: hybrid algorithm of genetic algorithm and ant colony optimization. Math. Comput. Simul. 156, 294–309 (2019)

    Article  MathSciNet  Google Scholar 

  26. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)

    Article  MathSciNet  Google Scholar 

  27. Naghibi, S.A., Ahmadi, K., Daneshi, A.: Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resour. Manage. 31(9), 2761–2775 (2017)

    Article  Google Scholar 

  28. Qasem, S.N., Shamsuddin, S.M., Zain, A.M.: Multi-objective hybrid evolutionary algorithms for radial basis function neural network design. Knowl. Based Syst. 27, 475–497 (2012)

    Article  Google Scholar 

  29. Rao, R.V., Rai, D.P., Balic, J.: A multi-objective algorithm for optimization of modern machining processes. Eng. Appl. Artif. Intell. 61, 103–125 (2017)

    Article  Google Scholar 

  30. Salehpoor, I.B., Molla-Alizadeh-Zavardehi, S.: A constrained portfolio selection model at considering risk-adjusted measure by using hybrid meta-heuristic algorithms. Appl. Soft Comput. 75, 233–253 (2019)

    Article  Google Scholar 

  31. Savsani, P., Jhala, R.L., Savsani, V.: Effect of hybridizing biogeography-based optimization (BBO) technique with artificial immune algorithm (AIA) and Ant colony optimization (ACO). Appl. Soft Comput. 21, 542–553 (2014)

    Article  Google Scholar 

  32. Sulaiman, M.H., Mustaffa, Z., Saari, M.M., Daniyal, H.: Barnacles mating optimizer: a new bio-inspired algorithm for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103330–103342 (2020)

    Article  Google Scholar 

  33. Taguchi, G., Chowdhury, S., Wu, Y.: Taguchi’s Quality Engineering Handbook. Wiley, Hoboken (2005)

    MATH  Google Scholar 

  34. Tayfur, G.: Modern optimization methods in water resources planning, engineering and management. Water Resour. Manage. 31(10), 3205–3233 (2017)

    Article  Google Scholar 

  35. Tian, Z.: Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM. Eng. Appl. Artif. Intell. 91, 103573–103596 (2020)

    Article  Google Scholar 

  36. Urquhart, M., Ljungskog, E., Sebben, S.: Surrogate-based optimisation using adaptively scaled radial basis functions. Appl. Soft Comput. 88, 106050–106066 (2020)

    Article  Google Scholar 

  37. Yang, Z., Li, K., Guo, Y., Ma, H., Zheng, M.: Compact real-valued teaching-learning based optimization with the applications to neural network training. Knowl. Based Syst. 159, 51–62 (2018)

    Article  Google Scholar 

  38. Yin, X., Niu, Z., He, Z., Li, Z.S., Lee, D.: An integrated computational intelligence technique based operating parameters optimization scheme for quality improvement oriented process-manufacturing system. Comput. Ind. Eng. 140, 106284–106298 (2020)

    Article  Google Scholar 

  39. Zhang, F., Liao, Z.: Gold price forecasting based on RBF neural network and hybrid fuzzy clustering algorithm. In: Jiuping, X., Fry, J.A., Lev, B., Hajiyev, A. (eds.) Proceedings of the Seventh International Conference on Management Science and Engineering Management, pp. 73–84. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-40078-0_6

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Business Administration, Hungkuo Delin University of Technology, No. 1, Ln 380, Qingyun Road, New Taipei City, 23654, Taiwan

    Zhen-Yao Chen

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  1. Zhen-Yao Chen
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Correspondence to Zhen-Yao Chen .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  2. VU Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. University of the West of England, Bristol, UK

    Elias Pimenidis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

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Chen, ZY. (2021). Hybridization of Metaheuristic and Population-Based Algorithms with Neural Network Learning for Function Approximation. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-88113-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88112-2

  • Online ISBN: 978-3-030-88113-9

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