Abstract
Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents a simple and modified hybridized Differential Evolution algorithm for solving global optimization problems. The proposed algorithm is a hybrid of Differential Evolution (DE) and Evolutionary Programming (EP). Based on the generation of initial population, three versions are proposed. Besides using the uniform distribution (U-MDE), the Gaussian distribution (G-MDE) and Sobol sequence (S-MDE) are also used for generating the initial population. Empirical results show that the proposed versions are quite competent for solving the considered test functions.
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References
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial intelligence through a simulation of evolution. In: Maxfield, M., Callahan, A., Fogel, L.J. (eds.) Biophysics and Cybernetic systems. Proc. of the 2nd Cybernetic Sciences Symposium, pp. 131–155. Spartan Books (1965)
Rechenberg, I.: Evolution Strategy: Optimization of Technical systems by means of biological evolution. Fromman-Holzboog (1973)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, Perth, Australia, pp. IV:1942–IV:1948. IEEE Service Center, Piscataway (1995)
Storn, R., Price, K.: Differential Evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report, International Computer Science Institute, Berkley (1995)
Blesa, M.J., Blum, C.: A nature-inspired algorithm for the disjoint paths problem. In: Proc. Of 20th Int. Parallel and Distributed Processing Symposium, pp. 1–8. IEEE press, Los Alamitos (2006)
delValle, Y., Moorthy, G.K.V., Mohagheghi, S., Hernandez, J.-C., Harley, R.G.: Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems. IEEE Trans. On Evolutionary Computation 12(2), 171–195 (2008)
Hsiao, C.-T., Chahine, G., Gumerov, N.: Application of a Hybrid Genetic/Powell Algorithm and a Boundary Element Method to Electrical Impedence Tomograpghy. Journal of Computational Physics 173, 433–453 (2001)
Kannan, S., Slochanal, S.M.R., Pathy, N.P.: Application and Comparison of metaheuristic techniques to generation expansion planning problem. IEEE Trans. on Power Systems 20(1), 466–475 (2005)
Paterlini, S., Krink, T.: High performance clustering with differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 2004–2011 (2004)
Omran, M., Engelbrecht, A., Salman, A.: Differential evolution methods for unsupervised image classification. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 966–973 (2005)
Storn. R.: Differential evolution design for an IIR-filter with requirements for magnitude and group delay. Technical Report TR-95-026, International Computer Science Institute, Berkeley, CA (1995)
Babu, B., Angira, R.: Optimization of non-linear functions using evolutionary computation. In: Proceedings of the 12th ISME International Conference on Mechanical Engineering, India, pp. 153–157 (2001)
Angira, R., Babu, B.: Evolutionary computation for global optimization of non-linear chemical engineering processes. In: Proceedings of International Symposium on Process Systems Engineering and Control, Mumbai, pp. 87–91 (2003)
Abbass, H.: A memetic pareto evolutionary approach to artificial neural networks. In: Stumptner, M., Corbett, D.R., Brooks, M. (eds.) Canadian AI 2001. LNCS, vol. 2256, pp. 1–12. Springer, Heidelberg (2002a)
Vesterstroem, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. Proc. Congr. Evol. Comput. 2, 1980–1987 (2004)
Andre, J., Siarry, P., Dognon, T.: An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization. Advance in Engineering Software 32, 49–60 (2001)
Hrstka, O., Ku˘cerová, A.: Improvement of real coded genetic algorithm based on differential operators preventing premature convergence. Advance in Engineering Software 35, 237–246 (2004)
Chiou, J.-P.: Variable scaling hybrid differential evolution for large-scale economic dispatch problems. Electric Power Systems Research 77(3-4), 212–218 (2007)
Wang, F.-S., Su, T.-L., Jang, H.-J.: Hybrid Differential Evolution for Problems of Kinetic Parameter Estimationand Dynamic Optimization of an Ethanol Fermentation Process. Ind. Eng. Chem. Res. 40(13), 2876–2885 (2001)
Luo, C., Yu, B.: Low Dimensional Simplex Evolution—A Hybrid Heuristic for Global Optimization. In: Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 470–474 (2007)
Kimura, S., Matsumura, K.: Genetic Algorithms using low discrepancy sequences. In: Proc. of GEECO 2005, pp. 1341–1346 (2005)
Nguyen, X.H., Nguyen, Q.U., Mckay, R.I., Tuan, P.M.: Initializing PSO with Randomized Low-Discrepancy Sequences: The Comparative Results. In: Proc. of IEEE Congress on Evolutionary Algorithms, pp. 1985–1992 (2007)
Pant, M., Thangaraj, R., Abraham, A.: Improved Particle Swarm Optimization with Low-discrepancy Sequences. In: Proc. IEEE Cong. on Evolutionary Computation, Hong Kong, pp. 3016–3023 (2008)
Fogel, L.J.: Autonomous Automata. Industrial Research 4, 14–19 (1962)
Bäck, T., Schwefel, H.-P.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1(1), 1–23 (1993)
Fogel, D.B.: Evolutionary Computation: Toward a new Philosophy of Machine Intelligence. IEEE press, Los Alamitos (1995)
Hao, Z.-F., Gua, G.-H., Huang, H.: A Particle Swarm Optimization Algorithm with Differential Evolution. In: Sixth International conference on Machine Learning and Cybernetics, pp. 1031–1035 (2007)
Omran, M.G.H., Engelbrecht, A.P., Salman, A.: Differential Evolution based Particle Swarm Optimization. In: IEEE Swarm Intelligence Symposium (SIS 2007), pp. 112–119 (2007)
Zhang, W.-J., Xie, X.-F.: DEPSO: Hybrid Particle Swarm with Differential Evolution Operator. In: IEEE International Conference on Systems, Man & Cybernetics (SMCC), Washington D C, USA, pp. 3816–3821 (2003)
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Thangaraj, R., Pant, M., Abraham, A., Badr, Y. (2009). Hybrid Evolutionary Algorithm for Solving Global Optimization Problems. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_37
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DOI: https://doi.org/10.1007/978-3-642-02319-4_37
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