Abstract
This paper is focusing on comparing the performance of Differential Evolution (DE) variants, in the light of analyzing their Explorative power on a set of benchmark function. We have chosen fourteen different variants of DE and fourteen benchmark functions grouped by feature: Unimodal Separable, Unimodal NonSeparable, Multimodal Separable and Multimodal NonSeparable. Fourteen variants of DE were implemented and tested on these fourteen functions for the dimension of 30. The explorative power of the variants is evaluated and analyzed by measuring the evolution of population variance, at each generation. This analysis provides insight about the competitiveness of DE variants in solving the problem at hand.
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References
Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012, ICSI (1995)
Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic Strategy for Global Optimization and Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)
Price, K.V.: An Introduction to Differential Evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 79–108. Mc Graw-Hill, UK (1999)
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A practical Approach to Global Optimzation. Springer, Heidelberg (2005)
Vesterstrom, J., Thomsen, R.: A Comparative Study of Differential Evolution Particle Swarm Optimization and Evolutionary Algorithm on Numerical Benchmark Problems. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2004), vol. 3, pp. 1980–1987 (2004)
Zaharie, D.: On the Explorative Power of Differential Evolution Algorithms. In: 3rd Int. Workshop Symbolic and Numeric Algorithms of Scientific Computing SYNASC 2001, Romania (2001)
Zaharie, D.: Critical values for the control parameters of Differential Evolution algorithms. In: Proc. of the 8th International Conference of Soft Computing, pp. 62–67 (2002)
Zaharie, D.: Control of Population Diversity and Adaptation in Differential Evolution Algorithms. In: Matouek, R., Omera, P. (eds.) Proceedings of Mendel Ninth International Conference on Soft Computing, pp. 41–46 (2003)
Angela, A.R.S., Andrade, A.O., Soares, A.B.: Exploration vs Exploitation in Differential Evolution. Convention in Communication, Interaction and Social Intelligence, Scotland (2008)
Beyer, H.-G.: On the Explorative Power of ES/EP-like Algorithms. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 323–334. Springer, Heidelberg (1998)
Yao, H., Liu, Y., Lian, K.H., Lin, G.: Fast Evolutionary Algorithms. In: Rozenberg, G., Back, T., Eiben, A. (eds.) Advances in Evolutionary Computing Theory and Applications, pp. 45–94. Springer, New York (2003)
Mezura-Montes, E., Velazquez-Reyes, J., Coello Coello, A.C.: A Comparative Study on Differential Evolution Variants for Global Optimization. In: GECCO 2006 (2006)
Mezura-Montes, E.: Personal Communication (unpublished)
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Jeyakumar, G., Shanmugavelayutham, C. (2010). Analyzing the Explorative Power of Differential Evolution Variants on Different Classes of Problems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_12
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DOI: https://doi.org/10.1007/978-3-642-17563-3_12
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