Modified Self-adaptive Strategy for Controlling Parameters in Differential Evolution
In this paper, we propose a new technical to modify the self-adaptive Strategy for Controlling Parameters in Differential Evolution algorithm (MSADE). The DE algorithm has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters as NP (Number of Particles), F (scaling factor) and CR (crossover), which are kept fixed throughout the entire evolutionary process. However, these control parameters are very sensitive to the setting of the control parameters based on their experiments. The value of control parameters depend on the characteristics of each objective function, so we have to tune their value in each problem that mean it will take too long time to perform. We present a new version of the DE algorithm for obtaining self-adaptive control parameter settings that show good performance on numerical benchmark problems.
KeywordsDifferential Evolution (DE) Global search Multi-peak problems Local search
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- 1.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)Google Scholar
- 3.Fogel, L.J., Angeline, P.J., Fogel, D.B.: An evolutionary programming approach to self-adaptation in nite state machines. In: McDonnell, J.R., Reynolds, R.G., Fogel, D.B. (eds.) Evolutionary Programming IV: Proc. of Fourth Annual Conference on Evolutionary Programming, pp. 355–365. MIT Press, Cambridge (1995)Google Scholar
- 4.Soliman, O.S., Bui, L., Abbass, H.A.: The effect of a stochastic step length on the performance of the differential evolution algorithm. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 2850–2857. IEEE Press (2007)Google Scholar
- 5.Liu, J., Lampinen, J.: Fuzzy adaptive differential evolution algorithm. In: Proceedings of the 17th IEEE Region 10 International Conference on Computer, Communications, Control and Power Engineering, vol. III, pp. 606–611 (2002)Google Scholar
- 6.Price, K.V.: An introduction to differential evolution. In: New Ideas in Optimization, pp. 79–108. McGraw-Hill Ltd., London (1999)Google Scholar
- 7.Abbass, H.A.: The self-adaptive pareto differential evolution algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2002), Piscataway, NJ, vol. 1, pp. 831–836. IEEE Press (2002)Google Scholar
- 8.Reynoso-Meza, G., Sanchis, J., Blasco, X., Herrero, J.M.: Hybrid DE algorithm with adaptive crossover operator for solving real-world numerical optimization problems. In: 2011 IEEE Congress on Evolutionary Computation (CEC), June 5-8 (2011)Google Scholar
- 10.Hasegawa, H., Tooyama, S.: Adaptive plan system with genetic algorithm using the variable neighborhood range control. In: IEEE Congress on Evolutionary Computation (CEC 2009), pp. 846–853 (2009)Google Scholar