Abstract
A fast differential evolution (FDE) approach to solve several constrained engineering design optimization problems is proposed. In this approach, a new mutation strategy “DE/current-to-ppbest/bin” is proposed to get a balance between exploration and exploitation of the population. What’s more, a ranking based selection mechanism selects the promising individuals from the combination of parents and offspring to update the population. Experimental results on 5 instances extracted from engineering design show that FDE can acquire quite competitive performance. FDE is comparable to other state-of-the-art approaches in terms of solution quality. As for convergence speed, FDE is more fast, or at least comparable to, other state-of-the-art approaches. When the number of function evaluation is limited or the cost of function evaluation is expensive, FDE is a good choice.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akhtar, S., Tai, K., Ray, T.: A socio-behavioural simulation model for engineering design optimization. Eng. Optim. 34(4), 341–354 (2002)
Ao, Y.Y., Chi, H.Q., et al.: An adaptive differential evolution algorithm to solve constrained optimization problems in engineering design. Engineering 2(01), 65 (2010)
Bu, C., Luo, W., Zhu, T.: Differential evolution with a species-based repair strategy for constrained optimization. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 967–974. IEEE (2014)
Coello, C.A.C.: Self-adaptive penalties for ga-based optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 99, vol. 1. IEEE (1999)
Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2), 311–338 (2000)
Gämperle, R., Müller, S.D., Koumoutsakos, P.: A parameter study for differential evolution. Adv. Intell. Syst. Fuzzy Syst. Evol. Comput. 10, 293–298 (2002)
Hedar, A.R., Fukushima, M.: Derivative-free filter simulated annealing method for constrained continuous global optimization. J. Global Optim. 35(4), 521–549 (2006)
Hu, X., Eberhart, R.C., Shi, Y.: Engineering optimization with particle swarm. In: Swarm Intelligence Symposium, SIS 2003. Proceedings of the 2003 IEEE, pp. 53–57. IEEE (2003)
Lampinen, J., Storn, R.: Differential Evolution. Springer, Heidelberg (2004)
Li, J., Shen, A., Lu, G.: Reference point based constraint handling method for evolutionary algorithm. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI-CCI 2015. LNCS, vol. 9140, pp. 294–301. Springer, Heidelberg (2015)
Mezura-Montes, E., Coello, C.A.C., Velázquez-Reyes, J.: Increasing successful offspring and diversity in differential evolution for engineering design. In: Proceedings of the Seventh International Conference on Adaptive Computing in Design and Manufacture (ACDM 2006), pp. 131–139 (2006)
Muñoz Zavala, A.E., Hernández Aguirre, A., Villa Diharce, E.R., Botello Rionda, S.: Constrained optimization with an improved particle swarm optimization algorithm. Int. J. Intell. Comput. Cybern. 1(3), 425–453 (2008)
Ray, T., Liew, K.M.: Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans. Evol. Comput. 7(4), 386–396 (2003)
Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Trans. Evol. Comput. 4(3), 284–294 (2000)
Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, vol. 3. ICSI Berkeley (1995)
Takahama, T., Sakai, S.: Constrained optimization by the \(\varepsilon \) constrained differential evolution with gradient-based mutation and feasible elites. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1–8. IEEE (2006)
Wang, Y., Cai, Z.: Constrained evolutionary optimization by means of (\(\mu \mu +\lambda \lambda \))-differential evolution and improved adaptive trade-off model. Evol. Comput. 19(2), 249–285 (2011)
Wang, Y., Cai, Z.: Combining multiobjective optimization with differential evolution to solve constrained optimization problems. IEEE Trans. Evol. Comput. 16(1), 117–134 (2012)
Wang, Y., Cai, Z.: A dynamic hybrid framework for constrained evolutionary optimization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(1), 203–217 (2012)
Zhang, J., Sanderson, A.C.: Jade: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)
Zhang, M., Luo, W., Wang, X.: Differential evolution with dynamic stochastic selection for constrained optimization. Inf. Sci. 178(15), 3043–3074 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shen, A., Li, J. (2015). A Fast Differential Evolution for Constrained Optimization Problems in Engineering Design. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-662-49014-3_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49013-6
Online ISBN: 978-3-662-49014-3
eBook Packages: Computer ScienceComputer Science (R0)