Abstract
There are many optimization problems in the intelligent material and adaptive material fields. Differential evolution (DE) is simple and effective and has been successfully applied to solve optimization problems. And it can be applied to intelligent material field. It is easy to understand and realized and has a strong spatial search capability compared to other evolutionary algorithms. In order to avoid the original versions of DE to remain trapped into local minima and accelerate the optimization process, several approaches have been proposed. The mutation of the classical DE is improved in this paper. It effectively guarantees the convergence of the algorithm and avoids the local minima. Testing and comparing results showed the effectiveness of the algorithm.
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References
Bäck, T., Schwefel, H.P.: An Overview of Evolutionary Algorithms for Parameter Optimization. Evol. Comput. 1, 1–23 (1993)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA2 II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimizer In Noisy and Continuously Changing Environments. In: Proceeding of the IASTED International Conference on Artificial Intelligence and Soft Computing, pp. 2892–2894. I2ASTED/ ACTA Press, ICancun, Mexico (2001)
Michalewicz, Z., Shoenauer, M.: Evolutionary Algorithms for Constrained Parameter Optimization Problems. Evolutionary Computation 4(1), 1–32 (1996)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Storn, R., Price, K.: Differential Evolution-A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Tsai, J.-T., Liu, T.-K., Chou, J.-H.: Hybrid Taguchi-Genetic Algorithm for Global Numerical Optimization. IEEE Trans. Evolutionary Computation 8(4), 365–377
Dorronsoro, B., Bouvry, P.: Improving Classical and Decentralized Differential Evolution with New Mutation Operator and Population Topologies. IEEE Transactions on Evolutionary Computation 15(1), 67–98
Mayer, D.G., Kinghorn, B.P., Archer, A.A.: Differential Evolution - an Easy and Efficient Evolutionary Algorithm for Model Optimization. Agricultural Systems 83(3), 315–328 (2005)
Rui, M., Arvind, M.: DynDE:A Differential Evolution for Dynamic Optimization Problems Mendes. In: IEEE. Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005), pp. 2808–2815. IEEE Service Center, New York (2005)
Mezura-Montes, E., Vel´azquez-Reyes, J., Coello, C.A.C.: Modified Differential Evolution for Constrained Optimization. In: 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, July 16-21, pp. 25–32 (2006)
Ali, M., Pant, M., Singh, V.P.: An Improved Differential Evolution Algorithm for Real Parameter Optimization Problems. International Journal of Recent Trends in Engineering 1(5), 63–65 (2009)
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Zhang, L., Xu, X., Zhou, C., Ma, M., Yu, Z. (2011). An Improved Differential Evolution Algorithm for Optimization Problems. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23777-5_39
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DOI: https://doi.org/10.1007/978-3-642-23777-5_39
Publisher Name: Springer, Berlin, Heidelberg
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