A Modified Differential Evolution Algorithm for Numerical Optimization Problems

Article Preview

Abstract:

To efficiently enhance the global search and local search of Differential Evolution algorithm ( DE), A modified differential evolution algorithm (MDE) is proposed in this paper. The MDE and the DE are different in two aspects. The first is the MDE Algorithm use a strategy of Pitch adjustment instead of original mutation operation, this can enhance the convergence of the MDE, the second is integrate the opposed-learning operation in the crossover operation to prevent DE from being trapped into local optimum. Four test functions are adopted to make comparison with original DE, the MDE has demonstrated stronger velocity of convergence and precision of optimization than differential DE algorithm and PSO.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

3585-3588

Citation:

Online since:

August 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Liang ZHANG, Ziping DU. Affinity Propagation Clustering with Geodesic Distances. Journal of Computational Information Systems, 2010, 6(1): 47- 53.

Google Scholar

[2] Xiulan ZHANG, Fengyun ZHANG. Synchronizing Uncertain Chaotic Systems by Using Adaptive Fuzzy Chattering Free Sliding Mode Control. Journal of Computational Information Systems, 2012, 8(24): 10509- 10515.

Google Scholar

[3] Ji ZHAO, Juan MEI, Yi FU. An Improved Quantum-behavior Particle Swarm Based on Speciation in Dynamic Environments. Journal of Computational Information Systems, 2012, 8 (20) : 8597- 8604.

Google Scholar

[4] M. Dorigo, V. Maniezzo, A. Golomi, Ant system: optimization by a colony of cooperation agents. IEEE Transactions on SMC, 1996, 26(1): 29-41.

Google Scholar

[5] Kirkpatrick S, Gelatt C and Vecchi M. Optimization by simulated annealing. Science, 1983, 220: 671-680.

DOI: 10.1126/science.220.4598.671

Google Scholar

[6] LI Xiaolei, SHAO Zhijiang, QIAN Jixin. An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm. Systems Engineering Theory & Practice, 2002, 22(11): 32-38.

Google Scholar

[7] D. Dasgupta, N. Attoh-okine. Immunity Based Systems: A Survey[C]. Proc IEEE International Conference on Systems, Man, and Cybernetics, Orlando, Florida, 1997: 369-374.

DOI: 10.1109/icsmc.1997.625778

Google Scholar