Hybrid Differential Evolutionary Algorithm Based on Extremal Optimization

Abstract:

A new hybrid algorithm based on Differential Evolution (DE) and Extremal Optimization (EO) is proposed to solve the premature convergence and low precision of standard differential evolution when applied to complex optimization problems. The key points of it lie in: the hybrid algorithm introduced the population-based Extremal Optimization algorithm in the iteration process of DE when population aggregation got the high degree, which uses the volatility of EO to increase the diversity of population and the ability of breaking away from the local optimum. Simulations show that the hybrid algorithm has remarkable global convergence ability, and can avoid the premature convergence effectively.

• Periodical: Applied Mechanics and Materials (Volume 339)
• Pages: 259-264
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.339.259
• Online since: July 2013
• Authors: Cong Jiao Wang, Xi Huai Wang, Jian Mei Xiao
• Keywords: Differential Evolution, Extremal Optimization, Global Optimization, Hybrid Algorithm

References

[1] Rainer Storn, Kenneth Price:Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization,1997,11(4):341-359.

[2] Kenneth Price,Rainer Storn:Differential Evolution-A Practical Approach to Global Optimization .Berlin, Germany: Springer-Verlag, 2006: 133-152.

[3] Varadarajan M, Swarup KS:Network loss minimization with voltage security using differential evolution .Electric Power Systems Research, 2008,78(5):815-823.

DOI: 10.1016/j.epsr.2007.06.005

[4] Swagatam Das,Ajith Abraham,Amit Konar:Automatic clustering using an improved differential evolution algorithm.IEEE Transaction on Systems, Man and Cybernetics, 2008,38(1): 218-236.

DOI: 10.1109/tsmca.2007.909595

[5] Das S, Abraham A:Differential evolution using a neighborhood-based mutation operator. IEEE Trans on Evolutionary Computation.2009,13(3):526-553

DOI: 10.1109/tevc.2008.2009457

[6] YUAN yun-gang,SUN Zhi-guo,QU Guang-ji:Simulation Study of Differential,Journal of System Simulation,2007,19(20):4646-4648.

[7] Brest J, Grener S, Boskovic B:Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 646-657.

DOI: 10.1109/tevc.2006.872133

[8] Qin A, Huang V, Suganthan P:Differential evolution algorithm with strategy adaptation for global numerical optimization.IEEE Transactions on Evolutionary Computation,2009,13(2): 398-417.

DOI: 10.1109/tevc.2008.927706

[9] Das S, Abraham A, Konar A:Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives.Studies in Computational Intelligence,2008,116:1-38.

DOI: 10.1007/978-3-540-78297-1_1

[10] Boettcher S, Percus A G:Extremal Optimization: Methods Derived from Co-Evolution. Proceedings of the Genetic and Evolutionary Computation Conference. San Francisco: Morgan Kaufmann,1999: 825-832.

[11] QI Jie, WANG Ding-wei:Overview of extremal optimization algorithm,Control and Decision,2007,22(10):1081-1085

[12] Lee C Y, Yao X:Evolutionary Algorithms with Adaptive Levy Mutations. In Proceedings of the 2001 Congress on Evolutionary Computation,2001:568-575.

DOI: 10.1109/cec.2001.934442

[13] Lv Zhen-su,Hou Zhi-rong:Particle Swarm Optimization with Adaptive Mutation, Electronica Sinica,,2004,32(3):416-420.