An improved differential evolution algorithm based on adaptive parameter

The differential evolution (DE) algorithm is a heuristic global optimization technique based on population which is easy to understand, simple to implement, reliable, and fast. The evolutionary parameters directly influence the performance of differential evolution algorithm. The adjustment of control parameters is a global behavior and has no general research theory to control the parameters in the evolution process at present. In this paper, we propose an adaptive parameter adjustment method which can dynamically adjust control parameters according to the evolution stage. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

[1]  Shiwen Yang,et al.  Sideband suppression in time-modulated linear arrays by the differential evolution algorithm , 2002 .

[2]  Mehmet Fatih Tasgetiren,et al.  Differential Evolution Algorithms for the Generalized Assignment problem , 2009, 2009 IEEE Congress on Evolutionary Computation.

[3]  Andries Petrus Engelbrecht,et al.  Self-adaptive Differential Evolution , 2005, CIS.

[4]  V. Aslantas,et al.  Differential Evolution Algorithm For Segmentation Of Wound Images , 2007, 2007 IEEE International Symposium on Intelligent Signal Processing.

[5]  C. Su,et al.  Network Reconfiguration of Distribution Systems Using Improved Mixed-Integer Hybrid Differential Evolution , 2002, IEEE Power Engineering Review.

[6]  Bogdan Filipic,et al.  DEMO: Differential Evolution for Multiobjective Optimization , 2005, EMO.

[7]  Jouni Lampinen,et al.  A Fuzzy Adaptive Differential Evolution Algorithm , 2005, Soft Comput..

[8]  Wang Yaonan,et al.  Differential Evolution Algorithm with Adaptive Second Mutation , 2006 .

[9]  Zhang Ying,et al.  A modified differential evolution algorithm with self-adaptive control parameters , 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering.

[10]  Adel M. Alimi,et al.  The Modified Differential Evolution and the RBF (MDE-RBF) Neural Network for Time Series Prediction , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[11]  G.A. Taylor,et al.  A differential evolution algorithm for multistage transmission expansion planning , 2007, 2007 42nd International Universities Power Engineering Conference.

[12]  Hao Shao,et al.  A Novel Differential Evolution Algorithm for a Single Batch-Processing Machine with Non-Identical Job Sizes , 2008, 2008 Fourth International Conference on Natural Computation.

[13]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[14]  B. Babu,et al.  Differential evolution for multi-objective optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[15]  A. Massa,et al.  Optimization of the Directivity of a Monopulse Antenna With a Subarray Weighting by a Hybrid Differential Evolution Method , 2006, IEEE Antennas and Wireless Propagation Letters.

[16]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[17]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[18]  D. Lowther,et al.  Differential Evolution Strategy for Constrained Global Optimization and Application to Practical Engineering Problems , 2006, IEEE Transactions on Magnetics.