An Informative Differential Evolution with Self Adaptive Re-clustering Technique

We propose an informative Differential Evolution (DE) algorithm where the information gained by the individuals of a cluster will be exchanged after a certain number of iterations called refreshing gap. The DE is empowered with a clustering technique to improve its efficiency over multimodal landscapes. During evolution, self-adaptive behaviour helps in re-clustering. With the better explorative power of the proposed algorithm we have used a new local search technique for fine tuning near a suspected optimal position. The performance of the proposed algorithm is evaluated over 25 benchmark functions and compared with existing algorithms.

[1]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[2]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[3]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[4]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[5]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[6]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[7]  Joni-Kristian Kämäräinen,et al.  Differential Evolution Training Algorithm for Feed-Forward Neural Networks , 2003, Neural Processing Letters.

[8]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  Marco Dorigo.  Ant colony optimization , 2004, Scholarpedia.

[11]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[12]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[13]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[14]  Swagatam Das,et al.  An improved differential evolution algorithm with fitness-based adaptation of the control parameters , 2011, Inf. Sci..

[15]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[16]  Rainer Storn,et al.  Differential evolution design of an IIR-filter , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

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