A hybrid differential evolutionary algorithm based on the hierarchical clustering

The unconstrained global optimization algorithm has great applicability. Differential Evolution is widely used to solve the unconstrained global optimization problem. In this paper, we present a Hierarchical Clustering Differential Evolution Algorithm (HCDE), which combines Hierarchical Clustering and Differential Evolution. We cluster the population of Differential Evolution Algorithm every K generations. Then updating current population by population update strategy. The HCDE is tested by 30 test problems, the good or excellent performance of HCDE has been verified by comparing with other improved DE algorithms.

[1]  Chris Fraley,et al.  Algorithms for Model-Based Gaussian Hierarchical Clustering , 1998, SIAM J. Sci. Comput..

[2]  Li Xin,et al.  A Hybrid ABC-DE Algorithm and Its Application for Time-Modulated Arrays Pattern Synthesis , 2013, IEEE Transactions on Antennas and Propagation.

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

[4]  Safieddin Safavi-Naeini,et al.  A hybrid evolutionary programming method for circuit optimization , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[5]  Vo Ngoc Dieu,et al.  A hybrid differential evolution and harmony search for nonconvex economic dispatch problems , 2013, 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO).

[6]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

[7]  Jun Zhang,et al.  A new differential evolution algorithm with dynamic population partition and local restart , 2011, GECCO '11.

[8]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[9]  Hong Liu,et al.  A differential evolutionary architecture for artificial neural trees with applications to medical data mining , 2008, 2008 IEEE International Symposium on IT in Medicine and Education.

[10]  Hitoshi Iba,et al.  Accelerating Differential Evolution Using an Adaptive Local Search , 2008, IEEE Transactions on Evolutionary Computation.

[11]  Changhe Li,et al.  A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments , 2010, IEEE Transactions on Evolutionary Computation.

[12]  Kalyanmoy Deb,et al.  A population-based algorithm-generator for real-parameter optimization , 2005, Soft Comput..

[13]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[14]  Juan Zhou,et al.  Improved differential evolution based BP neural network for prediction of groundwater table , 2010, 2010 Third International Symposium on Knowledge Acquisition and Modeling.

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

[16]  Wei Du,et al.  Improving differential evolution with impulsive control framework , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[17]  Ming Yang,et al.  Differential Evolution With Auto-Enhanced Population Diversity , 2015, IEEE Transactions on Cybernetics.

[18]  Wenyin Gong,et al.  A clustering-based differential evolution for global optimization , 2011, Appl. Soft Comput..

[19]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[20]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.