Comparative analysis of a modified differential evolution algorithm based on bacterial mutation scheme

A new modified differential evolution algorithm DE-BEA, is proposed to improve the reliability of the standard DE/current-to-rand/1/bin by implementing a new mutation scheme inspired by the bacterial evolutionary algorithm (BEA). The crossover and the selection schemes of the DE method are also modified to fit the new DE-BEA mechanism. The new scheme diversifies the population by applying to all the individuals a segment based scheme that generates multiple copies (clones) from each individual one-by-one and applies the BEA segment-wise mechanism. These new steps are embedded in the DE/current-to-rand/bin scheme. The performance of the new algorithm has been compared with several DE variants over eighteen benchmark functions including several CEC 2005 test problems and it shows reliability in most of the test cases.

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

[2]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[3]  Ilpo Poikolainen,et al.  Micro-differential evolution with extra moves along the axes , 2013, 2013 IEEE Symposium on Differential Evolution (SDE).

[4]  Chaohua Dai,et al.  Dynamic multi-group self-adaptive differential evolution algorithm for reactive power optimization , 2010 .

[5]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[6]  Ruhul A. Sarker,et al.  An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems , 2013, IEEE Transactions on Industrial Informatics.

[7]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[8]  Jouni Lampinen,et al.  A Trigonometric Mutation Operation to Differential Evolution , 2003, J. Glob. Optim..

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

[10]  Ivan Zelinka,et al.  ON STAGNATION OF THE DIFFERENTIAL EVOLUTION ALGORITHM , 2000 .

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

[12]  Miklós F. Hatwágner,et al.  Comparative Analysis of Parallel Gene Transfer Operators in the Bacterial Evolutionary Algorithm , 2011 .

[13]  László T. Kóczy,et al.  Fuzzy rule extraction by bacterial memetic algorithms , 2009, Int. J. Intell. Syst..

[14]  T. Samaras,et al.  Self-Adaptive Differential Evolution Applied to Real-Valued Antenna and Microwave Design Problems , 2011, IEEE Transactions on Antennas and Propagation.

[15]  Dexuan Zou,et al.  A novel modified differential evolution algorithm for constrained optimization problems , 2011, Comput. Math. Appl..

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

[17]  Ponnuthurai N. Suganthan,et al.  An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Ajith Abraham,et al.  A Bacterial Evolutionary Algorithm for automatic data clustering , 2009, 2009 IEEE Congress on Evolutionary Computation.

[19]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[20]  David Naso,et al.  Compact Differential Evolution , 2011, IEEE Transactions on Evolutionary Computation.

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

[22]  Luciano Stefanini,et al.  A Differential Evolution algorithm to deal with box, linear and quadratic-convex constraints for boundary optimization , 2012, J. Glob. Optim..

[23]  Dimitris K. Tasoulis,et al.  Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators , 2011, IEEE Transactions on Evolutionary Computation.

[24]  Adam Kiczko,et al.  Differential Evolution algorithm with Separated Groups for multi-dimensional optimization problems , 2012, Eur. J. Oper. Res..

[25]  Janez Brest,et al.  Performance comparison of self-adaptive and adaptive differential evolution algorithms , 2007, Soft Comput..

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

[27]  Thomas Weise,et al.  Global Optimization Algorithms -- Theory and Application , 2009 .

[28]  Takeshi Furuhashi,et al.  Fuzzy system parameters discovery by bacterial evolutionary algorithm , 1999, IEEE Trans. Fuzzy Syst..