Adaptive Differential Evolution with Difference Mean Based Perturbation for Practical Engineering Optimization Problems

Differential EvolutionDE is one of the most versatile evolutionary techniques that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Recent developments on DE includes self adaptation of its parameters F=step size and CR=cross-over probability making it a parameter free optimizer. A new self adaptive DEjDE proposed by Janez Brest, is a robust improvement of DE, where the self adaptive parameters undergo similar operations of genetic operators. This paper aims at introducing a unique mutation strategy by modifying the existing "DE/rand/1/bin" strategy of jDE with Difference Mean Based Perturbation DMP technique. The algorithm addressed as ADE-DMP is basically a variant of jDE, but the modified mutation scheme ensues within the algorithm effective search of area near the current best that effectively proves it to be a better and fast optimizer in complex real world problems of diverse domains.

[1]  Tapabrata Ray,et al.  How does the good old Genetic Algorithm fare at real world optimization? , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[2]  Ruhul A. Sarker,et al.  GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[3]  Bin Li,et al.  Estimation of distribution and differential evolution cooperation for real-world numerical optimization problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[4]  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).

[5]  Jurij Silc,et al.  The Continuous Differential Ant-Stigmergy Algorithm applied to real-world optimization problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

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

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

[8]  Tapabrata Ray,et al.  Performance of a hybrid EA-DE-memetic algorithm on CEC 2011 real world optimization problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[9]  Jacek M. Zurada,et al.  Swarm and Evolutionary Computation , 2012, Lecture Notes in Computer Science.

[10]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[11]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

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

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

[14]  Ajith Abraham,et al.  Self adaptive cluster based and weed inspired differential evolution algorithm for real world optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[15]  Tilo Strutz,et al.  Data Fitting and Uncertainty: A practical introduction to weighted least squares and beyond , 2010 .

[16]  Ponnuthurai N. Suganthan,et al.  Modified differential evolution with local search algorithm for real world optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[17]  Antonio LaTorre,et al.  Benchmarking a hybrid DE-RHC algorithm on real world problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[18]  Ruhul A. Sarker,et al.  Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[19]  Tilo Strutz,et al.  Data Fitting and Uncertainty , 2011 .