IMPROVEMENTS TO MUTATION DONOR FORMULATION OF DIFFERENTIAL EVOLUTION

As one of the most promising novel evolutionary algorithms, differential evolution has been demonstrated to be an efficient, effective and robust optimization method for nonlinear optimization. Nevertheless, the convergence rate of differential evolution is still far from ideal when it is applied to optimizing a computationally expensive objective function that is fre- quently encountered in engineering optimization problems. This paper proposes three new schemes to determine the donor for mutation operation in differential evolution. These modi- fications to the mutation operator are analyzed and compared empirically by using a suite of artificial test functions. They are further examined with two practical neural-network-based aerodynamic data approximation cases. The simulation results demonstrate that the proposed strategies are capable of accelerating the convergence rate of the differential evolution algo- rithms.

[1]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[2]  K. V. Price,et al.  Differential evolution: a fast and simple numerical optimizer , 1996, Proceedings of North American Fuzzy Information Processing.

[3]  Ivan Zelinka,et al.  Mechanical engineering design optimization by differential evolution , 1999 .

[4]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[5]  V. Sherbaum,et al.  A Numerical Investigation of Mixing Processes in a Novel Combustor Application , 2005 .

[6]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[7]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

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

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

[10]  J. Lampinen.  A constraint handling approach for the differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  David B. Fogel,et al.  Evolutionary algorithms in theory and practice , 1997, Complex.

[12]  Ivan Zelinka,et al.  MIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 1: the optimization method , 2004 .

[13]  Ivan Zelinka,et al.  MIXED INTEGER-DISCRETE-CONTINUOUS OPTIMIZATION BY DIFFERENTIAL EVOLUTION Part 2 : a practical example , 1999 .