Parameter adaptation for differential evolution with design of experiments

Optimal settings for control parameters of the Differential Evolution algorithm depend on the considered optimization problem and may also change during an optimization run. In this work an approach is suggested that adaptively controls the parameters F and CR that influence the mutation and recombination processes in Differential Evolution. By application of Design of Experiments methods significant differences in performance due to different parameter settings can be detected during an optimization run. Additionally, interaction effects of the parameters are discovered. By changing the parameter settings on the basis of these results, feedback from the current state of the optimization run is taken into account. The method is tested using a constrained single-objective optimization problem. A comparison with another study using the same problem with tuned fixed parameter values shows promising results.

[1]  Ren-Jye Yang,et al.  Approximation methods in multidisciplinary analysis and optimization: a panel discussion , 2004 .

[2]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[3]  Karl-Dirk Kammeyer,et al.  Parameter Study for Differential Evolution Using a Power Allocation Problem Including Interference Cancellation , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  Karl-Dirk Kammeyer,et al.  Analysis of Iterative Successive Interference Cancellation in SC-CDMA Systems , 2006 .

[5]  Kok Wai Wong,et al.  Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems , 2005 .

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

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

[8]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[9]  Kenneth V. Price,et al.  An introduction to differential evolution , 1999 .

[10]  Yaochu Jin,et al.  A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..

[11]  R. Storn,et al.  Differential Evolution , 2004 .

[12]  Jack P. C. Kleijnen,et al.  Design and Analysis of Monte Carlo Experiments , 2004 .

[13]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

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

[15]  Godfrey C. Onwubolu,et al.  Optimizing CNC Drilling Machine Operations: Traveling Salesman Problem-Differential Evolution Approach , 2004 .