Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces

A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means of an extensivetestbed it is demonstrated that the new methodconverges faster and with more certainty than manyother acclaimed global optimization methods. The newmethod requires few control variables, is robust, easyto use, and lends itself very well to parallelcomputation.

[1]  L. Goddard.  Operations Research , 1969, Nature.

[2]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[3]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[4]  C. K. Yuen,et al.  Theory and Application of Digital Signal Processing , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  A. Griewank.  Generalized descent for global optimization , 1981 .

[6]  A.L. Sangiovanni-Vincentelli,et al.  A survey of optimization techniques for integrated-circuit design , 1981, Proceedings of the IEEE.

[7]  R. Davison,et al.  Optimisation methods in Pascal , 1984 .

[8]  F. Aluffi-Pentini,et al.  Global optimization and stochastic differential equations , 1985 .

[9]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  David K. Smith.  Optimisation Methods in Pascal , 1988 .

[12]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[13]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[14]  E. Lüder,et al.  Optimization of circuits with a large number of parameters , 1990 .

[15]  Bruce E. Rosen,et al.  Genetic Algorithms and Very Fast Simulated Reannealing: A comparison , 1992 .

[16]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[17]  Lester Ingber,et al.  Simulated annealing: Practice versus theory , 1993 .

[18]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[19]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

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

[21]  Rainer Storn,et al.  Differential evolution design of an IIR-filter , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[22]  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.

[23]  R. Storn,et al.  On the usage of differential evolution for function optimization , 1996, Proceedings of North American Fuzzy Information Processing.