Culturizing differential evolution for constrained optimization

We propose the use of differential evolution as a population space of a cultural algorithm, applied to the solution of constrained optimization problems. Differential evolution is a relatively recent evolutionary algorithm that has been found to be very robust as a search engine for real parameter optimization. Adding different knowledge sources to the variation operator of differential evolution it is possible to improve the search and reduce the computational cost necessary to approximate the global optima of different problems. The proposed technique is validated using a set of well-known constrained optimization problems commonly adopted in the specialized literature. The approach is compared with respect to two techniques that are representative of the state-of-the-art in the area.

[1]  Robert G. Reynolds,et al.  A Testbed for Solving Optimization Problems Using Cultural Algorithms , 1996, Evolutionary Programming.

[2]  Marcel Bergerman,et al.  Cultural algorithms: concepts and experiments , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[3]  Carlos A. Coello Coello,et al.  Adding Knowledge And Efficient Data Structures To Evolutionary Programming: A Cultural Algorithm For Constrained Optimization , 2002, GECCO.

[4]  Lawrence J. Fogel,et al.  Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming , 1999 .

[5]  Robert G. Reynolds,et al.  Cultural algorithms: theory and applications , 1999 .

[6]  G. Swaminathan Robot Motion Planning , 2006 .

[7]  Carlos A. Coello Coello,et al.  Evolutionary multiobjective optimization using a cultural algorithm , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[8]  Robert G. Reynolds,et al.  Cultural swarms: assessing the impact of culture on social interaction and problem solving , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[9]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[10]  Michael Luck,et al.  Proceedings of the Third Mexican International Conference on Computer Science , 2001 .

[11]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization , 1999, Evolutionary Computation.

[12]  Zbigniew Michalewicz,et al.  Using Cultural Algorithms for Constraint Handling in GENOCOP , 1995, Evolutionary Programming.

[13]  Robert G. Reynolds,et al.  Knowledge-based solution to dynamic optimization problems using cultural algorithms , 2001 .

[14]  Carlos A. Coello Coello,et al.  Simple Feasibility Rules and Differential Evolution for Constrained Optimization , 2004, MICAI.

[15]  Robert G. Reynolds,et al.  Cultural swarms: modeling the impact of culture on social interaction and problem solving , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[16]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[17]  Robert G. Reynolds,et al.  Cultural algorithms in dynamic environments , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

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

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

[20]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[21]  Russell C. Eberhart,et al.  The particle swarm: social adaptation in information-processing systems , 1999 .

[22]  Jon Louis Bentley,et al.  Data Structures for Range Searching , 1979, CSUR.

[23]  R. Reynolds,et al.  Using knowledge-based evolutionary computation to solve nonlinear constraint optimization problems: a cultural algorithm approach , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).