Constrained Single-Objective Optimization Using Differential Evolution

Differential evolution (DE) is a rather new evolutionary optimization algorithm that has been shown to be fast and simple for unconstrained single-objective optimization problems. In this work DE is employed for the constrained optimization test suite of the special session on constrained real parameter optimization at CEC06. Constraints are handled using a modified selection procedure that does not require additional parameters. For the control parameters of the DE algorithm the best found settings from another examination were used so that almost no parameter tuning was necessary. Most of the test functions are successfully and reliably optimized. Difficulties arise mainly from a high number of equality constraints.

[1]  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.

[2]  Saku Kukkonen,et al.  Real-parameter optimization with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[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]  Carlos A. Coello Coello,et al.  A simple multimembered evolution strategy to solve constrained optimization problems , 2005, IEEE Transactions on Evolutionary Computation.

[5]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[6]  R. Storn,et al.  Differential evolution a simple and efficient adaptive scheme for global optimization over continu , 1997 .

[7]  E. M. Montes.  Alternative techniques to handle constraints in evolutionary optimization , 2004 .

[8]  Godfrey C. Onwubolu,et al.  Differential Evolution for the Flow Shop Scheduling Problem , 2004 .

[9]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[10]  Carlos A. Coello Coello,et al.  What Makes a Constrained Problem Difficult to Solve by an Evolutionary Algorithm , 2004 .

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

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

[13]  Jing J. Liang,et al.  Problem Deflnitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization , 2006 .

[14]  Carlos Artemio Coello-Coello,et al.  Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art , 2002 .

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

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