An ensemble of differential evolution algorithms for constrained function optimization

This paper presents an ensemble of differential evolution algorithms employing the variable parameter search and two distinct mutation strategies in the ensemble to solve real-parameter constrained optimization problems. It is well known that the performance of DE is sensitive to the choice of mutation strategies and associated control parameters. For these reasons, the ensemble is achieved in such a way that each individual is assigned to one of the two distinct mutation strategies or a variable parameter search (VPS). The algorithm was tested using benchmark instances in Congress on Evolutionary Computation 2010. For these benchmark problems, the problem definition file, codes and evaluation criteria are available in http://www.ntu.edu.sg/home/EPNSugan. Since the optimal or best known solutions are not available in the literature, the detailed computational results required in line with the special session format are provided for the competition.

[1]  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).

[2]  Feng-Sheng Wang,et al.  Hybrid differential evolution with multiplier updating method for nonlinear constrained optimization problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Tetsuyuki Takahama,et al.  Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  Mehmet Fatih Tasgetiren,et al.  A Harmony Search Algorithm with Ensemble of Parameter Sets , 2009, 2009 IEEE Congress on Evolutionary Computation.

[5]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

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

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

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

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

[10]  Feng-Sheng Wang,et al.  Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to a fed-batch fermentation process , 1999 .

[11]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[12]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

[13]  Haralambos Sarimveis,et al.  A line up evolutionary algorithm for solving nonlinear constrained optimization problems , 2005, Comput. Oper. Res..

[14]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

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

[16]  Mehmet Fatih Tasgetiren,et al.  A differential evolution algorithm with variable parameter search for real-parameter continuous function optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[17]  Alice E. Smith,et al.  Genetic Optimization Using A Penalty Function , 1993, ICGA.

[18]  Michael M. Skolnick,et al.  Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints , 1993, ICGA.

[19]  Mehmet Fatih Tasgetiren,et al.  An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem , 2010, Appl. Math. Comput..

[20]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .