A Differential Evolution Algorithm with Constraint Sequencing

The presence of constraints is known to affect the performance of all optimization algorithms. This paper proposes a novel approach of constraint handling, wherein for every solution, the evaluation of the constraints follow a predefined sequence. Furthermore, for any solution, such an evaluation is aborted whenever any constraint in the sequence is violated. The benefits of such a sequencing approach is illustrated using an example before comparing its performance with three existing state of the art differential evolution algorithms on 11 commonly studied constrained optimization benchmark problems. The results clearly highlight that the proposed algorithm has a faster rate of convergence and a better performance over existing state of the art DEs.

[1]  Angel Eduardo Muñoz Zavala,et al.  Continuous Constrained Optimization with Dynamic Tolerance Using the COPSO Algorithm , 2009 .

[2]  Tetsuyuki Takahama,et al.  Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation , 2010, IEEE Congress on Evolutionary Computation.

[3]  P. N. Suganthan,et al.  Ensemble of Constraint Handling Techniques , 2010, IEEE Transactions on Evolutionary Computation.

[4]  Amy FitzGerald,et al.  Genetic Repair for Optimization under Constraints Inspired by Arabidopsis Thaliana , 2008, PPSN.

[5]  P. Suganthan,et al.  Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods , 2011 .

[6]  Isao Ono,et al.  Constraint-Handling Method for Multi-objective Function Optimization: Pareto Descent Repair Operator , 2007, EMO.

[7]  Alice E. Smith,et al.  Penalty guided genetic search for reliability design optimization , 1996 .

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

[9]  Janez Brest,et al.  Constrained Real-Parameter Optimization with ε -Self-Adaptive Differential Evolution , 2009 .

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

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

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

[13]  Tapabrata Ray,et al.  An adaptive differential evolution algorithm and its performance on real world optimization problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[14]  Erik D. Goodman,et al.  SRDE: an improved differential evolution based on stochastic ranking , 2009, GEC '09.

[15]  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 .