A Matrix Adaptation Evolution Strategy for Constrained Real-Parameter Optimization

By combination of successful constraint handling techniques known within the context of Differential Evolution with the recently suggested Matrix Adaptation Evolution Strategy (MA-ES), a new Evolution Strategy for constrained optimization is presented. The novel MA - ES variant is applied to the benchmark problems specified for the CEC 2018 competition on constrained single objective real-parameter optimization. The algorithm is able to find feasible solutions on more than 80 % of the benchmark problems with high accuracy.

[1]  Radka Polakova,et al.  L-SHADE with competing strategies applied to constrained optimization , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[2]  P. Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2010 Competition on Constrained Real- Parameter Optimization , 2010 .

[3]  A. Kai Qin,et al.  Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  Bernhard Sendhoff,et al.  Covariance Matrix Adaptation Revisited - The CMSA Evolution Strategy - , 2008, PPSN.

[5]  Efrén Mezura-Montes,et al.  Differential evolution in constrained numerical optimization: An empirical study , 2010, Inf. Sci..

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

[7]  Hans-Georg Beyer,et al.  On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint , 2012, IEEE Transactions on Evolutionary Computation.

[8]  Nikolaus Hansen,et al.  Covariance Matrix Adaptation Evolution Strategy for Multidisciplinary Optimization of Expendable Launcher Family , 2010 .

[9]  Fang Liu,et al.  A novel selection evolutionary strategy for constrained optimization , 2013, Inf. Sci..

[10]  Ponnuthurai N. Suganthan,et al.  Differential evolution with ensemble of constraint handling techniques for solving CEC 2010 benchmark problems , 2010, IEEE Congress on Evolutionary Computation.

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

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

[13]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[14]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[15]  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.

[16]  Anne Auger,et al.  Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling , 2017, FOGA '17.

[17]  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.

[18]  Dirk V. Arnold,et al.  Reconsidering constraint release for active-set evolution strategies , 2017, GECCO.

[19]  Bernhard Sendhoff,et al.  Simplify Your Covariance Matrix Adaptation Evolution Strategy , 2017, IEEE Transactions on Evolutionary Computation.

[20]  Ruhul A. Sarker,et al.  Adaptive Configuration of evolutionary algorithms for constrained optimization , 2013, Appl. Math. Comput..