Ecology-inspired evolutionary algorithm using feasibility-based grouping for constrained optimization

When evolutionary algorithms are used for solving numerical constrained optimization problems, how to deal with the relationship between feasible and infeasible individuals can directly influence the final results. This paper proposes a novel ecology-inspired EA to balance the relationship between feasible and infeasible individuals. According to the feasibility of the individuals, the population is divided into two groups, feasible group and infeasible group. The evaluation and ranking of these two groups are performed separately. The number of parents from feasible group has a sigmoid relation with the number of feasible individuals, which is inspired by the ecological population growth in a confined space. The proposed method is tested using (/spl mu/, /spl lambda/) evolution strategies with 13 benchmark problems. Experimental results show that the proposed method is capable of improving performance of the dynamic penalty method for constrained optimization problems.

[1]  Marc Schoenauer,et al.  ASCHEA: new results using adaptive segregational constraint handling , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[2]  Andrew S. Ball,et al.  Instant Notes Ecology , 1998 .

[3]  Christopher R. Houck,et al.  On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[4]  Sana Ben Hamida,et al.  The need for improving the exploration operators for constrained optimization problems , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[5]  Z. Michalewicz,et al.  Your brains and my beauty: parent matching for constrained optimisation , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[6]  Z. Michalewicz,et al.  Genocop III: a co-evolutionary algorithm for numerical optimization problems with nonlinear constraints , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[7]  Keigo Watanabe,et al.  Evolutionary Optimization of Constrained Problems , 2004 .

[8]  Frank Hoffmeister,et al.  Problem-Independent Handling of Constraints by Use of Metric Penalty Functions , 1996, Evolutionary Programming.

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

[10]  Ming Yuchi,et al.  A grouping-based evolutionary algorithm for constrained optimization problem , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[11]  Jong-Hwan Kim.  Grouping-Based Evolutionary Algorithm Improves The Performance of Dynamic Penalty Method for Constrained Optimization Problems , 2004 .

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

[13]  Zbigniew Michalewicz,et al.  Evolutionary optimization of constrained problems , 1994 .

[14]  Ming Yuchi,et al.  Grouping-based evolutionary algorithm: seeking balance between feasible and infeasible individuals of constrained optimization problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).