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JACIII Vol.18 No.2 pp. 221-231
doi: 10.20965/jaciii.2014.p0221
(2014)

Paper:

Archive of Useful Solutions for Directed Mating in Evolutionary Constrained Multiobjective Optimization

Minami Miyakawa, Keiki Takadama, and Hiroyuki Sato

The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

Received:
October 15, 2013
Accepted:
January 31, 2014
Published:
March 20, 2014
Keywords:
evolutionary multi-objective optimization, constraint-handling, archive of solutions, directed mating
Abstract
As an evolutionary approach to solve multi-objective optimization problems involving several constraints, recently a multi-objective evolutionary algorithm (MOEA) using two-stage non-dominated sorting and directed mating (TNSDM) has been proposed. In TNSDM, directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. In our previous studies, significant contribution of directed mating to the improvement of the search performancewas verified on several benchmark problems. However, in the conventional TNSDM, infeasible solutions utilized in directed mating are discarded in the selection process of parents (elites) population and cannot be utilized in the next generation. TNSDM has potential to further improve the search performance by archiving useful solutions for directed mating to the next generation and repeatedly utilizing them in directed mating. To further improve effects of directed mating in TNSDM, in this work, we propose an archiving strategy of useful solutions for directed mating. We verify the search performance of TNSDM using the proposed archive by varying the size of archive, and compare its search performance with the conventional CNSGA-II and RTS on m objectives k knapsacks problems. As results, we show that the search performance of TNSDM is improved by introducing the proposed archive in aspects of diversity of the obtained solutions in the objective space and convergence of solutions toward the optimal Pareto front.
Cite this article as:
M. Miyakawa, K. Takadama, and H. Sato, “Archive of Useful Solutions for Directed Mating in Evolutionary Constrained Multiobjective Optimization,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.2, pp. 221-231, 2014.
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References
  1. [1] K. Deb, “Multi-Objective Optimization using Evolutionary Algorithms,” John Wiley & Sons, 2001.
  2. [2] E. Mezura-Montes, “Constraint-Handling in Evolutionary Optimization,” Springer, 2009.
  3. [3] F. Hoffmeister and J. Sprave, “Problem-independent handling of constraints by use of metric penalty functions,” Proc. of the 5th Annual Conf. on Evolutionary Programming (EP 1996), pp. 289-294, 1996.
  4. [4] T. Bäck, F. Hoffmeister, and H. Schwefel, “A Survey of Evolution Strategies,” Proc. of the 4th Int. Conf. on Genetic Algorithms, pp. 2-9, 1991.
  5. [5] C. A. C. Coello and A. D. Christiansen, “MOSES: AMultiobjective Optimization Tool for Engineering Design,” Engineering Optimization, Vol.31, No.3, pp. 337-368, 1999.
  6. [6] E. Zitzler and L. Thiele, “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach,” IEEE Trans. on Evolutionary Computation, Vol.3, No.4, pp. 257-271, 1999.
  7. [7] H. Ishibuchi and S. Kaige, “Effects of Repair Procedures on the Performance of EMO Algorithms for Multiobjective 0/1 Knapsack Problems,” Proc. of the 2003 Congress on Evolutionary Computation (CEC’2003), Vol.4, pp. 2254-2261, 2003.
  8. [8] A. Homaifar, S. H. Y. Lai, and X. Qi, “Constrained Optimization via Genetic Algorithms,” Trans. of The Society for Modeling and Simulation Int. – SIMULATION, Vol.62, No.4, pp. 242-254, 1994.
  9. [9] J. Joines and C. Houck, “On the Use of Non-Stationary Penalty Functions to Solve Nonlinear Constrained Optimization Problems with Gas,” Proc. of the First IEEE Conf. on Evolutionary Computation, pp. 579-584, 1994.
  10. [10] R. Farmani and J. A. Wright, “Self-Adaptive Fitness Formulation for Constrained Optimization,” IEEE Trans. on Evolutionary Computation, Vol.7, No.5, pp. 445-455, 2003.
  11. [11] K. Deb, “Evolutionary Algorithms forMulti-Criterion Optimization in Engineering Design,” Evolutionary Algorithms in Engineering and Computer Science, Chapter 8, pp. 135-161, JohnWiley & Sons, 1999.
  12. [12] J. Hazra and A. K. Sinha, “A multi-objective optimal power flow using particle swarm optimization,” European Trans. on Electrical Power, Vol.21, Issue 1, pp. 1028-1045, 2011.
  13. [13] Y. G.Woldesenbet, G. G. Yen, and B. G. Tessema, “Constraint Handling in Multiobjective Evolutionary Optimization,” IEEE Trans. on Evolutionary Computation, Vol.13, Issue 3, pp. 514-525, 2009.
  14. [14] E. Mezura-Montes and C. A. C. Coello, “Constrained Optimization via Multiobjective Evolutionary Algorithms,” Multiobjective Problem Solving from Nature, Part I, Springer, pp. 53-75, 2008.
  15. [15] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II,” IEEE Trans. on Evolutionary Computation, Vol.6, pp. 182-197, 2002.
  16. [16] T. Ray, K. Tai, and C. Seow, “An evolutionary algorithm for multiobjective optimization,” Eng. Optim., Vol.33, No.3, pp. 399-424, 2001.
  17. [17] M. Miyakawa and H. Sato, “An Evolutionary Algorithm using Twostage Non-dominated Sorting and Directed Mating for Constrained Multi-objective Optimization,” Proc. of the 6th Int. Conf. on Soft Computing and Intelligent Systems and the 13th Int. Symposium on Advanced Intelligent Systems (SCIS-ISIS2012), pp. 1441-1446, 2012.
  18. [18] M.Miyakawa and H. Sato, “Constrained Multi-Objective Optimization Using Two-Stage Non-Dominated Sorting and Directed Mating,” Trans. of the Japanese Society for Evolutionary Computation, Vol.3, No.3, pp. 185-196, 2012 (Japanese).
  19. [19] M. Miyakawa, K. Takadama, and H. Sato, “Two-Stage Non-Dominated Sorting and Directed Mating for Solving Problems with Multi-Objectives and Constraints,” Proc. of 2013 Genetic and Evolutionary Computation Conference (GECCO 2013), pp. 647-654, 2013.
  20. [20] H. Kellerer, U. Pferschy, and D. Pisinger, “Knapsack Problems,” Springer, 2004.
  21. [21] S. Kukkonen and J. Lampinen, “Constrained Real-Parameter Optimization with Generalized Differential Evolution,” Proc. of 2006 IEEE Congress on Evolutionary Computation (CEC2006), pp. 911-918, 2006.
  22. [22] E. Zitzler, “Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications,” Ph.D. thesis, Swiss Federal Institute of Technology, Zurich, 1999.
  23. [23] J. Knowles and D. Corne, “On Metrics for Comparing Nondominated Sets,” Proc. 2002 IEEE Congress on Evolutionary Computation, pp. 711-716, 2002.
  24. [24] T. T. Binh and U. Korn, “MOBES: A multiobjective evolution strategy for constrained optimization problems,” Proc. of the 3rd Int. Conf. on Genetic Algorithms (Mendel 97), pp. 176-182, 1997.
  25. [25] A. Osyczka and S. Kundu, “A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm,” Structural Optimization, Vol.10, No.2, pp. 94-99, 1995.
  26. [26] M. Tanaka, H. Watanabe, Y. Furukawa, and T. Tanino, “GA-Based Decision Support System for Multicriteria Optimization,” Proc of the Int. Conf. on Systems, Man, and Cybernetics, Vol.2, pp. 1556-1561, 1995.

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