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An efficient constraint handling approach for optimization problems with limited feasibility and computationally expensive constraint evaluations

Published: 06 July 2013 Publication History

Abstract

Existing optimization approaches adopt a full evaluation policy, i.e. all the constraints corresponding to a solution are evaluated throughout the course of search. Furthermore, a common sequence of constraint evaluation is used for all the solutions. In this paper, we introduce a scheme of constraint handling, wherein every solution is assigned a random sequence of constraints and the evaluation process is aborted whenever a constraint is violated. The solutions are sorted based on two measures i.e. the number of satisfied constraints and the violation measure. The number of satisfied constraints takes a precedence over the amount of violation. We illustrate the performance of the proposed scheme and compare it with other state-of-the-art constraint handling methods within a framework of differential evolution. The results are compared using gseries test functions for inequality constraints. The results clearly highlight the potential savings offered by the proposed method

References

[1]
M. Schoenauer and S. Xanthakis, "Constrained GA optimization," in Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA-93), S. Forrest, Ed., University of Illinois at Urbana-Champaign. San Mateo, California: Morgan Kauffman Publishers, July 1993, pp. 573--580.
[2]
C. A. C. Coello, "Theoretical and numerical constraint handling techniques used with evolutionary algorithms: A survey of the state of the art," Computer Methods in Applied Mechanics and Engineering, vol. 191, no. 11--12, pp. 1245--1287, January 2002.
[3]
R. Storn and K. Price, "Differential evolution -- a simple and efficient adaptive scheme for global optimization over continuous spaces," Technical report TR-95-012, International Computer Science Institute, Berkeley, CA, 1995.
[4]
A. Asafuddoula, T. Ray, and R. Sarker, "A self-adaptive differential evolution algorithm with constraint sequencing," n Proceedings AI 2012: Advances in Artificial Intelligence, vol. 7691 of Lecture Notes in Artificial Intelligence, pp. 182--193, 2012.
[5]
J. J. Liang, T. P. Runarsson, E. Mezura-Montes, M. Clerc, P. Suganthan, C. A. Coello, and K. Deb, "Problem definitions and evaluation criteria for the cec 2006 special session on constrained real-parameter optimization," Technical Report, Nanyang Technological University, Dec 2005.
[6]
T. P. Runarsson and X. Yao, "Stochastic ranking for constrained evolutionary optimization," IEEE Transactions on Evolutionary Computation, vol. 4, no. 3, pp. 284--294, September 2000.
[7]
B. Tessema and G. G. Yen, "A self adaptative penalty function based algorithm for constrained optimization," in 2006 IEEE Congress on Evolutionary Computation (CEC'2006). Vancouver, BC, Canada: IEEE Press, July 2006, pp. 950--957.
[8]
K. Deb, "An efficient constraint handling method for genetic algorithms," Computer Methods in Applied Mechanics and Engineering, vol. 186, no. 2/4, pp. 311--338, 2000.
[9]
T. Takahama and S. Sakai, "Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation," in 2010 IEEE Congress on Evolutionary Computation (CEC'2010). Barcelona, Spain: IEEE Press, July 18--23 2010, pp. 1680--1688.
[10]
E. D. Dolan and J. J. More, "Benchmarking optimization software with performance profiles," Mathematical Programming, vol. 91, pp. 201--213, 2002.

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    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 06 July 2013

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    1. constraint handling
    2. constraint sequencing
    3. fitness evaluation

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    GECCO '13
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    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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