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Expected improvement of constraint violation for expensive constrained optimization

Published: 02 July 2018 Publication History

Abstract

For computationally expensive constrained optimization problems, one crucial issue is that the existing expected improvement (EI) criteria are no longer applicable when a feasible point is not initially provided. To address this challenge, this paper uses the expected improvement of constraint violation to reach feasible region. A new constrained expected improvement criterion is proposed to select sample solutions for the update of Gaussian process (GP) surrogate models. The validity of the proposed constrained expected improvement criterion is proved theoretically. It is also verified by experimental studies and results show that it performs better than or competitive to compared criteria.

References

[1]
S. Bagheri, W. Konen, R. Allmendinger, J. Branke, K. Deb, J. Fieldsend, D. Quagliarella, and K. Sindhya. 2017. Constraint handling in efficient global optimization. In Proc. Int. Conf. Genet. Evol. Comput. ACM, Berlin, Germany, 673--680.
[2]
R. Cheng, Y. Jin, K. Narukawa, and B. Sendhoff. 2015. A Multi-objective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling. IEEE Trans. Evol. Comput. 19, 6 (2015), 838--856.
[3]
T. Chugh, Y. Jin, K. Miettinen, J. Hakanen, and K. Sindhya. 2018. A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Trans. Evol. Comput. 22, 1 (2018), 129--142.
[4]
T. Chugh, K. Sindhya, J. Hakanen, and K. Miettinen. 2017. A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms. Soft Computing (2017), 1--30.
[5]
C. Durantin, J. Marzat, and M. Balesdent. 2016. Analysis of multi-objective Kriging-based methods for constrained global optimization. Comput. Optim. Appl. 63, 3 (2016), 903--926.
[6]
M TM Emmerich, K C Giannakoglou, and B. Naujoks. 2006. Single-and multiobjective evolutionary optimization assisted by gaussian random field metamodels. IEEE Trans. Evol. Comput. 10, 4 (2006), 421--439.
[7]
A. Habib, H K. Singh, and T. Ray. 2016. A study on the effectiveness of constraint handling schemes within Efficient Global Optimization framework. In Proc. IEEE Symp. Ser. Comput. Intell. Athens, Greece, 1--8.
[8]
R. Hussein and K. Deb. 2016. A Generative Kriging Surrogate Model for Constrained and Unconstrained Multi-objective Optimization. In Proc. GECCO. New York, USA, 573--580.
[9]
D R Jones, M Schonlau, and W J Welch. 1998. Efficient global optimization of expensive black-box functions. J. of Global Optim. 13, 4 (1998), 455--492.
[10]
J J Liang, T P Runarsson, E Mezura-Montes, M Clerc, PN Suganthan, CA C Coello, and K Deb. 2006. Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. J. Appl. Mechan. 41, 8 (2006).
[11]
N. Namura, K. Shimoyama, and S. Obayashi. 2017. Expected Improvement of Penalty-based Boundary Intersection for Expensive Multiobjective Optimization. IEEE Trans. Evol. Comput. 21, 6 (2017), 898--913.
[12]
M. Schonlau, W J. Welch, and D R. Jones. 1998. Global versus local search in constrained optimization of computer models. Lecture Notes-Monograph Series (1998), 11--25.
[13]
M. Stein. 1987. Large sample properties of simulations using Latin hypercube sampling. Techn. 29, 2 (1987), 143--151.
[14]
R. Storn and K V. Price. 1997. Differential Evolution A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. of Global Optim. 11, 4 (1997), 341--359.
[15]
S. Zeng, R. Jiao, C. Li, B. Lan, H. Li, J. Sun, and Y. Sun. 2017. Typical constrained optimization formulation in evolutionary computation not suitable for expensive optimization. In Proc. Computational Intelligence and Intelligent Systems - 9th International Symposium, ISICA 2017. Guangzhou, China, 1--14.
[16]
S. Zeng, R. Jiao, C. Li, X. Li, and J S. Alkasassbeh. 2017. A General Framework of Dynamic Constrained Multiobjective Evolutionary Algorithms for Constrained Optimization. IEEE Trans. Cybern. 47, 9 (2017), 2678--2688.
[17]
Q. Zhang, W. Liu, E. Tsang, and B. Virginas. 2010. Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Trans. Evol. Comput. 14, 3 (2010), 456--474.

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  • (2023)Expensive Optimization via Surrogate-Assisted and Model-Free Evolutionary OptimizationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2022.321908053:5(2758-2769)Online publication date: May-2023
  • (2022)Investigating the Correlation Amongst the Objective and Constraints in Gaussian Process-Assisted Highly Constrained Expensive OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.312098026:5(872-885)Online publication date: 1-Oct-2022
  • (2021)Multiple Penalties and Multiple Local Surrogates for Expensive Constrained OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.306660625:4(769-778)Online publication date: 1-Aug-2021
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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
July 2018
1578 pages
ISBN:9781450356183
DOI:10.1145/3205455
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 July 2018

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Author Tags

  1. constrained optimization
  2. evolutionary computation
  3. expected improvement
  4. expensive optimization
  5. gaussian process

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Cited By

View all
  • (2023)Expensive Optimization via Surrogate-Assisted and Model-Free Evolutionary OptimizationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2022.321908053:5(2758-2769)Online publication date: May-2023
  • (2022)Investigating the Correlation Amongst the Objective and Constraints in Gaussian Process-Assisted Highly Constrained Expensive OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.312098026:5(872-885)Online publication date: 1-Oct-2022
  • (2021)Multiple Penalties and Multiple Local Surrogates for Expensive Constrained OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.306660625:4(769-778)Online publication date: 1-Aug-2021
  • (2021)Derivative‐Free Optimization of a Fixed‐Bed Methanol Synthesis ReactorChemical Engineering & Technology10.1002/ceat.20210016544:10(1830-1839)Online publication date: 21-Aug-2021

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