Actor-Critic–Like Stochastic Adaptive Search for Continuous Simulation Optimization

Published Online:https://doi.org/10.1287/opre.2021.2214

References

  • Andradóttir S, Prudius AA (2009) Balanced explorative and exploitative search with estimation for simulation optimization. INFORMS J. Comput. 21(2):193–208.LinkGoogle Scholar
  • Andradóttir S, Prudius AA (2010) Adaptive random search for continuous simulation optimization. Naval Res. Logist. 57(6):583–604.CrossrefGoogle Scholar
  • Ankenman B, Nelson BL, Staum J (2010) Stochastic kriging for simulation metamodeling. Oper. Res. 58(2):371–382.LinkGoogle Scholar
  • Ball R, Branke J, Meisel S (2018) Optimal sampling for simulated annealing under noise. INFORMS J. Comput. 30(1):200–215.LinkGoogle Scholar
  • Barton RR (2009) Simulation optimization using metamodels. Proc. 2009 Winter Simulation Conf. (IEEE, New York), 230–238.Google Scholar
  • Barton RR, Meckesheimer M (2006) Metamodel-based simulation optimization. Handbook Oper. Res. Management Sci. 13:535–574.Google Scholar
  • Baumert S, Smith RL (2002) Pure random search for noisy objective functions. Technical report 01-03, University of Michigan, Ann Arbor.Google Scholar
  • Bertsekas DP, Tsitsiklis JN (1996) Neuro-Dynamic Programming (Athena Scientific, Belmont, MA).Google Scholar
  • Bishop CM (1995) Neural Networks for Pattern Recognition (Oxford University Press, New York).CrossrefGoogle Scholar
  • Das S, Suganthan PN (2010) Problem definitions and evaluation criteria for cec 2011 competition on testing evolutionary algorithms on real world optimization problems. Technical report, Jadavpur University, Kolkata, India and Nanyang Technological University, Singapore.Google Scholar
  • Dorigo M, Blum C (2005) Ant colony optimization theory: A survey. Theoretical Comput. Sci. 344(2-3):243–278.CrossrefGoogle Scholar
  • Fan Q, Hu J (2018) Surrogate-based promising area search for Lipschitz continuous simulation optimization. INFORMS J. Comput. 30(4):677–693.LinkGoogle Scholar
  • Glasserman P (2003) Monte Carlo Methods in Financial Engineering (Springer Science & Business Media, New York).CrossrefGoogle Scholar
  • Gosavi A (2009) Reinforcement learning: A tutorial survey and recent advances. INFORMS J. Comput. 21(2):178–192.LinkGoogle Scholar
  • Gutmann HM (2001) A radial basis function method for global optimization. J. Global Optim. 19:201–227.CrossrefGoogle Scholar
  • Hajek B (1998) Cooling schedules for optimal annealing. Math. Oper. Res. 13(2):311–329.LinkGoogle Scholar
  • Hansen N, Ostermeier A (1996) Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. Proc IEEE Internat. Conf. on Evolutionary Comput. (IEEE, New York), 312–317.Google Scholar
  • He D, Lee LH, Chen CH, Fu MC, Wasserkrug S (2010) Simulation optimization using the cross-entropy method with optimal computing budget allocation. ACM Trans. Modeling Comput. Simulation 20(1):1–22.CrossrefGoogle Scholar
  • Hong LJ, Nelson BL (2006) Discrete optimization via simulation using compass. Oper. Res. 54(1):115–129.LinkGoogle Scholar
  • Hu J (2015) Model-based stochastic search methods. Fu MC, ed., Handbook of Simulation Optimization (Springer, New York), 319–340.CrossrefGoogle Scholar
  • Hu J, Hu P (2011) Annealing adaptive search, cross-entropy, and stochastic approximation in global optimization. Naval Res. Logist. 58(5):457–477.CrossrefGoogle Scholar
  • Hu J, Zhou E, Fan Q (2014) Model-based annealing random search with stochastic averaging. ACM Trans. Modeling Comput. Simulation 24(4):1–23.CrossrefGoogle Scholar
  • Hu J, Chang HS, Fu MC, Marcus SI (2011) Dynamic sample budget allocation in model-based optimization. J. Global Optim. 50:575–596.CrossrefGoogle Scholar
  • Huang D, Allen TT, Notz WI, Zeng N (2006) Global optimization of stochastic black-box systems via sequential kriging meta-models. J. Global Optim. 34(3):441–466.CrossrefGoogle Scholar
  • Jalali H, Nieuwenhuyse IV, Picheny V (2017) Comparison of kriging-based algorithms for simulation optimization with heterogeneous noise. Eur. J. Oper. Res. 261(1):279–301.CrossrefGoogle Scholar
  • Jones D, Schonlau M, Welch W (1998) Efficient global optimization of expensive black-box functions. J. Global Optim. 13:455–492.CrossrefGoogle Scholar
  • Kaelbing L, Littman M, Moore A (1996) Reinforcement learning: A survey. J. Artificial Intelligence Res. 4:237–285.CrossrefGoogle Scholar
  • Kiatsupaibul S, Smith RL, Zabinsky ZB (2018) Single observation adaptive search for continuous simulation optimization. Oper. Res. 66(6):1713–1727.LinkGoogle Scholar
  • Kiefer J, Wolfowitz J (1952) Stochastic estimation of the maximum of a regression function. Ann. Math. Statist. 23(3):462–466.CrossrefGoogle Scholar
  • Kim S, Pasupathy R, Henderson SG (2015) A guide to sample average approximiation. Fu MC, ed. Handbook of Simulation Optimization (Springer, New York), 207–243.CrossrefGoogle Scholar
  • Kleijnen JPC (2014) Simulation-optimization via kriging and bootstrapping: a survey. J. Simul. 8(4):241–250.CrossrefGoogle Scholar
  • Kleijnen JPC (2015) Response surface methodology. Fu MC, ed. Handbook of Simulation Optimization (Springer, New York), 81–104.CrossrefGoogle Scholar
  • Kleywegt A, Shapiro A, Homem-De-Mello T (2001) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2):479–502.CrossrefGoogle Scholar
  • Konda VR, Borkar VS (1999) Actor-critic-type learning algorithms for Markov decision processes. SIAM J. Control Optim. 38(1):94–123.CrossrefGoogle Scholar
  • Konda VR, Tsitsiklis JN (2003) On actor-critic algorithms. SIAM J. Control Optim. 42(4):1143–1166.CrossrefGoogle Scholar
  • Kushner HJ, Clark DS (1978) Stochastic Approximation Methods for Constrained and Unconstrained Systems (Springer-Verlag, New York).CrossrefGoogle Scholar
  • Larrãnaga P, Lozano J, eds. (2002) Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. (Kluwer Academic Publisher, Boston, MA).CrossrefGoogle Scholar
  • Loeppky JL, Sacks J, Welch WJ (2009) Choosing the sample size of a computer experiment: A practical guide. Technometrics 51(4):366–376.CrossrefGoogle Scholar
  • Picheny V, Wagner T, Ginsbourger D (2013) A benchmark of kriging-based infill criteria for noisy optimization. Structural Multidisciplinary Optim. 48(3):607–626.CrossrefGoogle Scholar
  • Prudius A, Andradóttir S (2012) Averaging frameworks for simulation optimization with applications to simulated annealing. Naval Res. Logist. 59(6):411–429.CrossrefGoogle Scholar
  • Robbins H, Monro S (1951) A stochastic approximation method. Ann. Math. Statist. 22(3):400–407.CrossrefGoogle Scholar
  • Rubinstein RY, Kroese DP (2004) The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation (Information Science and Statistics) (Springer-Verlag, New York).CrossrefGoogle Scholar
  • Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Statist. Sci. 4(4):409–423.CrossrefGoogle Scholar
  • Shi L, Ólafsson S (2000) Nested partitions method for stochastic optimization. Methodology Comput. Appl. Probability 2(3):271–291.CrossrefGoogle Scholar
  • Spall JC (1992) Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans. Automat. Control 37(3):332–341.CrossrefGoogle Scholar
  • Spall JC (2003) Introduction to Stochastic Search and Optimization (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • Staum J (2009) Better simulation metamodeling: The why, what, and how of stochastic kriging. Proc. Winter Simulation Conf. (IEEE, New York), 119–133.Google Scholar
  • Sutton RS, McAllester D, Singh S, Mansour Y (2000) Policy Gradient Methods for Reinforcement Learning with Function Approximation, vol. 1057–1063. Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA).Google Scholar
  • Valdez S, Hernández A, Botello S (2013) A Boltzmann based estimation of distribution algorithm. Inform. Sci. 236(1):126–137.CrossrefGoogle Scholar
  • Wang H, Pasupathy R, Schmeiser BW (2013) Integer-ordered simulation optimization using r-spline: Retrospective search using piecewise-linear interpolation and neighborhood enumeration. ACM Trans. Modeling Comput. Simulation 23(3):1–24.CrossrefGoogle Scholar
  • Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learn. 8:229–256.CrossrefGoogle Scholar
  • Wolpert DH (2004) Finding bounded rational equilibria part I: Iterative focusing. Vincent T, ed. Proc. 11th Internat. Sympos. on Dynamic Games and Appl., Tucson, Arizona. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.154.5730&rep=rep1&type=pdf.Google Scholar
  • Xu J, Nelson BL, Hong LJ (2013) An adaptive hyperbox algorithm for high-dimensional discrete optimization via simulation problems. INFORMS J. Comput. 25(1):133–146.LinkGoogle Scholar
  • Yakowitz S, L’Ecuyer P, Vázquez-Abad F (2000) Global stochastic optimization with low-dispersion point sets. Oper. Res. 48(6):939–950.LinkGoogle Scholar
  • Yan D, Mukai H (1992) Stochastic discrete optimization. SIAM J. Control Optim. 30(3):594–612.CrossrefGoogle Scholar
  • Zhang Q, Hu J (2019) Enhancing random search with surrogate models for Lipschitz continuous optimization. Proc. IEEE 15th Internat. Conf. on Automation Sci. and Engrg. (IEEE, New York), 768–773.Google Scholar
  • Zlochin M, Birattari M, Meuleau N, Dorigo M (2004) Model-based search for combinatorial optimization: A critical survey. Ann. Oper. Res. 131(1):373–395.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.