Abstract
The paper is devoted to consideration of multidimensional optimization problems with multiextremal objective functions over search domains determined by constraints, which form a special type of domain boundaries called computable ones, which, in general case, are non-linear and multiextremal. The regions of this class can be very complicated, in particular, non-convex, non-simply connected, and even disconnected. For solving such problems, a new global optimization technique based on the adaptive nested scheme developed recently for unconstrained optimization is proposed. The novelty consists in combination of the adaptive scheme with a technique for reducing the constraints to an explicit form of feasible subregions in internal subproblems of the nested scheme that allows one to evaluate the objective function at the feasible points only. For efficiency estimation of the proposed adaptive nested algorithm in comparison with the classical nested optimization and the penalty function method, a representative numerical experiment on the test classes of multidimensional multiextremal functions has been carried out. The results of the experiment demonstrate a significant advantage of the adaptive scheme over its competitors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bartholomew-Biggs, M.C., Parkhurst, S.C., Wilson, S.P.: Using DIRECT to solve an aircraft routing problem. Comput. Optim. Appl. 21(3), 311–323 (2002)
Boender, C.G.E., Rinnooy Kan, A.H.G.: Bayesian stopping rules for multistart global optimization methods. Math. Program. 37(1), 59–80 (1987)
Butz, A.R.: Space-filling curves and mathematical programming. Inf. Control 12, 314–330 (1968)
Carr, C.R., Howe, C.W.: Quantitative Decision Procedures in Management and Economic: Deterministic Theory and Applications. McGraw-Hill, New York (1964)
Dam, E.R., Husslage, B., Hertog, D.: One-dimensional nested maximin designs. J. Global Optim. 46, 287–306 (2010)
Famularo, D., Pugliese, P., Sergeyev, Y.D.: A global optimization technique for checking parametric robustness. Automatica 35, 1605–1611 (1999)
Fiacco, A.V., McCormick, G.P.: Nonlinear Programming: Sequential Unconstrained Minimization Techniques. Wiley, New York (1968)
Gaviano, M., Kvasov, D.E., Lera, D., Sergeyev, Y.D.: Software for generation of classes of test functions with known local and global minima for global optimization. ACM Trans. Math. Softw. 29(4), 469–480 (2003)
Gergel, V.P., Grishagin, V.A., Gergel, A.V.: Adaptive nested optimization scheme for multidimensional global search. J. Global Optim. 66, 35–51 (2016)
Gergel, V.P., Grishagin, V.A., Israfilov, R.A.: Local tuning in nested scheme of global optimization. Procedia Comput. Sci. 51, 865–874 (2015)
Gergel, V.P., Grishagin, V.A., Israfilov, R.A.: Adaptive dimensionality reduction in multiobjective optimization with multiextremal criteria. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds.) Machine Learning, Optimization, and Data Science. LNCS, vol. 11331, pp. 129–140. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13709-0_11
Gergel, V.P., Grishagin, V.A., Israfilov, R.A.: Parallel dimensionality reduction for multiextremal optimization problems. In: Malyshkin, V. (ed.) PaCT 2019. LNCS, vol. 11657, pp. 166–178. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25636-4_13
Gergel, V.P., Kuzmin, M.I., Solovyov, N.A., Grishagin, V.A.: Recognition of surface defects of cold-rolling sheets based on method of localities. Int. Rev. Autom. Control 8, 51–55 (2015)
Goertzel, B.: Global optimization with space-filling curves. Appl. Math. Lett. 12, 133–135 (1999)
Grishagin, V.A.: Operating characteristics of some global search algorithms. In: Problems of Stochastic Search, vol. 7, pp. 198–206. Zinatne, Riga (1978). (in Russian)
Grishagin, V.A., Israfilov, R.A.: Multidimensional constrained global optimization in domains with computable boundaries. In: CEUR Workshop Proceedings, vol. 1513, pp. 75–84 (2015)
Grishagin, V.A., Israfilov, R.A.: Global search acceleration in the nested optimization scheme. In: AIP Conference Proceedings, vol. 1738, p. 400010 (2016)
Grishagin, V.A., Israfilov, R.A., Sergeyev, Y.D.: Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes. Appl. Math. Comput. 318, 270–280 (2018)
Grishagin, V.A., Sergeyev, Y.D., Strongin, R.G.: Parallel characteristic algorithms for solving problems of global optimization. J. Global Optim. 10, 185–206 (1997)
Han, S.P., Mangasarian, O.L.: Exact penalty functions in nonlinear programming. Math. Program. 17(1), 251–269 (1979)
Jones, D.R.: The DIRECT global optimization algorithm. In: Floudas, C.A., Pardalos, P.M. (eds.) Encyclopedia of Optimization, pp. 431–440. Kluwer Academic Publishers, Dordrecht (2001)
Kvasov, D.E., Menniti, D., Pinnarelli, A., Sergeyev, Y.D., Sorrentino, N.: Tuning fuzzy power-system stabilizers in multi-machine systems by global optimization algorithms based on efficient domain partitions. Electric. Power Syst. Res. 78, 1217–1229 (2008)
Kvasov, D.E., Pizzuti, C., Sergeyev, Y.D.: Local tuning and partition strategies for diagonal GO methods. Numer. Math. 94, 93–106 (2003)
Lera, D., Sergeyev, Y.D.: Lipschitz and Hölder global optimization using space-filling curves. Appl. Numer. Math. 60, 115–129 (2010)
Lera, D., Sergeyev, Y.D.: Deterministic global optimization using space-filling curves and multiple estimates of Lipschitz and Holder constants. Commun. Nonlinear Sci. Numer. Simul. 23, 328–342 (2015)
Oliveira Jr., H.A., Petraglia, A.: Global optimization using space-filling curves and measure-preserving transformations. In: Gaspar-Cunha, A., Takahashi, R., Schaefer, G., Costa, L. (eds.) Soft Computing in Industrial Applications. AINSC, vol. 96, pp. 121–130. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20505-7_10
Paulavičius, R., Žilinskas, J.: Simplicial Global Optimization. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-9093-7
Pintér, J.D.: Global Optimization in Action. Kluwer, Dordrecht (1996)
Sergeyev, Y.D., Grishagin, V.A.: Parallel asynchronous global search and the nested optimization scheme. J. Comput. Anal. Appl. 3, 123–145 (2001)
Sergeyev, Y.D., Kvasov, D.E.: Deterministic Global Optimization: An Introduction to the Diagonal Approach. Springer, New York (2017). https://doi.org/10.1007/978-1-4939-7199-2
Sergeyev, Y.D., Kvasov, D.E., Mukhametzhanov, M.S.: Emmental-type GKLS-based multiextremal smooth test problems with non-linear constraints. In: Battiti, R., Kvasov, D.E., Sergeyev, Y.D. (eds.) LION 2017. LNCS, vol. 10556, pp. 383–388. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69404-7_35
Sergeyev, Y.D., Strongin, R.G., Lera, D.: Introduction to Global Optimization Exploiting Space-Filling Curves. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-8042-6
Shevtsov, I.Y., Markine, V.L., Esveld, C.: Optimal design of wheel profile for railway vehicles. In: Proceedings 6th International Conference on Contact Mechanics and Wear of Rail/Wheel Systems, Gothenburg, Sweden, pp. 231–236 (2003)
Shi, L., Ólafsson, S.: Nested partitions method for global optimization. Oper. Res. 48, 390–407 (2000)
Snyman, J.A., Fatti, L.P.: A multi-start global minimization algorithm with dynamic search trajectories. J. Optimi. Theory Appl. 54(1), 121–141 (1987)
Strongin, R.G., Sergeyev, Y.D.: Global Optimization with Non-convex Constraints: Sequential and Parallel Algorithms, 3rd edn. Springer, New York (2014). https://doi.org/10.1007/978-1-4615-4677-1
Zangwill, W.I.: Non-linear programming via penalty functions. Manag. Sci. 13(5), 344–358 (1967)
Zhao, Z., Meza, J.C., Hove, V.: Using pattern search methods for surface structure determination of nanomaterials. J. Phys. Condens. Matter 18(39), 8693–8706 (2006)
Zhigljavsky, A.A., Žilinskas, A.: Stochastic Global Optimization. Springer, New York (2008). https://doi.org/10.1007/978-0-387-74740-8
Acknowledgements
The research of the first author has been supported by the Russian Science Foundation, project No 16-11-10150 “Novel efficient methods and software tools for timeconsuming decision make problems using superior-performance supercomputers.”
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gergel, V., Grishagin, V., Israfilov, R. (2020). Multiextremal Optimization in Feasible Regions with Computable Boundaries on the Base of the Adaptive Nested Scheme. In: Sergeyev, Y., Kvasov, D. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2019. Lecture Notes in Computer Science(), vol 11974. Springer, Cham. https://doi.org/10.1007/978-3-030-40616-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-40616-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-40615-8
Online ISBN: 978-3-030-40616-5
eBook Packages: Computer ScienceComputer Science (R0)