Relevant bibliographies by topics / Global Optimization

Academic literature on the topic 'Global Optimization'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Global Optimization"

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Sahinidis, Nikolaos V. "Global optimization." Optimization Methods and Software 24, no. 4-5 (October 2009): 479–82. http://dx.doi.org/10.1080/10556780903135287.

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Hartke, Bernd. "Global optimization." Wiley Interdisciplinary Reviews: Computational Molecular Science 1, no. 6 (May 12, 2011): 879–87. http://dx.doi.org/10.1002/wcms.70.

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Stephens, C. P., and W. Baritompa. "Global Optimization Requires Global Information." Journal of Optimization Theory and Applications 96, no. 3 (March 1998): 575–88. http://dx.doi.org/10.1023/a:1022612511618.

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Black, Fischer, and Robert Litterman. "Global Portfolio Optimization." Financial Analysts Journal 48, no. 5 (September 1992): 28–43. http://dx.doi.org/10.2469/faj.v48.n5.28.

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Sellis, Timos K. "Global query optimization." ACM SIGMOD Record 15, no. 2 (June 15, 1986): 191–205. http://dx.doi.org/10.1145/16856.16874.

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Odili, Julius Beneoluchi, and A. Noraziah. "African Buffalo Optimization for Global Optimization." Current Science 114, no. 03 (February 10, 2018): 627. http://dx.doi.org/10.18520/cs/v114/i03/627-636.

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Ng, Chi-Kong, Duan Li, and Lian-Sheng Zhang. "Global Descent Method for Global Optimization." SIAM Journal on Optimization 20, no. 6 (January 2010): 3161–84. http://dx.doi.org/10.1137/090749815.

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Beheshti, Zahra, Siti Mariyam Shamsuddin, and Sarina Sulaiman. "Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems." Mathematical Problems in Engineering 2014 (2014): 1–19. http://dx.doi.org/10.1155/2014/907386.

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In recent years, particle swarm optimization (PSO) has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO) is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20) unimodal and multimodal nonlinear benchmark functions and its performance is compared with several well-known PSO algorithms. The experimental results showed that the proposed method improves the performance of PSO algorithm in terms of solution accuracy and convergence speed.
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Ouyang, Hai-bin, Li-qun Gao, Xiang-yong Kong, De-xuan Zou, and Steven Li. "Teaching-learning based optimization with global crossover for global optimization problems." Applied Mathematics and Computation 265 (August 2015): 533–56. http://dx.doi.org/10.1016/j.amc.2015.05.012.

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Kassoul, Khelil, Nicolas Zufferey, Naoufel Cheikhrouhou, and Samir Brahim Belhaouari. "Exponential Particle Swarm Optimization for Global Optimization." IEEE Access 10 (2022): 78320–44. http://dx.doi.org/10.1109/access.2022.3193396.

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Dissertations / Theses on the topic "Global Optimization"

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Singer, Adam B. "Global dynamic optimization." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28662.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2004.
Includes bibliographical references (p. 247-256).
(cont.) on a set composed of the Cartesian product between the parameter bounds and the state bounds. Furthermore, I show that the solution of the differential equations is affine in the parameters. Because the feasible set is convex pointwise in time, the standard result that a convex function composed with an affine function remains convex yields the desired result that the integrand is convex under composition. Additionally, methods are developed using interval arithmetic to derive the exact state bounds for the solution of a linear dynamic system. Given a nonzero tolerance, the method is rigorously shown to converge to the global solution in a finite time. An implementation is developed, and via a collection of case studies, the technique is shown to be very efficient in computing the global solutions. For problems with embedded nonlinear dynamic systems, the analysis requires a more sophisticated composition technique attributed to McCormick. McCormick's composition technique provides a method for computing a convex underestimator for for the integrand given an arbitrary nonlinear dynamic system provided that convex underestimators and concave overestimators can be given for the states. Because the states are known only implicitly via the solution of the nonlinear differential equations, deriving these convex underestimators and concave overestimators is a highly nontrivial task. Based on standard optimization results, outer approximation, the affine solution to linear dynamic systems, and differential inequalities, I present a novel method for constructing convex underestimators and concave overestimators for arbitrary nonlinear dynamic systems ...
My thesis focuses on global optimization of nonconvex integral objective functions subject to parameter dependent ordinary differential equations. In particular, efficient, deterministic algorithms are developed for solving problems with both linear and nonlinear dynamics embedded. The techniques utilized for each problem classification are unified by an underlying composition principle transferring the nonconvexity of the embedded dynamics into the integral objective function. This composition, in conjunction with control parameterization, effectively transforms the problem into a finite dimensional optimization problem where the objective function is given implicitly via the solution of a dynamic system. A standard branch-and-bound algorithm is employed to converge to the global solution by systematically eliminating portions of the feasible space by solving an upper bounding problem and convex lower bounding problem at each node. The novel contributions of this work lie in the derivation and solution of these convex lower bounding relaxations. Separate algorithms exist for deriving convex relaxations for problems with linear dynamic systems embedded and problems with nonlinear dynamic systems embedded. However, the two techniques are unified by the method for relaxing the integral in the objective function. I show that integrating a pointwise in time convex relaxation of the original integrand yields a convex underestimator for the integral. Separate composition techniques, however, are required to derive relaxations for the integrand depending upon the nature of the embedded dynamics; each case is addressed separately. For problems with embedded linear dynamic systems, the nonconvex integrand is relaxed pointwise in time
by Adam Benjamin Singer.
Ph.D.
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Ruan, Ning. "Global optimization for nonconvex optimization problems." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1936.

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Duality is one of the most successful ideas in modern science [46] [91]. It is essential in natural phenomena, particularly, in physics and mathematics [39] [94] [96]. In this thesis, we consider the canonical duality theory for several classes of optimization problems.The first problem that we consider is a general sum of fourth-order polynomial minimization problem. This problem arises extensively in engineering and science, including database analysis, computational biology, sensor network communications, nonconvex mechanics, and ecology. We first show that this global optimization problem is actually equivalent to a discretized minimal potential variational problem in large deformation mechanics. Therefore, a general analytical solution is proposed by using the canonical duality theory.The second problem that we consider is a nonconvex quadratic-exponential optimization problem. By using the canonical duality theory, the nonconvex primal problem in n-dimensional space can be converted into a one-dimensional canonical dual problem, which is either a concave maximization or a convex minimization problem with zero duality gap. Several examples are solved so as to illustrate the applicability of the theory developed.The third problem that we consider is quadratic minimization problems subjected to either box or integer constraints. Results show that these nonconvex problems can be converted into concave maximization dual problems over convex feasible spaces without duality gap and the Boolean integer programming problem is actually equivalent to a critical point problem in continuous space. These dual problems can be solved under certain conditions. Both existence and uniqueness of the canonical dual solutions are presented. A canonical duality algorithm is presented and applications are illustrated.The fourth problem that we consider is a quadratic discrete value selection problem subjected to inequality constraints. The problem is first transformed into a quadratic 0-1 integer programming problem. The dual problem is thus constructed by using the canonical duality theory. Under appropriate conditions, this dual problem is a maximization problem of a concave function over a convex continuous space. Theoretical results show that the canonical duality theory can either provide a global optimization solution, or an optimal lower bound approximation to this NP-hard problem. Numerical simulation studies, including some relatively large scale problems, are carried out so as to demonstrate the effectiveness and efficiency of the canonical duality method. An open problem for understanding NP-hard problems is proposed.The fifth problem that we consider is a mixed-integer quadratic minimization problem with fixed cost terms. We show that this well-known NP-hard problem in R2n can be transformed into a continuous concave maximization dual problem over a convex feasible subset of Rn with zero duality gap. We also discuss connections between the proposed canonical duality theory approach and the classical Lagrangian duality approach. The resulting canonical dual problem can be solved under certain conditions, by traditional convex programming methods. Conditions for the existence and uniqueness of global optimal solutions are presented. An application to a decoupled mixed-integer problem is used to illustrate the derivation of analytic solutions for globally minimizing the objective function. Numerical examples for both decoupled and general mixed-integral problems are presented, and an open problem is proposed for future study.The sixth problem that we consider is a general nonconvex quadratic minimization problem with nonconvex constraints. By using the canonical dual transformation, the nonconvex primal problem can be converted into a canonical dual problem (i.e., either a concave maximization problem with zero duality gap). Illustrative applications to quadratic minimization with multiple quadratic constraints, box/integer constraints, and general nonconvex polynomial constraints are discussed, along with insightful connections to classical Lagrangian duality. Conditions for ensuring the existence and uniqueness of global optimal solutions are presented. Several numerical examples are solved.The seventh problem that we consider is a general nonlinear algebraic system. By using the least square method, the nonlinear system of m quadratic equations in n-dimensional space is first formulated as a nonconvex optimization problem. We then prove that, by using the canonical duality theory, this nonconvex problem is equivalent to a concave maximization problem in Rm, which can be solved by well-developed convex optimization techniques. Both existence and uniqueness of global optimal solutions are discussed, and several illustrative examples are presented.The eighth problem that we consider is a general sensor network localization problem. It is shown that by the canonical duality theory, this nonconvex minimization problem is equivalent to a concave maximization problem over a convex set in a symmetrical matrix space, and hence can be solved by combining a perturbation technique with existing optimization techniques. Applications are illustrated and results show that the proposed method is potentially a powerful one for large-scale sensor network localization problems.
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Moore, Roxanne Adele. "Value-based global optimization." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44750.

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Computational models and simulations are essential system design tools that allow for improved decision making and cost reductions during all phases of the design process. However, the most accurate models are often computationally expensive and can therefore only be used sporadically. Consequently, designers are often forced to choose between exploring many design alternatives with less accurate, inexpensive models and evaluating fewer alternatives with the most accurate models. To achieve both broad exploration of the alternatives and accurate determination of the best alternative with reasonable costs incurred, surrogate modeling and variable accuracy modeling are used widely. A surrogate model is a mathematically tractable approximation of a more expensive model based on a limited sampling of that model, while variable accuracy modeling involves a collection of different models of the same system with different accuracies and computational costs. As compared to using only very accurate and expensive models, designers can determine the best solutions more efficiently using surrogate and variable accuracy models because obviously poor solutions can be eliminated inexpensively using only the less expensive, less accurate models. The most accurate models are then reserved for discerning the best solution from the set of good solutions. In this thesis, a Value-Based Global Optimization (VGO) algorithm is introduced. The algorithm uses kriging-like surrogate models and a sequential sampling strategy based on Value of Information (VoI) to optimize an objective characterized by multiple analysis models with different accuracies. It builds on two primary research contributions. The first is a novel surrogate modeling method that accommodates data from any number of analysis models with different accuracies and costs. The second contribution is the use of Value of Information (VoI) as a new metric for guiding the sequential sampling process for global optimization. In this manner, the cost of further analysis is explicitly taken into account during the optimization process. Results characterizing the algorithm show that VGO outperforms Efficient Global Optimization (EGO), a similar global optimization algorithm that is considered to be the current state of the art. It is shown that when cost is taken into account in the final utility, VGO achieves a higher utility than EGO with statistical significance. In further experiments, it is shown that VGO can be successfully applied to higher dimensional problems as well as practical engineering design examples.
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Birbil, Sevket Ilker. "Stochastic Global Optimization Techniques." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-20020403-171452.

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In this research, a novel population-based global optimization method has been studied. The method is called Electromagnetism-like Mechanism or in short EM. The proposed method mimicks the behavior of electrically charged particles. In other words, a set of points is sampled from the feasible region and these points imitate the role of the charged particles in basic electromagnetism. The underlying idea of the method is directing sample points toward local optimizers, which point out attractive regions of the feasible space.The proposed method has been applied to different test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature. Without using the higher order information, EM has converged rapidly (in terms of the number of function evaluations) to the global optimum and produced highly efficient results for problems of varying degree of difficulty.After a systematic study of the underlying stochastic process, the proof of convergence to the global optimum has been given for the proposed method. The thrust of the proof has been to show that in the limit, at least one of the points in the population moves to the neighborhood of the global optimum with probability one.The structure of the proposed method is very flexible permitting the easy development of variations. Capitalizing on this, several variants of the proposed method has been developed and compared with the other methods from the literature. These variants of EM have been able to provide accurate answers to selected problems and in many cases have been able to outperform other well-known methods.

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Gattupalli, Rajeswar R. "Advances in global optimization /." View online ; access limited to URI, 2008. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/AAI3314454.

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Zehnder, Nino. "Global optimization of laminated structures /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17573.

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Quttineh, Nils-Hassan. "Algorithms for Costly Global Optimization." Licentiate thesis, Mälardalen University, School of Education, Culture and Communication, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-5970.

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There exists many applications with so-called costly problems, which means that the objective function you want to maximize or minimize cannot be described using standard functions and expressions. Instead one considers these objective functions as ``black box'' where the parameter values are sent in and a function value is returned. This implies in particular that no derivative information is available.The reason for describing these problems as expensive is that it may take a long time to calculate a single function value. The black box could, for example, solve a large system of differential equations or carrying out a heavy simulation, which can take anywhere from several minutes to several hours!These very special conditions therefore requires customized algorithms. Common optimization algorithms are based on calculating function values every now and then, which usually can be done instantly. But with an expensive problem, it may take several hours to compute a single function value. Our main objective is therefore to create algorithms that exploit all available information to the limit before a new function value is calculated. Or in other words, we want to find the optimal solution using as few function evaluations as possible.A good example of real life applications comes from the automotive industry, where on the development of new engines utilize advanced models that are governed by a dozen key parameters. The goal is to optimize the model by changing the parameters in such a way that the engine becomes as energy efficient as possible, but still meets all sorts of demands on strength and external constraints.

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Schonlau, Matthias. "Computer experiments and global optimization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22234.pdf.

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Al-Mharmah, Hisham. "Global optimization of stochastic functions." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/25665.

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Oulmane, Mourad. "On-Chip global interconnect optimization." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33985.

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We present a practical yet accurate approach for dealing with the problem of inserting repeaters along on-chip interconnect lines to meet delay and transition time requirements. This approach is based on the fact that the transition time and the delay at the far end of an interconnect segment are, respectively, independent and linearly dependent on the driving repeater's input transition time, as long as the ratio of the output to input transition time does not exceed a pre-defined value. In this context, we first derive simple closed form expressions for the optimal repeater spacing and sizing. Then, we propose a bottom-up "pseudo" hierarchical quadratic programming method for inserting and sizing repeaters in RC interconnects. This method, unlike Van Ginneken's [30], although largely based on it, is able to account for transition times at every potential repeater insertion point along the RC line of interest. The resulting technique can be readily incorporated in a more general RC network optimization scheme (through repeater insertion) where, eventually, wire sizing can be formulated either as an objective or a constraint.
Accurate moment matching techniques for computing the RC delays and transition times are used in addition to an accurate CMOS inverter/repeater delay model that takes into account short channel effects that are prevalent in deep submicron (DSM) technologies. In particular, a new delay metric, based on the first two moments of the impulse response of the interconnect RC circuit, is derived. Also, a new empirical ramp approximation that takes into account the inherent asymmetry of signals in signal distribution networks in DSM technologies is presented.
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Books on the topic "Global Optimization"

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Horst, Reiner, and Hoang Tuy. Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02947-3.

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Horst, Reiner, and Hoang Tuy. Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03199-5.

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Horst, Reiner, and Hoang Tuy. Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02598-7.

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Törn, Aimo, and Antanas Žilinskas, eds. Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-50871-6.

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Pintér, János D., ed. Global Optimization. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-30927-6.

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Liberti, Leo, and Nelson Maculan, eds. Global Optimization. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-30528-9.

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Schäffler, Stefan. Global Optimization. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3927-1.

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A, Zhilinskas, ed. Global optimization. Berlin: Springer-Verlag, 1989.

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Floudas, Christodoulos A. Deterministic Global Optimization. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-4949-6.

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Chew, Soo Hong, and Quan Zheng. Integral Global Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-46623-6.

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Book chapters on the topic "Global Optimization"

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Tuy, Hoang, Steffen Rebennack, and Panos M. Pardalos. "Global Optimization." In Encyclopedia of Operations Research and Management Science, 650–58. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1142.

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Kulisch, Ulrich, Rolf Hammer, Matthias Hocks, and Dietmar Ratz. "Global Optimization." In C++ Toolbox for Verified Computing I, 312–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79651-7_14.

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Kulisch, Ulrich, Rolf Hammer, Matthias Hocks, and Dietmar Ratz. "Global Optimization." In C++ Toolbox for Verified Computing I, 113–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79651-7_7.

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Forst, Wilhelm, and Dieter Hoffmann. "Global Optimization." In Optimization—Theory and Practice, 341–63. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-78977-4_8.

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Heitzinger, Clemens. "Global Optimization." In Algorithms with JULIA, 307–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16560-3_11.

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Kulisch, Ulrich, Rolf Hammer, Dietmar Ratz, and Matthias Hocks. "Global Optimization." In Springer Series in Computational Mathematics, 282–311. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78423-1_14.

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Kulisch, Ulrich, Rolf Hammer, Dietmar Ratz, and Matthias Hocks. "Global Optimization." In Springer Series in Computational Mathematics, 105–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78423-1_7.

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Schäffler, Stefan. "Unconstrained Global Optimization." In Global Optimization, 21–55. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3927-1_3.

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Schäffler, Stefan. "Constrained Global Optimization." In Global Optimization, 75–103. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3927-1_5.

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Schäffler, Stefan. "Vector Optimization." In Global Optimization, 105–17. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3927-1_6.

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Conference papers on the topic "Global Optimization"

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Sellis, Timos K. "Global query optimization." In the 1986 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/16894.16874.

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NAN, Marin Silviu. "EXACT GLOBAL OPTIMIZATION." In 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017/21/s07.039.

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Daboul, Siad, Stephan Held, Bento Natura, and Daniel Rotter. "Global Interconnect Optimization." In 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 2019. http://dx.doi.org/10.1109/iccad45719.2019.8942155.

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Bánhelyi, Balázs, Tibor Csendes, Balázs Lévai, Dániel Zombori, and László Pál. "Improved versions of the GLOBAL optimization algorithm and the globalJ modularized toolbox." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5089989.

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Calvin, James M., Craig J. Gotsman, and Cuicui Zheng. "Global optimization for image registration." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5089975.

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Kosolap, Anatolii. "Quadratic regularization for global optimization." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5090001.

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BOGDAN, Constantin. "NUMERICAL NONLINEAR GLOBAL OPTIMIZATION." In 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017/21/s07.061.

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Bilbro, Griff L. "Fast stochastic global optimization." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Su-Shing Chen. SPIE, 1993. http://dx.doi.org/10.1117/12.162050.

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Rogers, John. "Global optimization and desensitization." In SPIE Optifab, edited by Julie L. Bentley and Sebastian Stoebenau. SPIE, 2015. http://dx.doi.org/10.1117/12.2196010.

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Jagadish, H. V., Hui Jin, Beng Chin Ooi, and Kian-Lee Tan. "Global optimization of histograms." In the 2001 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375663.375687.

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Reports on the topic "Global Optimization"

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Dunlavy, Daniel M., and Dianne P. O'Leary. Homotopy optimization methods for global optimization. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/876373.

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Haftka, Raphael T. Theory and Algorithms for Global/Local Design Optimization. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada437353.

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Malony, Allen D., and Sameer S. Shende. MOGO: Model-Oriented Global Optimization of Petascale Applications. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1096588.

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Eskow, Elizabeth, and Robert B. Schnabel. Mathematical Modeling of a Parallel Global Optimization Algorithm. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada446514.

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Glover, Fred. Probabilistic Methods for Global Optimization in Continuous Variables. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada304297.

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Glover, Fred. Probabilistic Methods or Global Optimization in Continuous Variables. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada311405.

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Marcus, Steven I., Michael C. Fu, and Jiaqiao Hu. Simulation-Based Methodologies for Global Optimization and Planning. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada591505.

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8

Oblow, E. M. STP: A Stochastic Tunneling Algorithm for Global Optimization. Office of Scientific and Technical Information (OSTI), May 1999. http://dx.doi.org/10.2172/814395.

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9

HART, WILLIAM E. LDRD Final Report: Global Optimization for Engineering Science Problems. Office of Scientific and Technical Information (OSTI), December 1999. http://dx.doi.org/10.2172/15153.

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10

Anderson, P. B., D. W. Norton, and M. A. Young. CM-5 Kernel Optimization of a Global Weather Model. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada295796.

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