Dissertations / Theses on the topic 'Stochastic local search'
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Xu, Ruoxi. "Regression Model Stochastic Search via Local Orthogonalization." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322589253.
Full textHe, Jun. "Constraints for Membership in Formal Languages under Systematic Search and Stochastic Local Search." Doctoral thesis, Uppsala universitet, Avdelningen för datalogi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-196347.
Full textBalint, Adrian [Verfasser]. "Engineering stochastic local search for the satisfiability problem / Adrian Balint." Ulm : Universität Ulm. Fakultät für Ingenieurwissenschaften und Informatik, 2014. http://d-nb.info/1046623567/34.
Full textChiarandini, Marco. "Stochastic Local Search Methods for Highly Constrained Combinatorial Optimisation Problems." Phd thesis, [S.l. : s.n.], 2005. https://tuprints.ulb.tu-darmstadt.de/595/1/ChiarandiniPhD.pdf.
Full textCai, Shaowei. "Novel Local Search Methods for Satisfiability." Thesis, Griffith University, 2015. http://hdl.handle.net/10072/366424.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Science, Environment, Engineering and Technology
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Paquete, Luís F. "Stochastic local search algorithms for multiobjective combinatorial optimization methods and analysis." Berlin Aka, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2770886&prov=M&dok_var=1&dok_ext=htm.
Full textBin, Hussin Mohamed Saifullah. "Stochastic local search algorithms for single and bi-objective quadratic assignment problems." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/222286.
Full textDoctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
Garattoni, Lorenzo. "Advanced stochastic local search methods for automatic design of Boolean network robots." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3443/.
Full textBianchi, Leonora. "Ant colony optimization and local search for the probabilistic traveling salesman problem: a case study in stochastic combinatorial optimization." Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210877.
Full textOptimization problems under uncertainty are complex and difficult, and often classical algorithmic approaches based on mathematical and dynamic programming are able to solve only very small problem instances. For this reason, in recent years metaheuristic algorithms such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others, are emerging as successful alternatives to classical approaches.
In this thesis, metaheuristics that have been applied so far to SCOPs are introduced and the related literature is thoroughly reviewed. In particular, two properties of metaheuristics emerge from the survey: they are a valid alternative to exact classical methods for addressing real-sized SCOPs, and they are flexible, since they can be quite easily adapted to solve different SCOPs formulations, both static and dynamic. On the base of the current literature, we identify the following as the key open issues in solving SCOPs via metaheuristics:
(1) the design and integration of ad hoc, fast and effective objective function approximations inside the optimization algorithm;
(2) the estimation of the objective function by sampling when no closed-form expression for the objective function is available, and the study of methods to reduce the time complexity and noise inherent to this type of estimation;
(3) the characterization of the efficiency of metaheuristic variants with respect to different levels of stochasticity in the problem instances.
We investigate the above issues by focusing in particular on a SCOP belonging to the class of vehicle routing problems: the Probabilistic Traveling Salesman Problem (PTSP). For the PTSP, we consider the Ant Colony Optimization metaheuristic and we design efficient local search algorithms that can enhance its performance. We obtain state-of-the-art algorithms, but we show that they are effective only for instances above a certain level of stochasticity, otherwise it is more convenient to solve the problem as if it were deterministic.
The algorithmic variants based on an estimation of the objective function by sampling obtain worse results, but qualitatively have the same behavior of the algorithms based on the exact objective function, with respect to the level of stochasticity. Moreover, we show that the performance of algorithmic variants based on ad hoc approximations is strongly correlated with the absolute error of the approximation, and that the effect on local search of ad hoc approximations can be very degrading.
Finally, we briefly address another SCOP belonging to the class of vehicle routing problems: the Vehicle Routing Problem with Stochastic Demands (VRPSD). For this problem, we have implemented and tested several metaheuristics, and we have studied the impact of integrating in them different ad hoc approximations.
Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished
Goodson, Justin Christopher. "Solution methodologies for vehicle routing problems with stochastic demand." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/675.
Full textPrudius, Andrei A. "Adaptive Random Search Methods for Simulation Optimization." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16318.
Full textPham, Duc Nghia, and n/a. "Modelling and Exploiting Structures in Solving Propositional Satisfiability Problems." Griffith University. Institute for Integrated and Intelligent Systems, 2006. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20070216.143447.
Full textPham, Duc Nghia. "Modelling and Exploiting Structures in Solving Propositional Satisfiability Problems." Thesis, Griffith University, 2006. http://hdl.handle.net/10072/365503.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
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Nötel, Jörg. "Active Brownian Particles with alpha Stable Noise in the Angular Dynamics: Non Gaussian Displacements, Adiabatic Eliminations, and Local Searchers." Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/19681.
Full textActive Brownian particles described by Langevin equations are used to model the behavior of simple biological organisms or artificial objects that are able to perform self propulsion. In this thesis we discuss active particles with constant speed. In the first part, we consider angular driving by white Levy-stable noise and we discuss the mean squared displacement and diffusion coefficients. We derive an overdamped description for those particles that is valid at time scales larger the relaxation time. In order to provide an experimentally accessible property that distinguishes between the considered noise types, we derive an analytical expression for the kurtosis. Afterwards, we consider an Ornstein-Uhlenbeck process driven by Cauchy noise in the angular dynamics of the particle. While, we find normal diffusion with the diffusion coefficient identical to the white noise case we observe a Non-Gaussian displacement at time scales that can be considerable larger than the relaxation time and the time scale provided by the Ornstein-Uhlenbeck process. In order to provide a limit for the time needed for the transition to a Gaussian displacement, we approximate the kurtosis. Afterwards, we lay the foundation for a stochastic model for local search. Local search is concerned with the neighborhood of a given spot called home. We consider an active particle with constant speed and alpha-stable noise in the dynamics of the direction of motion. The deterministic motion will be discussed before considering the noise to be present. An analytical result for the steady state spatial density will be given. We will find an optimal noise strength for the local search and only a weak dependence on the considered noise types. Several extensions to the introduced model will then be considered. One extension includes a distance dependent coupling towards the home and thus the model becomes more general. Another extension concerned with an erroneous understanding by the particle of the direction of the home leads to the result that the return probability to the home depends on the noise type. Finally we consider a group of searchers.
Legriel, Julien. "Optimisation multicritères et applications aux systèmes multi-processeurs embarqués." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00875163.
Full textJunuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.
Full textMegahed, Aly. "Supply chain planning models with general backorder penalties, supply and demand uncertainty, and quantity discounts." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/54011.
Full textSmyth, Kevin R. G. "Understanding stochastic local search algorithms : an empirical analysis of the relationship between search space structure and algorithm behaviour." Thesis, 2004. http://hdl.handle.net/2429/15656.
Full textScience, Faculty of
Computer Science, Department of
Graduate
Chiarandini, Marco [Verfasser]. "Stochastic local search methods for highly constrained combinatorial optimisation problems : graph colouring, generalisations, and applications / von Marco Chiarandini." 2005. http://d-nb.info/976096552/34.
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