Relevant bibliographies by topics / Continuous global optimization problems

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Continuous global optimization problems'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Continuous global optimization problems"

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Vinkó, Tamás, and Kitti Gelle. "Basin Hopping Networks of continuous global optimization problems." Central European Journal of Operations Research 25, no. 4 (2017): 985–1006. http://dx.doi.org/10.1007/s10100-017-0480-0.

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Dua, V., K. P. Papalexandri, and E. N. Pistikopoulos. "Global Optimization Issues in Multiparametric Continuous and Mixed-Integer Optimization Problems." Journal of Global Optimization 30, no. 1 (2004): 59–89. http://dx.doi.org/10.1023/b:jogo.0000049091.73047.7e.

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Ji, Mingjun, and Jacek Klinowski. "Convergence of taboo search in continuous global optimization." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, no. 2071 (2006): 2077–84. http://dx.doi.org/10.1098/rspa.2006.1678.

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While taboo search (TS), a method of global optimization, has successfully solved many optimization problems, little is known about its convergence properties, especially for continuous optimization tasks. We consider the global convergence of the original TS for solving continuous optimization problems, and give a condition which guarantees the convergence of the objective value sequence of the method. We also prove that the minimum objective value sequence converges to the vicinity of the global optimal value with probability 1.
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Li, Changmin, Hai Yang, Daoli Zhu, and Qiang Meng. "A global optimization method for continuous network design problems." Transportation Research Part B: Methodological 46, no. 9 (2012): 1144–58. http://dx.doi.org/10.1016/j.trb.2012.05.003.

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Wu, Jinran, You-Gan Wang, Kevin Burrage, Yu-Chu Tian, Brodie Lawson, and Zhe Ding. "An improved firefly algorithm for global continuous optimization problems." Expert Systems with Applications 149 (July 2020): 113340. http://dx.doi.org/10.1016/j.eswa.2020.113340.

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Ritthipakdee, Amarita, Arit Thammano, Nol Premasathian, and Duangjai Jitkongchuen. "Firefly Mating Algorithm for Continuous Optimization Problems." Computational Intelligence and Neuroscience 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8034573.

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This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones) against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima.
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Valian, Ehsan, Saeed Tavakoli, and Shahram Mohanna. "An intelligent global harmony search approach to continuous optimization problems." Applied Mathematics and Computation 232 (April 2014): 670–84. http://dx.doi.org/10.1016/j.amc.2014.01.086.

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Pomrehn, L. P., and P. Y. Papalambros. "Global and Discrete Constraint Activity." Journal of Mechanical Design 116, no. 3 (1994): 745–48. http://dx.doi.org/10.1115/1.2919445.

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The concept of constraint activity, widely used throughout the optimization literature, is extended and clarified to deal with global optimization problems containing either continuous or discrete variables. The article presents definitions applicable to individual constraints and discusses definitions for groups of constraints. Concepts are reinforced through the use of examples. The definitions are used to investigate the ideas of optimization “cases” and monotonicity analysis as applied to global and discrete problems. Relationships to local optimization are also noted.
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Wedyan, Ahmad, Jacqueline Whalley, and Ajit Narayanan. "Hydrological Cycle Algorithm for Continuous Optimization Problems." Journal of Optimization 2017 (2017): 1–25. http://dx.doi.org/10.1155/2017/3828420.

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A new nature-inspired optimization algorithm called the Hydrological Cycle Algorithm (HCA) is proposed based on the continuous movement of water in nature. In the HCA, a collection of water drops passes through various hydrological water cycle stages, such as flow, evaporation, condensation, and precipitation. Each stage plays an important role in generating solutions and avoiding premature convergence. The HCA shares information by direct and indirect communication among the water drops, which improves solution quality. Similarities and differences between HCA and other water-based algorithms are identified, and the implications of these differences on overall performance are discussed. A new topological representation for problems with a continuous domain is proposed. In proof-of-concept experiments, the HCA is applied on a variety of benchmarked continuous numerical functions. The results were found to be competitive in comparison to a number of other algorithms and validate the effectiveness of HCA. Also demonstrated is the ability of HCA to escape from local optima solutions and converge to global solutions. Thus, HCA provides an alternative approach to tackling various types of multimodal continuous optimization problems as well as an overall framework for water-based particle algorithms in general.
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Chiu, Chui-Yu, Po-Chou Shih, and Xuechao Li. "A Dynamic Adjusting Novel Global Harmony Search for Continuous Optimization Problems." Symmetry 10, no. 8 (2018): 337. http://dx.doi.org/10.3390/sym10080337.

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A novel global harmony search (NGHS) algorithm, as proposed in 2010, is an improved algorithm that combines the harmony search (HS), particle swarm optimization (PSO), and a genetic algorithm (GA). Moreover, the fixed parameter of mutation probability was used in the NGHS algorithm. However, appropriate parameters can enhance the searching ability of a metaheuristic algorithm, and their importance has been described in many studies. Inspired by the adjustment strategy of the improved harmony search (IHS) algorithm, a dynamic adjusting novel global harmony search (DANGHS) algorithm, which combines NGHS and dynamic adjustment strategies for genetic mutation probability, is introduced in this paper. Moreover, extensive computational experiments and comparisons are carried out for 14 benchmark continuous optimization problems. The results show that the proposed DANGHS algorithm has better performance in comparison with other HS algorithms in most problems. In addition, the proposed algorithm is more efficient than previous methods. Finally, different strategies are suitable for different situations. Among these strategies, the most interesting and exciting strategy is the periodic dynamic adjustment strategy. For a specific problem, the periodic dynamic adjustment strategy could have better performance in comparison with other decreasing or increasing strategies. These results inspire us to further investigate this kind of periodic dynamic adjustment strategy in future experiments.
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Dissertations / Theses on the topic "Continuous global optimization problems"

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Ahmed, Abdel-Rahman Hedar A. "Studies on metaheuristics continuous global optimization problems." 京都大学 (Kyoto University), 2004. http://hdl.handle.net/2433/145313.

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Hirsch, Michael J. "Grasp-based heuristics for continuous global optimization problems." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0017140.

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Silva, Victor Billy da. "Otimização volumétrica de gemas de cor utilizadas para lapidação." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/90428.

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O Problema do Lapidário tem como objetivo encontrar o modelo de lapidação que resulte no maior aproveitamento volumétrico para uma dada gema bruta. Nesta dissertação apresentamos um Algoritmo Genético com variáveis de valores reais, e um GRASP Contínuo como heurísticas para resolução deste problema. Ambos os algoritmos maximizam o fator de escala do modelo de lapidação, sobre todas as posições de centro e ângulos de giro que o modelo pode assumir, buscando encontrar o modelo de maior volume inscrito no interior da gema, representada virtualmente por uma malha triangular. Propomos também um algoritmo de avaliação de uma instância do problema, o qual determina eficientemente o maior fator de escala, para um dado centro e orientação, que o modelo de lapidação pode assumir permanecendo completamente no interior da gema. Os algoritmos propostos foram avaliados em um conjunto de 50 gemas reais para o problema, utilizando como modelos base os cortes redondo e oval. Por fim, comparamos os resultados computacionais obtidos em relação a aproveitamento volumétrico e tempo de execução com os principais trabalhos relatados na literatura, demonstrando que as heurísticas propostas são competitivas com as demais abordagens.<br>The goal of the gemstone cutting problem is to find the largest cutting design which fits inside a given rough gemstone. In this work, we propose a real-valued Genetic Algorithm and a Continuous GRASP heuristic to solve it. The algorithms determine the largest scaling factor, over all possibilities of centers and orientations which the cutting could assume, finding the cutting with the largest volume as possible inside a gemstone, represented by a triangular mesh. We also propose an algorithm to evaluate a problem instance. This method efficiently determines the greatest scaling factor, for a given center and orientation, such that the cutting fits inside the rough gemstone. The proposed algorithms are validated for an instance set of 50 real-world gemstones, using the round and oval cuttings. Finally, we compare our computational results, for volume yield and running time, with the state-of-art. Ours methods are proved be competitive with the previous approachs.
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Couetoux, Adrien. "Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112192.

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Dans cette thèse, nous avons étudié les problèmes de décisions séquentielles, avec comme application la gestion de stocks d'énergie. Traditionnellement, ces problèmes sont résolus par programmation dynamique stochastique. Mais la grande dimension, et la non convexité du problème, amènent à faire des simplifications sur le modèle pour pouvoir faire fonctionner ces méthodes.Nous avons donc étudié une méthode alternative, qui ne requiert pas de simplifications du modèle: Monte Carlo Tree Search (MCTS). Nous avons commencé par étendre le MCTS classique (qui s’applique aux domaines finis et déterministes) aux domaines continus et stochastiques. Pour cela, nous avons utilisé la méthode de Double Progressive Widening (DPW), qui permet de gérer le ratio entre largeur et profondeur de l’arbre, à l’aide de deux méta paramètres. Nous avons aussi proposé une heuristique nommée Blind Value (BV) pour améliorer la recherche de nouvelles actions, en utilisant l’information donnée par les simulations passées. D’autre part, nous avons étendu l’heuristique RAVE aux domaines continus. Enfin, nous avons proposé deux nouvelles méthodes pour faire remonter l’information dans l’arbre, qui ont beaucoup amélioré la vitesse de convergence sur deux cas tests.Une part importante de notre travail a été de proposer une façon de mêler MCTS avec des heuristiques rapides pré-existantes. C’est une idée particulièrement intéressante dans le cas de la gestion d’énergie, car ces problèmes sont pour le moment résolus de manière approchée. Nous avons montré comment utiliser Direct Policy Search (DPS) pour rechercher une politique par défaut efficace, qui est ensuite utilisée à l’intérieur de MCTS. Les résultats expérimentaux sont très encourageants.Nous avons aussi appliqué MCTS à des processus markoviens partiellement observables (POMDP), avec comme exemple le jeu de démineur. Dans ce cas, les algorithmes actuels ne sont pas optimaux, et notre approche l’est, en transformant le POMDP en MDP, par un changement de vecteur d’état.Enfin, nous avons utilisé MCTS dans un cadre de méta-bandit, pour résoudre des problèmes d’investissement. Le choix d’investissement est fait par des algorithmes de bandits à bras multiples, tandis que l’évaluation de chaque bras est faite par MCTS.Une des conclusions importantes de ces travaux est que MCTS en continu a besoin de très peu d’hypothèses (uniquement un modèle génératif du problème), converge vers l’optimum, et peut facilement améliorer des méthodes suboptimales existantes<br>In this thesis, we study sequential decision making problems, with a focus on the unit commitment problem. Traditionally solved by dynamic programming methods, this problem is still a challenge, due to its high dimension and to the sacrifices made on the accuracy of the model to apply state of the art methods. We investigate on the applicability of Monte Carlo Tree Search methods for this problem, and other problems that are single player, stochastic and continuous sequential decision making problems. We started by extending the traditional finite state MCTS to continuous domains, with a method called Double Progressive Widening (DPW). This method relies on two hyper parameters, and determines the ratio between width and depth in the nodes of the tree. We developed a heuristic called Blind Value (BV) to improve the exploration of new actions, using the information from past simulations. We also extended the RAVE heuristic to continuous domain. Finally, we proposed two new ways of backing up information through the tree, that improved the convergence speed considerably on two test cases.An important part of our work was to propose a way to mix MCTS with existing powerful heuristics, with the application to energy management in mind. We did so by proposing a framework that allows to learn a good default policy by Direct Policy Search (DPS), and to include it in MCTS. The experimental results are very positive.To extend the reach of MCTS, we showed how it could be used to solve Partially Observable Markovian Decision Processes, with an application to game of Mine Sweeper, for which no consistent method had been proposed before.Finally, we used MCTS in a meta-bandit framework to solve energy investment problems: the investment decision was handled by classical bandit algorithms, while the evaluation of each investment was done by MCTS.The most important take away is that continuous MCTS has almost no assumption (besides the need for a generative model), is consistent, and can easily improve existing suboptimal solvers by using a method similar to what we proposed with DPS
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Janiszewski, Szymon Pawel. "Optimization problems in discrete and continuous time." Thesis, University of Hull, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396087.

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Liao, Tianjun. "Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209439.

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Continuous optimization problems are optimization problems where all variables<p>have a domain that typically is a subset of the real numbers; mixed discrete-continuous<p>optimization problems have additionally other types of variables, so<p>that some variables are continuous and others are on an ordinal or categorical<p>scale. Continuous and mixed discrete-continuous problems have a wide range<p>of applications in disciplines such as computer science, mechanical or electrical<p>engineering, economics and bioinformatics. These problems are also often hard to<p>solve due to their inherent difficulties such as a large number of variables, many<p>local optima or other factors making problems hard. Therefore, in this thesis our<p>focus is on the design, engineering and configuration of high-performing heuristic<p>optimization algorithms.<p>We tackle continuous and mixed discrete-continuous optimization problems<p>with two classes of population-based heuristic algorithms, ant colony optimization<p>(ACO) algorithms and evolution strategies. In a nutshell, the main contributions<p>of this thesis are that (i) we advance the design and engineering of ACO algorithms to algorithms that are competitive or superior to recent state-of-the-art<p>algorithms for continuous and mixed discrete-continuous optimization problems,<p>(ii) we improve upon a specific state-of-the-art evolution strategy, the covariance<p>matrix adaptation evolution strategy (CMA-ES), and (iii) we extend CMA-ES to<p>tackle mixed discrete-continuous optimization problems.<p>More in detail, we propose a unified ant colony optimization (ACO) framework<p>for continuous optimization (UACOR). This framework synthesizes algorithmic<p>components of two ACO algorithms that have been proposed in the literature<p>and an incremental ACO algorithm with local search for continuous optimization,<p>which we have proposed during my doctoral research. The design of UACOR<p>allows the usage of automatic algorithm configuration techniques to automatically<p>derive new, high-performing ACO algorithms for continuous optimization. We also<p>propose iCMAES-ILS, a hybrid algorithm that loosely couples IPOP-CMA-ES, a<p>CMA-ES variant that uses a restart schema coupled with an increasing population<p>size, and a new iterated local search (ILS) algorithm for continuous optimization.<p>The hybrid algorithm consists of an initial competition phase, in which IPOP-CMA-ES and the ILS algorithm compete for further deployment during a second<p>phase. A cooperative aspect of the hybrid algorithm is implemented in the form<p>of some limited information exchange from IPOP-CMA-ES to the ILS algorithm<p>during the initial phase. Experimental studies on recent benchmark functions<p>suites show that UACOR and iCMAES-ILS are competitive or superior to other<p>state-of-the-art algorithms.<p>To tackle mixed discrete-continuous optimization problems, we extend ACOMV <p>and propose CESMV, an ant colony optimization algorithm and a covariance matrix adaptation evolution strategy, respectively. In ACOMV and CESMV ,the decision variables of an optimization problem can be declared as continuous, ordinal, or categorical, which allows the algorithm to treat them adequately. ACOMV and<p>CESMV include three solution generation mechanisms: a continuous optimization<p>mechanism, a continuous relaxation mechanism for ordinal variables, and a categorical optimization mechanism for categorical variables. Together, these mechanisms allow ACOMV and CESMV to tackle mixed variable optimization problems.<p>We also propose a set of artificial, mixed-variable benchmark functions, which can<p>simulate discrete variables as ordered or categorical. We use them to automatically tune ACOMV and CESMV's parameters and benchmark their performance.<p>Finally we test ACOMV and CESMV on various real-world continuous and mixed-variable engineering optimization problems. Comparisons with results from the<p>literature demonstrate the effectiveness and robustness of ACOMV and CESMV<p>on mixed-variable optimization problems.<p>Apart from these main contributions, during my doctoral research I have accomplished a number of additional contributions, which concern (i) a note on the<p>bound constraints handling for the CEC'05 benchmark set, (ii) computational results for an automatically tuned IPOP-CMA-ES on the CEC'05 benchmark set and<p>(iii) a study of artificial bee colonies for continuous optimization. These additional<p>contributions are to be found in the appendix to this thesis.<p><br>Doctorat en Sciences de l'ingénieur<br>info:eu-repo/semantics/nonPublished
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Leon, Miguel. "Enhancing Differential Evolution Algorithm for Solving Continuous Optimization Problems." Licentiate thesis, Mälardalens högskola, Inbyggda system, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-33466.

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Differential Evolution (DE) has become one of the most important metaheuristics during the recent years, obtaining attractive results in solving many engineering optimization problems. However, the performance of DE is not always strong when seeking optimal solutions. It has two major problems in real world applications. First, it can easily get stuck in a local optimum or fail to generate better solutions before the population has converged. Secondly, its performance is significantly influenced by the control parameters, which are problem dependent and which vary in different regions of space under exploration.  It usually entails a time consuming trial-and-error procedure to set suitable parameters for DE in a specific problem, particularly for those practioners with limited knowledge and experience of using this technique.   This thesis aims to develop new DE algorithms to address the two aforementioned problems. To mitigate the first problem, we studied the hybridization of DE with local search techniques to enhance the efficiency of search. The main idea is to apply a local search mechanism to the best individual in each generation of DE to exploit the most promising regions during the evolutionary processs so as to speed up the convergence or increase the chance to scape from local optima. Four local search strategies have been integrated  and tested in the global DE framework, leading to variants of the memetic DE algorithms with different properties concerning diversification and intensification. For tackling the second problem, we propose a greedy adaptation method for dynamic adjustment of the control parameters in DE. It is implemented by conducting greedy search repeatedly during the run of DE to reach better parameter assignments in the neighborhood of a current candidate. The candidates are assessed by considering both, the success rate and also fitness improvement of trial solutions against the target ones. The incorporation of this greedy parameter adaptation method into standard DE has led to a new adaptive DE algorithm, referred to as Greedy Adaptive Differential Evolution (GADE).   The methods proposed in this thesis have been tested in different benchmark problems and compared with the state of the art algorithms, obtaining competitive results. Furthermore, the proposed GADE algorithm has been applied in an industrial scenario achieving more accurate results than those obtained by a standard DE algorithm.<br>Differential Evolution (DE) har blivit en av de viktigaste metaheuristikerna under de senaste åren och har uppnått attraktiva resultat för att lösa många optimeringsproblem inom teknik. Dock är prestationen hos DE inte alltid framgångsrik när man söker optimala lösningar. Det finns två huvudsakliga problem för applikationer i den verkliga världen. Det första är att den lätt kan fastna i lokala optimum eller misslyckas att generera bättre lösningar före det att populationen (en grupp av lösningar) har hunnit konvergera. Det andra är att prestandan påverkas märkvärdigt av kontrollparametrar, vilkas optimala värden beror på problem som ska lösas och varierar mellan regioner i sökrymden. Detta innebär oftast ett tidskrävande trial-and-error förfarande för att hitta lämpliga parametrar till ett specifikt DE-problem, framför allt för utövare med begränsad kunskap och erfarenhet av DE.   Syftet med denna licentiatavhandling är att utveckla nya DE-algoritmer för att behandla de ovannämnda problemen. För att möta det första problemet så studerades hybridisering av DE och lokala söktekniker för att effektivisera sökningen. Tanken är att använda en lokal sökmekanism på den bästa individen i varje generation i DE-algoritmen och utnyttja de mest lovande regionerna under evolutionsprocessen för att snabba på konvergensen eller öka chansen att undvika lokala optimum. Fyra lokala sökstrategier har integrerats och testats i det globala DE-ramverket vilket har lett till fyra varianter av DE-algoritmerna med olika egenskaper beträffande diversifiering och intensifiering. Till det andra problemet föreslås en greedy adaptation method för dynamisk justering av kontrollparametrarna i DE. Den implementeras genom att utföra greedy search upprepade gånger under körningen av DE för att hitta bättre värden till kontrollparametrarna. Utvärderingen av parameterval baseras på både success rate och fitness improvement av trial lösningar jämfört med target lösningar. Sammanslagningen av DE och denna greedy parameter adaptation har lett till en ny adaptiv DE-algoritm som kallas Greedy Adaptive Differential Evolution (GADE).   Den föreslagna metoden i denna licentiatavhandling har testats i olika prestandamätningar och jämförts med state-of-the-art-algoritmer, med goda resultat. Dessutom har den föreslagna GADE-algoritmen använts i ett industriellt scenario och uppnådde då mer exakta resultat än den med en standard DE-algoritm.
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Schutte, Jaco Francois. "Applications of parallel global optimization to mechanics problems." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012932.

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Koullias, Stefanos. "Methodology for global optimization of computationally expensive design problems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49085.

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The design of unconventional aircraft requires early use of high-fidelity physics-based tools to search the unfamiliar design space for optimum designs. Current methods for incorporating high-fidelity tools into early design phases for the purpose of reducing uncertainty are inadequate due to the severely restricted budgets that are common in early design as well as the unfamiliar design space of advanced aircraft. This motivates the need for a robust and efficient global optimization algorithm. This research presents a novel surrogate model-based global optimization algorithm to efficiently search challenging design spaces for optimum designs. The algorithm searches the design space by constructing a fully Bayesian Gaussian process model through a set of observations and then using the model to make new observations in promising areas where the global minimum is likely to occur. The algorithm is incorporated into a methodology that reduces failed cases, infeasible designs, and provides large reductions in the objective function values of design problems. Results on four sets of algebraic test problems are presented and the methodology is applied to an airfoil section design problem and a conceptual aircraft design problem. The method is shown to solve more nonlinearly constrained algebraic test problems than state-of-the-art algorithms and obtains the largest reduction in the takeoff gross weight of a notional 70-passenger regional jet versus competing design methods.
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Pham, Viet. "A global optimization approach to pooling problems in refineries." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1445.

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Books on the topic "Continuous global optimization problems"

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Kearfott, R. Baker. Rigorous global search: Continuous problems. Kluwer Academic Publishers, 1996.

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Kearfott, R. Baker. Rigorous Global Search: Continuous Problems. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2495-0.

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1946-, Woźniakowski Henryk, and European Mathematical Society, eds. Essays on the complexity of continuous problems. European Mathematical Society, 2009.

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Montrucchio, Luigi. Lipschitz continuous policy functions for strongly concave optimization problems. Institute for Mathematical Studies in the Social Sciences, Stanford University, 1987.

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Pintér, J. Global optimization in action: Continuous and Lipschitz optimization--algorithms, implementations, and applications. Kluwer Academic Publishers, 1996.

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1948-, Stoffa Paul L., ed. Global optimization methods in geophysical inversion. Elsevier, 1995.

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Tawarmalani, Mohit, and Nikolaos V. Sahinidis. Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3532-1.

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Floudas, Christodoulos A. Handbook of Test Problems in Local and Global Optimization. Springer US, 1999.

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Floudas, Christodoulos A., Pãnos M. Pardalos, Claire S. Adjiman, et al. Handbook of Test Problems in Local and Global Optimization. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-3040-1.

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Floudas, Christodoulos A. A collection of test problems for constrained global optimization algorithms. Springer-Verlag, 1990.

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Book chapters on the topic "Continuous global optimization problems"

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Kearfott, R. Baker. "Optimization." In Rigorous Global Search: Continuous Problems. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2495-0_5.

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Chauhan, Pinkey, Millie Pant, and Kusum Deep. "Novel Binary PSO for Continuous Global Optimization Problems." In Advances in Intelligent and Soft Computing. Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0487-9_17.

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Bae, Egil, Jing Yuan, Xue-Cheng Tai, and Yuri Boykov. "A Fast Continuous Max-Flow Approach to Non-convex Multi-labeling Problems." In Efficient Algorithms for Global Optimization Methods in Computer Vision. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54774-4_7.

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Sharma, Tarun Kumar, and Millie Pant. "Enhancing Different Phases of Artificial Bee Colony for Continuous Global Optimization Problems." In Advances in Intelligent and Soft Computing. Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0487-9_68.

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Haddad, Emile. "Nonconvex Global Optimization of the Separable Resource Allocation Problem with Continuous Variables." In Nonconvex Optimization and Its Applications. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-3437-8_24.

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Benyoub, Abdeljalil, and El Mostafa Daoudi. "Parallelization of the Continuous Global Optimization Problem with Inequality Constraints by Using Interval Arithmetic⋆." In High-Performance Computing and Networking. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48228-8_68.

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Horst, Reiner, and Hoang Tuy. "Lipschitz and Continuous Optimization." In Global Optimization. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02947-3_11.

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Horst, Reiner, and Hoang Tuy. "Lipschitz and Continuous Optimization." In Global Optimization. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03199-5_11.

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Horst, Reiner, and Hoang Tuy. "Lipschitz and Continuous Optimization." In Global Optimization. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02598-7_11.

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Evtushenko, Yu G., and M. A. Potapov. "Deterministic Global Optimization." In Algorithms for Continuous Optimization. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-009-0369-2_17.

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Conference papers on the topic "Continuous global optimization problems"

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Yanxia, Niu, and Zhao Hengjun. "Generalized Filled Function for Global Continuous Optimization Problems." In 2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2012. http://dx.doi.org/10.1109/cso.2012.87.

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Ogot, Madara, and Sherif Aly. "Discretizing Continuous Problems for Faster Global Convergence." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dac-48798.

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Global optimization of mechanical design problems using heuristic methods such as Simulated annealing (SA) and genetic algorithms (GAs) have been able to find global or near-global minima where prior methods have failed. The use of these nongradient based methods allow the broad efficient exploration of multimodal design spaces that could be continuous, discrete or mixed. From a survey of articles in the ASME Journal of Mechanical Design over the last 10 years, we have observed that researchers will typically run these algorithms in continuous mode for problems that contain continuous design variables. What we suggest in this paper is that computational efficiencies can be significantly increased by discretizing all continuous variables, perform a global optimization on the discretized design space, and then conduct a local search in the continuous space from the global minimum discrete state. The level of discretization will depend on the complexity of the problem, and becomes an additional parameter that needs to be tuned. The rational behind this assertion is presented, along with results from four test problems.
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Kazemi, Moslem, G. Gary Wang, Shahryar Rahnamayan, and Kamal Gupta. "Constraint Importance Mode Pursuing Sampling for Continuous Global Optimization." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28355.

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Many engineering design problems deal with global optimization of constrained black-box problems which is usually computation-intensive. Ref. [1] proposed a Mode-Pursuing Sampling (MPS) method for global optimization based on a sampling technique which systematically generates more sample points in the neighborhood of the function mode while statistically covering the entire problem domain. In this paper, we propose a novel and more efficient sampling technique which greatly enhances the performance of the MPS method, especially in the presence of expensive constraints. The effective sampling of the search space is attained via biasing the sample points towards feasible regions and being away from the forbidden regions. This is achieved by utilizing the incrementally obtained information about the constraints, hence, it is called Constraint-importance Mode Pursuing Sampling (CiMPS). According to intensive comparisons and experimental verifications, the new sampling technique is found to be more efficient in solving constrained optimization problems compared to the original MPS method. To the best of our knowledge, this is the first metamodel-based global optimization method that directly aims at reducing the number of function evaluations for both expensive objective functions and constraints.
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Anescu, George. "An Imperialistic Strategy Approach to Continuous Global Optimization Problem." In 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2014. http://dx.doi.org/10.1109/synasc.2014.79.

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Zhang, Qin, and Xiong-hai Wang. "Binary and Continuous Ant Colony Algorithms Research for Solving Continuous Global Optimization Problem." In 2008 First International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2008. http://dx.doi.org/10.1109/icinis.2008.12.

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Zhai, Junchang, Yuping Qin, and Ping Zhang. "A Modified Global Harmony Search Algorithm with Random Crossover for Continuous High Dimensional Optimization Problems." In 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019. http://dx.doi.org/10.1109/ccdc.2019.8833127.

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Zheng, Kaifeng, Chao Wang, Weizheng An, and Likun Ge. "A Hybrid Method Combining Tab Search and Nelder-Mead Algorithms for Global Continuous Optimization Problems." In 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2018. http://dx.doi.org/10.1109/cscwd.2018.8465387.

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Pomrehn, Leonard P., and Panos Y. Papalambros. "Constraint Activity Revisited: Application to Global and Discrete Design Optimization." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0125.

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Abstract The concept of constraint activity, widely used throughout the optimization literature, is extended and clarified to deal with global optimization problems containing either continuous or discrete variables. The article begins by presenting definitions applicable to individual constraints, followed by definitions of groups of constraints. Concepts are reinforced through the use of examples. The definitions are used to investigate the ideas of degrees of freedom, optimization “cases,” and monotonicity analysis, as applied to global and discrete problems. Applicability to local optimization is also noted.
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Wang, Yan. "Global Optimization With Quantum Walk Enhanced Grover Search." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34634.

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One of the significant breakthroughs in quantum computation is Grover’s algorithm for unsorted database search. Recently, the applications of Grover’s algorithm to solve global optimization problems have been demonstrated, where unknown optimum solutions are found by iteratively improving the threshold value for the selective phase shift operator in Grover rotation. In this paper, a hybrid approach that combines continuous-time quantum walks with Grover search is proposed. By taking advantage of quantum tunneling effect, local barriers are overcome and better threshold values can be found at the early stage of search process. The new algorithm based on the formalism is demonstrated with benchmark examples of global optimization. The results between the new algorithm and the Grover search method are also compared.
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Yalamanchili, Vijay Krishna, and Ashok V. Kumar. "Topology Optimization of Structures Using a Global Stress Measure." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71546.

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An approach for stress based topology optimization is studied here where stress constraints for continuum structures are imposed using a conservative global stress measure. A relation between the mean compliance and Von-Mises stress is used to construct an objective function that minimizes mass until stress constraints are activated. This approach is implemented in a mesh independent finite element framework where the feasible region is defined using boundary equations while analysis and topology optimization are performed on a background mesh. The SIMP approach is used for topology optimization and nodal values of density are treated as the design variables. The density field is interpolated over the elements to obtain a continuous distribution. To ensure smooth boundaries a smoothing term is also added to the objective function which minimizes gradient of the density function. The optimization problem is then solved using Moving Barrier Method (MBM). Several examples are studied to evaluate this approach.
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Reports on the topic "Continuous global optimization problems"

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

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

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

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McCormick, Garth P. Global solutions to factorable nonlinear optimization problems using separable programming techniques. National Bureau of Standards, 1985. http://dx.doi.org/10.6028/nbs.ir.85-3206.

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Byrd, Richard H., Elizabeth Eshow, and Robert B. Schnabel. A New Large-Scale Global Optimization Method and Its Application to Lennard-Jones Problems. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada454694.

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AL-Khayyal, Fais A., Reiner Horst, and Panos M. Pardalos. Global Optimization of Concave Functions Subject to Separable Quadratic Constraints and of All-Quadratic Separable Problems. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada197747.

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Gao, David Y. Canonical Duality Theory and Algorithms for Solving Some Challenging Problems in Global Optimization and Decision Science. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada628112.

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Byrd, Richard H., Thomas Derby, Elizabeth Eskow, Klaas P. Oldenkamp, and Robert B. Schnabel. A New Stochastic/Perturbation Method for Large-Scale Global Optimization and Its Application to Water Cluster Problems. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada446616.

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Financial Stability Report - First Semester of 2020. Banco de la República de Colombia, 2021. http://dx.doi.org/10.32468/rept-estab-fin.1sem.eng-2020.

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In the face of the multiple shocks currently experienced by the domestic economy (resulting from the drop in oil prices and the appearance of a global pandemic), the Colombian financial system is in a position of sound solvency and adequate liquidity. At the same time, credit quality has been recovering and the exposure of credit institutions to firms with currency mismatches has declined relative to previous episodes of sudden drops in oil prices. These trends are reflected in the recent fading of red and blue tonalities in the performance and credit risk segments of the risk heatmaps in Graphs A and B.1 Naturally, the sudden, unanticipated change in macroeconomic conditions has caused the appearance of vulnerabilities for short-term financial stability. These vulnerabilities require close and continuous monitoring on the part of economic authorities. The main vulnerability is the response of credit and credit risk to a potential, temporarily extreme macroeconomic situation in the context of: (i) recently increased exposure of some banks to household sector, and (ii) reductions in net interest income that have led to a decline in the profitability of the banking business in the recent past. Furthermore, as a consequence of greater uncertainty and risk aversion, occasional problems may arise in the distribution of liquidity between agents and financial markets. With regards to local markets, spikes have been registered in the volatility of public and private fixed income securities in recent weeks that are consistent with the behavior of the international markets and have had a significant impact on the liquidity of those instruments (red portions in the most recent past of some market risk items on the map in Graph A). In order to adopt a forward-looking approach to those vulnerabilities, this Report presents a stress test that evaluates the resilience of credit institutions in the event of a hypothetical scenario thatseeks to simulate an extreme version of current macroeconomic conditions. The scenario assumes a hypothetical negative growth that is temporarily strong but recovers going into the middle of the coming year and has extreme effects on credit quality. The results suggest that credit institutions have the ability to withstand a significant deterioration in economic conditions in the short term. Even though there could be a strong impact on credit, liquidity, and profitability under the scenario being considered, aggregate capital ratios would probably remain at above their regulatory limits over the horizon of a year. In this context, the recent measures taken by both Banco de la República and the Office of the Financial Superintendent of Colombia that are intended to help preserve the financial stability of the Colombian economy become highly relevant. In compliance with its constitutional objectives and in coordination with the financial system’s security network, Banco de la República will continue to closely monitor the outlook for financial stability at this juncture and will make the decisions that are necessary to ensure the proper functioning of the economy, facilitate the flow of sufficient credit and liquidity resources, and further the smooth functioning of the payment system. Juan José Echavarría Governor
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