Literatura académica sobre el tema "METAHEURISTIC APPROACH"
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Artículos de revistas sobre el tema "METAHEURISTIC APPROACH"
Laudis, Lalin L. y Amit Kumar Sinha. "Metaheuristic Approach for VLSI 3D-Floorplanning". International Journal of Scientific Research 2, n.º 12 (1 de junio de 2012): 202–3. http://dx.doi.org/10.15373/22778179/dec2013/62.
Texto completoLEE, YOUNG CHOON, JAVID TAHERI y ALBERT Y. ZOMAYA. "A PARALLEL METAHEURISTIC FRAMEWORK BASED ON HARMONY SEARCH FOR SCHEDULING IN DISTRIBUTED COMPUTING SYSTEMS". International Journal of Foundations of Computer Science 23, n.º 02 (febrero de 2012): 445–64. http://dx.doi.org/10.1142/s0129054112400229.
Texto completoBajenaru, Victor, Steven Lavoie, Brett Benyo, Christopher Riker, Mitchell Colby y James Vaccaro. "Recommender System Metaheuristic for Optimizing Decision-Making Computation". Electronics 12, n.º 12 (14 de junio de 2023): 2661. http://dx.doi.org/10.3390/electronics12122661.
Texto completoRosłon, Jerzy Hubert y Janusz Edward Kulejewski. "A hybrid approach for solving multi-mode resource-constrained project scheduling problem in construction". Open Engineering 9, n.º 1 (31 de enero de 2019): 7–13. http://dx.doi.org/10.1515/eng-2019-0006.
Texto completoCorreia, Sérgio D., Marko Beko, Luis A. Da Silva Cruz y Slavisa Tomic. "Elephant Herding Optimization for Energy-Based Localization". Sensors 18, n.º 9 (29 de agosto de 2018): 2849. http://dx.doi.org/10.3390/s18092849.
Texto completoTalatahari, Babak, Mahdi Azizi, Siamak Talatahari, Mohamad Tolouei y Pooya Sareh. "Crystal structure optimization approach to problem solving in mechanical engineering design". Multidiscipline Modeling in Materials and Structures 18, n.º 1 (1 de marzo de 2022): 1–23. http://dx.doi.org/10.1108/mmms-10-2021-0174.
Texto completoCruz-Duarte, Jorge M., José C. Ortiz-Bayliss, Iván Amaya, Yong Shi, Hugo Terashima-Marín y Nelishia Pillay. "Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems". Mathematics 8, n.º 11 (17 de noviembre de 2020): 2046. http://dx.doi.org/10.3390/math8112046.
Texto completoBarraza, Juan, Luis Rodríguez, Oscar Castillo, Patricia Melin y Fevrier Valdez. "A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm". Journal of Optimization 2018 (27 de mayo de 2018): 1–18. http://dx.doi.org/10.1155/2018/6495362.
Texto completoYaghini, Masoud, Mohsen Momeni y Mohammadreza Sarmadi. "A DIMMA-Based Memetic Algorithm for 0-1 Multidimensional Knapsack Problem Using DOE Approach for Parameter Tuning". International Journal of Applied Metaheuristic Computing 3, n.º 2 (abril de 2012): 43–55. http://dx.doi.org/10.4018/jamc.2012040104.
Texto completoWahab, Hala Bahjat Abdul, Suhad Malallah Kadhem y Estabraq Abdul Redaa Kadhim. "Proposed Approach for Elliptic Curve Cryptography Based on Metaheuristic Algorithms". International Journal of Scientific Research 2, n.º 10 (1 de junio de 2012): 1–5. http://dx.doi.org/10.15373/22778179/oct2013/33.
Texto completoTesis sobre el tema "METAHEURISTIC APPROACH"
Zhao, Jian-Hua. "The reliability optimization of mechanical systems using metaheuristic approach". Mémoire, École de technologie supérieure, 2005. http://espace.etsmtl.ca/326/1/ZHAO_Jian%2DHua.pdf.
Texto completoZamperin, Filippo <1994>. "Testing standard technical analysis parameters' efficiency, a metaheuristic approach". Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17564.
Texto completoCUNHA, VICTOR ABU-MARRUL CARNEIRO DA. "RESCHEDULING OF OIL EXPLORATION SUPPORT VESSELS WITHIN A METAHEURISTIC APPROACH". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30908@1.
Texto completoCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
A dissertação aborda um problema real de reprogramação de uma frota de embarcações do tipo PLSV (Pipe Laying Support Vessel), responsáveis pelas interligações de poços petrolíferos submarinos. O cronograma de curto prazo dessas embarcações está sujeito à inúmeras incertezas inerentes às operações realizadas, acarretando em ociosidade nas embarcações ou postergações na produção de petróleo, que podem resultar em prejuízo de milhões de reais. Uma metaheurística ILS (Iterated Local Search) é proposta para atender a frequente demanda por reprogramações dos PLSVs. O método é composto de uma fase inicial de viabilização, para tratar potenciais inconsistências nas programações. Na sequência, iterativamente, são realizadas perturbações na solução por meio de movimentos de swap e aplicada uma busca local baseada na vizinhança insert, a fim de fugir de ótimos locais e encontrar soluções que aprimorem o cronograma. Foram feitos experimentos com diferentes parâmetros e critérios do ILS, sendo definidas duas abordagens aplicadas a dez instâncias oriundas de uma programação real de PLSVs. A partir de uma função de avaliação, capaz de medir o impacto operacional na programação, o ILS proporcionou uma melhoria média nos cronogramas acima de 91 por cento, quando comparados aos cronogramas originais. As soluções foram obtidas em um tempo computacional médio de 30 minutos, aderente ao processo da companhia. Em função dos resultados alcançados, o método provou ser uma boa base para uma ferramenta de apoio à decisão para a reprogramação dos PLSVs.
This dissertation addresses a real-life rescheduling problem of a Pipe Laying Support Vessels (PLSVs) fleet, in charge of subsea oil wells interconnections. The short-term schedule of these vessels is subject to uncertainties inherent to its operations, resulting in ships idleness or delays in oil production, which may lead to losses of millions of Brazilian Reais. A method based on the ILS (Iterated Local Search) metaheuristic is proposed to meet the frequent demand of PLSVs rescheduling. The first step of this method aims to find a feasible initial solution from an incoming schedule with potencial inconsistencies. The following steps consists in, iteratively, performing a perturbation on a solution through swap movements and applying a local search based on the insertion neighborhood, in order to escape from local optimal and find better solutions. Extensive preliminary experiments were conducted considering different ILS parameters setups. The two most performing setups were selected and applied to ten instances of a real PLSV schedule. Taking into account an objective function that measures the operational impact on schedules, the ILS provided an average improvement above 91 percent in schedules when compared to the original planning. These solutions were obtained in an average computational time of 30 minutes, which fits in the company process. The obtained results showed that the proposed method might be a basis for a decision support tool for the PLSVs rescheduling problem.
FADDA, GIANFRANCO. "A metaheuristic approach for the Vehicle Routing Problem with Backhauls". Doctoral thesis, Università degli Studi di Cagliari, 2017. http://hdl.handle.net/11584/249582.
Texto completoAhmad, Maqsood. "Mathematical models and methods based on metaheuristic approach for timetabling problem". Thesis, Clermont-Ferrand 2, 2013. http://www.theses.fr/2013CLF22393/document.
Texto completoIn this thesis we have concerned ourselves with university timetabling problems both course timetabling and examination timetabling problems. Most of the timetabling problems are computationally NP-complete problems, which means that the amount of computation required to find solutions increases exponentially with problem size. These are idiosyncratic nature problems, for example different universities have their own set of constraints, their own definition of good timetable, feasible timetable and their own choice about the use of constraint type (as a soft or hard constraint). Unfortunately, it is often the case that a problem solving approach which is successfully applied for one specific problem may not become suitable for others. This is a motivation, we propose a generalized problem which covers many constraints used in different universities or never used in literature. Many university timetabling problems are sub problems of this generalized problem. Our proposed algorithms can solve these sub problems easily, moreover constraints can be used according to the desire of user easily because these constraints can be used as reference to penalty attached with them as well. It means that give more penalty value to hard constraints than soft constraint. Thus more penalty value constraints are dealt as a hard constraint by algorithm. Our algorithms can also solve a problem in two phases with little modification, where in first phase hard constraints are solved. In this work we have preferred and used two phase technique to solve timetabling problems because by using this approach algorithms have broader search space in first phase to satisfy hard constraints while not considering soft constraints at all. Two types of algorithms are used in literature to solve university timetabling problem, exact algorithms and approximation algorithms. Exact algorithms are able to find optimal solution, however in university timetabling problems exact algorithms constitute brute-force style procedures. And because these problems have the exponential growth rates of the search spaces, thus these kinds of algorithms can be applied for small size problems. On the other side, approximation algorithms may construct optimal solution or not but they can produce good practically useable solutions. Thus due to these factors we have proposed approximation algorithms to solve university timetabling problem. We have proposed metaheuristic based techniques to solve timetabling problem, thus we have mostly discussed metaheuristic based algorithms such as evolutionary algorithms, simulated annealing, tabu search, ant colony optimization and honey bee algorithms. These algorithms have been used to solve many other combinatorial optimization problems other than timetabling problem by modifying a general purpose algorithmic framework. We also have presented a bibliography of linear integer programming techniques used to solve timetabling problem because we have formulated linear integer programming formulations for our course and examination timetabling problems. We have proposed two stage algorithms where hard constraints are satisfied in first phase and soft constraints in second phase. The main purpose to use this two stage technique is that in first phase hard constraints satisfaction can use more relax search space because in first phase it does not consider soft constraints. In second phase it tries to satisfy soft constraints when maintaining hard constraints satisfaction which are already done in first phase. (...)
Kuang, Yue(Yue Rick). "A metaheuristic approach to optimizing a multimodal truck and drone delivery system". Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122401.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 50-51).
The success of e-commerce continues to push the bounds of delivery services as customers expect near instant fulfillment at little additional cost. This demand for delivery performance and operational cost efficiency has led to the exploration of the last-mile delivery problem using creative multimodal delivery systems. One promising system consists of a truck that can carry and deploy multiple autonomous drones to assist in the fulfillment of customer demand. The contribution of this thesis is towards furthering the understanding of the application of autonomous flying drones in such a system and improve parcel delivery performance within the constraint of the current state of technology. This thesis explores the feasibility of deploying drones in last-mile delivery by modeling and then optimizing the cost of serving customers with a system consisting of one truck and multiple drones under multiple customer demand scenarios. While this optimization problem can be solved with mixed integer linear programming (MILP), the computation requirement is such that MILP is inefficient for real world scenarios with 100 or more customers. This research applies metaheuristic methodology to solve the truck-and-drone problem for scenarios with up to 158 customers in approximately 30 minutes of computation time. The test results confirm an average of 7% to 9% in savings opportunity for a 2-drone baseline over traditional single truck delivery tours. This savings opportunity is shown to vary with customer density, number of drones carried, range of drone flight, and speed of drone relative to speed of truck.
by Yue Kuang.
M. Eng. in Supply Chain Management
M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program
Saremi, Alireza. "Mathematical programming enhanced metaheuristic approach for simulation-based optimization in outpatient appointment scheduling". Elsevier, 2013. http://hdl.handle.net/1993/21710.
Texto completoAlvarez, Fernandez Stephanie Milena. "A metaheuristic and simheuristic approach for the p-HUB median problem from a telecommunication perspective". Doctoral thesis, Universitat Oberta de Catalunya, 2018. http://hdl.handle.net/10803/666752.
Texto completoLos recientes avances en la industria de las telecomunicaciones ofrecen grandes oportunidades para ciudadanos y organizaciones en un mundo globalmente conectado, pero también presentan una gran cantidad de desafíos complejos a los que se enfrentan diariamente técnicos e ingenieros. Algunos de estos desafíos se pueden modelar como problemas de optimización. El primer objetivo de esta tesis es proporcionar una revisión de la literatura de cómo se han utilizado estas técnicas tradicionalmente para tratar los problemas de optimización asociados a sistemas de telecomunicaciones, detectando las principales tendencias y desafíos. En particular, el estudio se centra en los problemas de diseño de red, direccionamiento y problemas de asignación de recursos. Debido a la naturaleza de estos problemas, este trabajo también analiza cómo se pueden combinar las técnicas metaheurísticas con metodologías de simulación para ampliar las capacidades de resolver problemas de optimización estocásticos. Después se trata un popular problema de optimización con aplicaciones prácticas para redes de telecomunicaciones, el problema de la p mediana no capacitado, analizándolo desde escenarios deterministas y estocásticos.
Recent advances in the telecommunications industry offer great opportunities to citizens and organizations in a globally-connected world, but they also create a vast number of complex challenges that decision-makers must face. Some of these challenges can be modelled as optimization problems. First, this thesis reviews how metaheuristics have been used to date to deal with optimization problems associated with telecommunication systems, detecting the main trends and challenges. In particular, the analysis focuses on problems in network design, routing, and allocation. Given the nature of these challenges, this work also discusses how the hybridization of metaheuristics with methodologies such as simulation can be employed to increase metaheuristics' capabilities when solving stochastic optimization problems. In addition, a popular optimization problem with practical applications in the design of telecommunications networks, the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP) – where a fixed number of hubs have unlimited capacity, each non-hub node is allocated to a single hub and the number of hubs is known in advance –, is analysed in deterministic and stochastic scenarios.
Güttinger, Dennis [Verfasser], Johannes [Akademischer Betreuer] Fürnkranz y Karsten [Akademischer Betreuer] Weihe. "A New Metaheuristic Approach for Stabilizing the Solution Quality of Simulated Annealing and Applications / Dennis Güttinger. Betreuer: Johannes Fürnkranz ; Karsten Weihe". Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2013. http://d-nb.info/110645376X/34.
Texto completoXu, Ying. "Metaheuristic approaches for QoS multicast routing problems". Thesis, University of Nottingham, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546470.
Texto completoLibros sobre el tema "METAHEURISTIC APPROACH"
Jana, Nanda Dulal, Swagatam Das y Jaya Sil. A Metaheuristic Approach to Protein Structure Prediction. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74775-0.
Texto completoChristian, Blum, ed. Hybrid metaheuristics: An emerging approach to optimization. Berlin: Springer, 2008.
Buscar texto completoBandyopadhyay, Sanghamitra. Unsupervised classification: Similarity measures, classical and metaheuristic approaches, and applications. Berlin: Springer, 2013.
Buscar texto completoCognitive Big Data Intelligence with a Metaheuristic Approach. Elsevier, 2022. http://dx.doi.org/10.1016/c2020-0-02004-9.
Texto completoSavsani, Vimal J., Vivek K. Patel y Mohamed A. Tawhid. Thermal System Optimization: A Population-Based Metaheuristic Approach. Springer, 2019.
Buscar texto completoBlondin, Maude Josée. Controller Tuning Optimization Methods for Multi-Constraints and Nonlinear Systems: A Metaheuristic Approach. Springer International Publishing AG, 2021.
Buscar texto completoDas, Swagatam, Nanda Dulal Jana y Jaya Sil. A Metaheuristic Approach to Protein Structure Prediction: Algorithms and Insights from Fitness Landscape Analysis. Springer, 2018.
Buscar texto completoDas, Swagatam, Nanda Dulal Jana y Jaya Sil. A Metaheuristic Approach to Protein Structure Prediction: Algorithms and Insights from Fitness Landscape Analysis. Springer, 2018.
Buscar texto completoMetaheuristic Approaches to Portfolio Optimization. IGI Global, 2020.
Buscar texto completoRay, Jhuma, Sadhan Kumar Dey, Goran Klepac y Anirban Mukherjee. Metaheuristic Approaches to Portfolio Optimization. IGI Global, 2019.
Buscar texto completoCapítulos de libros sobre el tema "METAHEURISTIC APPROACH"
Caserta, Marco y Stefan Voß. "Workgroups Diversity Maximization: A Metaheuristic Approach". En Hybrid Metaheuristics, 118–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38516-2_10.
Texto completoJana, Nanda Dulal, Swagatam Das y Jaya Sil. "Hybrid Metaheuristic Approach for Protein Structure Prediction". En Emergence, Complexity and Computation, 197–206. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74775-0_7.
Texto completoCarotenuto, Pasquale, Graziano Galiano y Stefano Giordani. "A Metaheuristic Approach for Hazardous Materials Transportation". En Operations Research Proceedings, 119–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17022-5_16.
Texto completoPan, Hongqi y Chung-Hsing Yeh. "A Metaheuristic Approach to Fuzzy Project Scheduling". En Lecture Notes in Computer Science, 1081–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45224-9_145.
Texto completoCergibozan, Çağla y A. Serdar Tasan. "Tourist Route Planning with a Metaheuristic Approach". En Lecture Notes in Management and Industrial Engineering, 193–99. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58409-6_22.
Texto completoĆirković, Petar, Predrag Đorđević, Miloš Milićević y Tatjana Davidović. "Metaheuristic Approach to Spectral Reconstruction of Graphs". En Mathematical Optimization Theory and Operations Research, 79–93. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09607-5_6.
Texto completoYedurkar, Dhanalekshmi P. y Shilpa P. Metkar. "EEG Analysis Using Bio-Inspired Metaheuristic Approach". En Evolving Role of AI and IoMT in the Healthcare Market, 33–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82079-4_2.
Texto completoKunche, Prajna y K. V. V. S. Reddy. "Speech Enhancement Approach Based on Gravitational Search Algorithm (GSA)". En Metaheuristic Applications to Speech Enhancement, 61–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_6.
Texto completoKunche, Prajna y K. V. V. S. Reddy. "Speech Enhancement Approach Based on Accelerated Particle Swarm Optimization (APSO)". En Metaheuristic Applications to Speech Enhancement, 39–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_5.
Texto completoLappas, P. Z., S. Z. Xanthopoulos y A. N. Yannacopoulos. "Metaheuristic-Based Machine Learning Approach for Customer Segmentation". En Metaheuristics for Machine Learning, 101–33. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3888-7_4.
Texto completoActas de conferencias sobre el tema "METAHEURISTIC APPROACH"
Ghasemzadeh, Hamzeh. "A metaheuristic approach for solving jigsaw puzzles". En 2014 Iranian Conference on Intelligent Systems (ICIS). IEEE, 2014. http://dx.doi.org/10.1109/iraniancis.2014.6802604.
Texto completoJoseph, Caroline S., Vaishnavi Kini M, Sunad Suhas, Dherya Nagori, Arti Arya y Pooja Agarwal. "Metaheuristic Approach for Optimizing Supply-Demand Algorithms". En 2022 IEEE 7th International conference for Convergence in Technology (I2CT). IEEE, 2022. http://dx.doi.org/10.1109/i2ct54291.2022.9824600.
Texto completoMansor, Mohd Asyraf, Saratha Sathasivam y Mohd Shareduwan Mohd Kasihmuddin. "Enhanced metaheuristic approach in pattern satisfiability problem". En PROCEEDING OF THE 25TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM25): Mathematical Sciences as the Core of Intellectual Excellence. Author(s), 2018. http://dx.doi.org/10.1063/1.5041557.
Texto completoPal, Siddharth, Anniruddha Basak, Swagatam Das, Ajith Abraham y Vaclav Snasel. "Automatic shell clustering using a metaheuristic approach". En 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5641913.
Texto completoZhang, Yuanyuan y Ming Chen. "A Metaheuristic Approach for the Frequency Assignment Problem". En 2010 6th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2010. http://dx.doi.org/10.1109/wicom.2010.5600818.
Texto completoJohari, Rahul y Dhari Ali Mahmood. "GAACO: Metaheuristic driven approach for routing in OppNet". En 2014 Global Summit on Computer & Information Technology (GSCIT). IEEE, 2014. http://dx.doi.org/10.1109/gscit.2014.6970129.
Texto completoAdriaensen, Steven, Tim Brys y Ann Nowe. "Designing reusable metaheuristic methods: A semi-automated approach". En 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900575.
Texto completoMohamadi, Hamid, Jafar Habibi y Mohammad Saniee Abadeh. "Misuse Intrusion Detection Using a Fuzzy-Metaheuristic Approach". En 2008 Second Asia International Conference on Modelling & Simulation (AMS). IEEE, 2008. http://dx.doi.org/10.1109/ams.2008.128.
Texto completoHussein, Ahmed, Heba Mostafa, Mohamed Badrel-din, Osama Sultan y Alaa Khamis. "Metaheuristic optimization approach to mobile robot path planning". En 2012 International Conference on Engineering and Technology (ICET). IEEE, 2012. http://dx.doi.org/10.1109/icengtechnol.2012.6396150.
Texto completoShalchian, Hengameh, Mohammad-Hadi Sotoudeh, Habib G. Khosroshahi, Reza Ravanmehr, Surena Fatemi y Hamed Altafi. "A metaheuristic approach for INO340 telescope flexible scheduling". En Software and Cyberinfrastructure for Astronomy VII, editado por Gianluca Chiozzi y Jorge Ibsen. SPIE, 2022. http://dx.doi.org/10.1117/12.2628899.
Texto completoInformes sobre el tema "METAHEURISTIC APPROACH"
Olin, Irwin D. Flat-Top Sector Beams Using Only Array Element Phase Weighting: A Metaheuristic Optimization Approach. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2012. http://dx.doi.org/10.21236/ada569184.
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