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Relevant bibliographies by topics / Real-world optimization

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Academic literature on the topic 'Real-world optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Real-world optimization"

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AKDOĞAN, Fatmanur, Doğan ALTAN, Ercan PEKER, Altuğ ÜSTÜN, and Sanem SARIEL. "Optimization of real-world outdoor campaign allocations." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28, no. 3 (2020): 1276–92. http://dx.doi.org/10.3906/elk-1907-122.

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Lee, Ki-Baek, Young-Joo Kim, and Young-Dae Hong. "Real-Time Swarm Search Method for Real-World Quadcopter Drones." Applied Sciences 8, no. 7 (2018): 1169. http://dx.doi.org/10.3390/app8071169.

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This paper proposes a novel search method for a swarm of quadcopter drones. In the proposed method, inspired by the phenomena of swarms in nature, drones effectively look for the search target by investigating the evidence from the surroundings and communicating with each other. The position update mechanism is implemented using the particle swarm optimization algorithm as the swarm intelligence (a well-known swarm-based optimization algorithm), as well as a dynamic model for the drones to take the real-world environment into account. In addition, the mechanism is processed in real-time along

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Panagant, Natee, Nantiwat Pholdee, Sujin Bureerat, Ali Rıza Yıldız, and Sadiq M. Sait. "Seagull optimization algorithm for solving real-world design optimization problems." Materials Testing 62, no. 6 (2020): 640–44. http://dx.doi.org/10.3139/120.111529.

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Krink, Thiemo, and Sandra Paterlini. "Multiobjective optimization using differential evolution for real-world portfolio optimization." Computational Management Science 8, no. 1-2 (2009): 157–79. http://dx.doi.org/10.1007/s10287-009-0107-6.

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Someya, Hiroshi, Hisashi Handa, and Seiichi Koakutsu. "Designing Stochastic Optimization Algorithms for Real-world Applications." IEEJ Transactions on Electronics, Information and Systems 132, no. 1 (2012): 2–5. http://dx.doi.org/10.1541/ieejeiss.132.2.

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Bujok, Petr, Josef Tvrdik, and Radka Polakova. "Nature-Inspired Algorithms in Real-World Optimization Problems." MENDEL 23, no. 1 (2017): 7–14. http://dx.doi.org/10.13164/mendel.2017.1.007.

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Eight popular nature inspired algorithms are compared with the blind random search and three advanced adaptive variants of differential evolution (DE) on real-world problems benchmark collected for CEC 2011 algorithms competition. The results show the good performance of the adaptive DE variants and their superiority over the other algorithms in the test problems. Some of the nature-inspired algorithms perform even worse that the blind random search in some problems. This is a strong argument for recommendation for application, where well-verified algorithm successful in competitions should be

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Fang, Wei, Xiaodong Li, Mengjie Zhang, and Mengqi Hu. "Nature-Inspired Algorithms for Real-World Optimization Problems." Journal of Applied Mathematics 2015 (2015): 1–2. http://dx.doi.org/10.1155/2015/359203.

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Omran, Mahamed G. H., and Salah Al-Sharhan. "Improved continuous Ant Colony Optimization algorithms for real-world engineering optimization problems." Engineering Applications of Artificial Intelligence 85 (October 2019): 818–29. http://dx.doi.org/10.1016/j.engappai.2019.08.009.

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Steffel, Jan, Helene Rempel, Alexander Breitenstein, et al. "Comprehensive cardiac resynchronization therapy optimization in the real world." Cardiology Journal 21, no. 3 (2014): 316–24. http://dx.doi.org/10.5603/cj.a2013.0123.

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Coltorti, Dario, and Andrea E. Rizzoli. "Ant colony optimization for real-world vehicle routing problems." ACM SIGEVOlution 2, no. 2 (2007): 2–9. http://dx.doi.org/10.1145/1329465.1329466.

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Dissertations / Theses on the topic "Real-world optimization"

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Ryan, Mark Desmond Charles. "Hybrid genetic algorithms for real world combinatorial optimization problems." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393303.

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Zielinski, Karin. "Optimizing real-world problems with differential evolution and particle swarm optimization." Aachen Shaker, 2009. http://d-nb.info/993509398/04.

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Zielinski, Karin [Verfasser]. "Optimizing Real-World Problems with Differential Evolution and Particle Swarm Optimization / Karin Zielinski." Aachen : Shaker, 2009. http://d-nb.info/1156517362/34.

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Jiang, Hsin-yi. "A framework for estimating the applicability of GAs for real-world optimization problems." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3389107.

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Novellani, Stefano <1985&gt. "Models and Algorihtm for the Optimization of Real-World Routing and Logistics Problems." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/7112/.

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Logistics involves planning, managing, and organizing the flows of goods from the point of origin to the point of destination in order to meet some requirements. Logistics and transportation aspects are very important and represent a relevant costs for producing and shipping companies, but also for public administration and private citizens. The optimization of resources and the improvement in the organization of operations is crucial for all branches of logistics, from the operation management to the transportation. As we will have the chance to see in this work, optimization techniques, m

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Dymond, Antoine Smith Dryden. "Multiple objective optimization of an airfoil shape." Diss., University of Pretoria, 2011. http://hdl.handle.net/2263/22939.

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An airfoil shape optimization problem with conflicting objectives is handled using two different multi-objective approaches. These are an a priori scalarization approach where the conflicting objectives are assigned weights and summed together to form a single objective, and the Pareto-optimal multi-objective approach. The optimization formulations for both approaches contain challenging numerical characteristics which include noise, multi-modality and undefined regions. Gradient-, surrogate- and population-based single objective optimization methods are applied to the `a priori' formulations.

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Al, Khatib Sultan. "A comparative study of the relative performance and real-world suitability of optimization approaches for Human Resource Allocation." Thesis, University of East Anglia, 2018. https://ueaeprints.uea.ac.uk/68211/.

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The problem of Staffing and Scheduling a Software Project (SSSP), where we consider Human Resource Allocation (HRA) to minimize project time, offers a management challenge for Project Managers (PM’s). Unlike the general HRA problem, SSSP involves determination of the assignment of a fixed amount of resources to teams and the allocation of these teams to project’s jobs. SSSP problem arises across a diverse range of resources’ and project characteristics (discrete variables), and this variety has offered a wide range of HRA methods. The general consensus is that the benchmark for SSSP are Meta-h

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Eckert, Marie-Lena Katharina Noemi [Verfasser], Nils [Akademischer Betreuer] Thürey, Nils [Gutachter] Thürey, and Matthias [Gutachter] Teschner. "Optimization for Fluid Simulation and Reconstruction of Real-World Flow Phenomena / Marie-Lena Katharina Noemi Eckert ; Gutachter: Nils Thürey, Matthias Teschner ; Betreuer: Nils Thürey." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1195708645/34.

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Gulati, Mayank. "Bridging Sim-to-Real Gap in Offline Reinforcement Learning for Antenna Tilt Control in Cellular Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292948.

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Antenna tilt is the angle subtended by the radiation beam and horizontal plane. This angle plays a vital role in determining the coverage and the interference of the network with neighbouring cells and adjacent base stations. Traditional methods for network optimization rely on rule-based heuristics to do decision making for antenna tilt optimization to achieve desired network characteristics. However, these methods are quite brittle and are incapable of capturing the dynamics of communication traffic. Recent advancements in reinforcement learning have made it a viable solution to overcome thi

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Messaoudi, Bilal. "Approches par décomposition pour des problèmes réels de planification et de tournées de véhicules." Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0211.

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Différents acteurs du monde de l'industrie rencontrent quotidiennement des problèmes d'optimisation impliquant des décisions à prendre soit à un niveau tactique, comme pour déterminer le meilleur emplacement d'entrepôts à installer pour couvrir une zone géographique donnée, ou bien à un niveau opérationnel, comme pour améliorer le rendement d'un processus de production. L'objectif de cette thèse Cifre est de proposer des approches de résolution en se focalisant sur des problèmes de planification et de tournées de véhicules issus de clients industriels. Dans un premier temps, nous mettrons en l

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Books on the topic "Real-world optimization"

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Fujisawa, Katsuki, Yuji Shinano, and Hayato Waki, eds. Optimization in the Real World. Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-55420-2.

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service), SpringerLink (Online, ed. Algebraic Modeling Systems: Modeling and Solving Real World Optimization Problems. Springer Berlin Heidelberg, 2012.

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Fujisawa, Katsuki, Yuji Shinano, and Hayato Waki. Optimization in the Real World: Toward Solving Real-World Optimization Problems. Springer, 2016.

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Fujisawa, Katsuki, Yuji Shinano, and Hayato Waki. Optimization in the Real World: Toward Solving Real-World Optimization Problems. Ingramcontent, 2015.

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Algebraic Modeling Systems Modeling And Solving Real World Optimization Problems. Springer, 2012.

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Kallrath, Josef. Algebraic Modeling Systems: Modeling and Solving Real World Optimization Problems. Springer, 2014.

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Kallrath, Josef. Algebraic Modeling Systems: Modeling and Solving Real World Optimization Problems. Springer, 2012.

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Green, Yair, and Eli Mansoor. Mastering AWS Cost Optimization: Real-World Technical and Operational Cost-saving Best Practices. Independently Published, 2019.

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Milliken, Christopher, Ehsan Nikbakht, and Andrew Spieler. Traditional Asset Allocation Securities. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190269999.003.0020.

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Asset allocation models have evolved in complexity with the development of modern portfolio theory, but they continue to operate under the assumption of investor rationality and other assumptions that do not hold in the real world. For this reason, academics and industry professionals make efforts to understand the behavioral biases of decision makers and the implications these biases have on asset allocation strategies. This chapter reviews the building blocks of asset allocation, involving stocks, bonds, real estate, and cash. It also examines the history and theory behind two of the most po

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Din, Roshidi, Siti Sakira Kamaruddin, Angela Amphawan, and Mohd Nizam Omar. Basic discrete structures. UUM Press, 2015. http://dx.doi.org/10.32890/9789670876177.

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Digital technology has pervaded almost all spheres of life.Due to the importance of discrete information in our increasingly digital world, familiarity with the underlying principles, concepts and operations on discrete information is inevitable.This book is intended as a basic course for introducing students to abstract mathematical structures to represent discrete information and relationships between them.These discrete structures include sets, sequences, permutations, combinations, functions, trees and finite-state machines which are predominantly used in computer science and data networki

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Book chapters on the topic "Real-world optimization"

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Stewart, Theodor, Oliver Bandte, Heinrich Braun, et al. "Real-World Applications of Multiobjective Optimization." In Multiobjective Optimization. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88908-3_11.

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Fasano, Giorgio. "Computational Experience and Real-World Context." In SpringerBriefs in Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05005-8_5.

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Pelikan, Martin. "Hierarchical BOA in the Real World." In Hierarchical Bayesian Optimization Algorithm. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32373-0_7.

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Fujisawa, Katsuki, Toyotaro Suzumura, Hitoshi Sato, et al. "Advanced Computing and Optimization Infrastructure for Extremely Large-Scale Graphs on Post Peta-Scale Supercomputers." In Optimization in the Real World. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55420-2_1.

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Otsuki, Tomoshi. "Demand Response Optimization Based on Building’s Characteristics." In Optimization in the Real World. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55420-2_10.

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Nakajima, Kengo, Masaki Satoh, Takashi Furumura, et al. "ppOpen-HPC: Open Source Infrastructure for Development and Execution of Large-Scale Scientific Applications on Post-Peta-Scale Supercomputers with Automatic Tuning (AT)." In Optimization in the Real World. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55420-2_2.

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Gamrath, Gerald, Timo Berthold, Stefan Heinz, and Michael Winkler. "Structure-Based Primal Heuristics for Mixed Integer Programming." In Optimization in the Real World. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55420-2_3.

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Fischetti, Martina, Matteo Fischetti, and Michele Monaci. "Optimal Turbine Allocation for Offshore and Onshore Wind Farms." In Optimization in the Real World. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55420-2_4.

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Escolar, Emerson G., and Yasuaki Hiraoka. "Optimal Cycles for Persistent Homology Via Linear Programming." In Optimization in the Real World. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55420-2_5.

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Draegert, Andreas, Andreas Eisenblätter, Inken Gamrath, and Axel Werner. "Optimal Battery Control for Smart Grid Nodes." In Optimization in the Real World. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55420-2_6.

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Conference papers on the topic "Real-world optimization"

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Gargoloff, Joaquin, Bradley Duncan, Edward Tate, Ales Alajbegovic, Alain Belanger, and Barnali Paul. "Robust Optimization for Real World CO2 Reduction." In CO2 Reduction for Transportation Systems Conference. SAE International, 2018. http://dx.doi.org/10.4271/2018-37-0015.

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Biermann, D., K. Weinert, and T. Wagner. "Model-based optimization revisited: Towards real-world processes." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631199.

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El-Hosseini, Mostafa A., Aboul Ella Hassanien, Ajith Abraham, and Hameed Al-Qaheri. "Genetic Annealing Optimization: Design and Real World Applications." In 2008 Eighth International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2008. http://dx.doi.org/10.1109/isda.2008.311.

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Shen, Daliang, Dominik Karbowski, and Aymeric Rousseau. "Fuel Efficient Speed Optimization for Real-World Highway Cruising." In WCX World Congress Experience. SAE International, 2018. http://dx.doi.org/10.4271/2018-01-0589.

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Gurrola-Ramos, Javier, Arturo Hernandez-Aguirre, and Oscar Dalmau-Cedeno. "COLSHADE for Real-World Single-Objective Constrained optimization Problems." In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185583.

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Syberfeldt, Anna, and Simon Lidberg. "Real-world simulation-based manufacturing optimization using Cuckoo Search." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465158.

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Starkey, Andrew, Hani Hagras, Sid Shakya, and Gilbert Owusu. "Fuzzy dominance rules for real-world many objective optimization." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015460.

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Hauder, Viktoria A., Andreas Beham, Stefan Wagner, and Michael Affenzeller. "Optimization networks for real-world production and logistics problems." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3092959.

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GU, Wei, and Gan XIANG. "Improved Moth Flame Optimization with Multioperator for solving real-world optimization problems." In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021. http://dx.doi.org/10.1109/iaeac50856.2021.9390876.

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Yuan, Ting, Guangwei Li, Jie Lu, Chen Liu, Lian Li, and Jingling Xue. "GoBench: A Benchmark Suite of Real-World Go Concurrency Bugs." In 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2021. http://dx.doi.org/10.1109/cgo51591.2021.9370317.

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