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Journal articles on the topic 'Real-world optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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10

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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11

Dell’Amico, Mauro, Simone Falavigna, and Manuel Iori. "Optimization of a Real-World Auto-Carrier Transportation Problem." Transportation Science 49, no. 2 (2015): 402–19. http://dx.doi.org/10.1287/trsc.2013.0492.

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12

Meske, R., J. Sauter, and E. Schnack. "Nonparametric gradient-less shape optimization for real-world applications." Structural and Multidisciplinary Optimization 30, no. 3 (2005): 201–18. http://dx.doi.org/10.1007/s00158-005-0518-0.

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13

Rizzoli, A. E., R. Montemanni, E. Lucibello, and L. M. Gambardella. "Ant colony optimization for real-world vehicle routing problems." Swarm Intelligence 1, no. 2 (2007): 135–51. http://dx.doi.org/10.1007/s11721-007-0005-x.

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14

Trutnevyte, Evelina. "Does cost optimization approximate the real-world energy transition?" Energy 106 (July 2016): 182–93. http://dx.doi.org/10.1016/j.energy.2016.03.038.

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15

Ehrgott, Matthias. "Multiobjective Optimization." AI Magazine 29, no. 4 (2008): 47. http://dx.doi.org/10.1609/aimag.v29i4.2198.

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Using some real world examples I illustrate the important role of multiobjective optimization in decision making and its interface with preference handling. I explain what optimization in the presence of multiple objectives means and discuss some of the most common methods of solving multiobjective optimization problems using transformations to single objective optimisation problems. Finally, I address linear and combinatorial optimization problems with multiple objectives and summarize techniques for solving them. Throughout the article, I refer to the real world examples introduced at the be

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16

He, Cheng, Ye Tian, Handing Wang, and Yaochu Jin. "A repository of real-world datasets for data-driven evolutionary multiobjective optimization." Complex & Intelligent Systems 6, no. 1 (2019): 189–97. http://dx.doi.org/10.1007/s40747-019-00126-2.

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Abstract Many real-world optimization applications have more than one objective, which are modeled as multiobjective optimization problems. Generally, those complex objective functions are approximated by expensive simulations rather than cheap analytic functions, which have been formulated as data-driven multiobjective optimization problems. The high computational costs of those problems pose great challenges to existing evolutionary multiobjective optimization algorithms. Unfortunately, there have not been any benchmark problems reflecting those challenges yet. Therefore, we carefully select

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17

Clerckx, B., L. Vandendorpe, D. Vanhoenacker-Janvier, and C. Oestges. "Optimization of Nonlinear Signal Constellations for Real-World MIMO Channels." IEEE Transactions on Signal Processing 52, no. 4 (2004): 894–902. http://dx.doi.org/10.1109/tsp.2004.823486.

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18

Brown, Gerald G., and Richard E. Rosenthal. "Optimization Tradecraft: Hard-Won Insights from Real-World Decision Support." Interfaces 38, no. 5 (2008): 356–66. http://dx.doi.org/10.1287/inte.1080.0382.

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19

Shambour, Moh'd Khaled Yousef. "Adaptive Multi-crossover Evolutionary Algorithm for Real-world Optimization Problems." International Journal of Reasoning-based Intelligent Systems 10, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijris.2018.10016282.

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20

Luna, Francisco, Gustavo R. Zavala, Antonio J. Nebro, Juan J. Durillo, and Carlos A. Coello Coello. "Distributed Multi-Objective Metaheuristics for Real-World Structural Optimization Problems." Computer Journal 59, no. 6 (2014): 777–92. http://dx.doi.org/10.1093/comjnl/bxu082.

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21

Nemhauser, George L. "The Age of Optimization: Solving Large-Scale Real-World Problems." Operations Research 42, no. 1 (1994): 5–13. http://dx.doi.org/10.1287/opre.42.1.5.

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22

Lagaros, Nikos D. "A general purpose real-world structural design optimization computing platform." Structural and Multidisciplinary Optimization 49, no. 6 (2013): 1047–66. http://dx.doi.org/10.1007/s00158-013-1027-1.

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23

Mirjalili, Seyedali. "Special issue on “real-world optimization problems and meta-heuristics”." Neural Computing and Applications 32, no. 16 (2020): 11965–66. http://dx.doi.org/10.1007/s00521-020-04966-9.

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24

Yıldız, Mustafa, Natee Panagant, Nantiwat Pholdee, Sujin Bureerat, Sadiq M. Sait, and Ali Rıza Yıldız. "Hybrid Taguchi-Lévy flight distribution optimization algorithm for solving real-world design optimization problems." Materials Testing 63, no. 6 (2021): 547–51. http://dx.doi.org/10.1515/mt-2020-0091.

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Abstract The Lévy flight distribution optimization algorithm is a recently developed meta-heuristic. In this study, the Lévy flight distribution optimization algorithm and the Taguchi method are hybridized to solve the shape optimization problem, which is the final step in developing optimum structural components. The new method is termed the hybrid Lévy flight distribution and Taguchi (HLFD-T) algorithm. Geometric dimensions are used as design variables in the optimization, and the problem is aimed at mass minimization. The constraint in the problem is the maximum stress value. The well-known

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25

Lidberg, Simon, Tehseen Aslam, Leif Pehrsson, and Amos H. C. Ng. "Optimizing real-world factory flows using aggregated discrete event simulation modelling." Flexible Services and Manufacturing Journal 32, no. 4 (2019): 888–912. http://dx.doi.org/10.1007/s10696-019-09362-7.

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AbstractReacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage lev

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26

VASANT, P. "A NOVEL HYBRID GENETIC ALGORITHMS AND PATTERN SEARCH TECHNIQUES FOR INDUSTRIAL PRODUCTION PLANNING." International Journal of Modeling, Simulation, and Scientific Computing 03, no. 04 (2012): 1250020. http://dx.doi.org/10.1142/s1793962312500201.

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Soft computing has attracted many research scientists, decision makers and practicing researchers in recent years as powerful computational intelligent techniques, for solving unlimited number of complex real-world problems particularly related to research area of optimization. Under the uncertain and turbulence environment, classical and traditional approaches are unable to obtain a complete solution with satisfaction for the real-world problems on optimization. Therefore, new global optimization methods are required to handle these issues seriously. One such method is hybrid Genetic algorith

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27

Omran, Mahamed G. H., Salah Alsharhan, and Maurice Clerc. "A modified Intellects-Masses Optimizer for solving real-world optimization problems." Swarm and Evolutionary Computation 41 (August 2018): 159–66. http://dx.doi.org/10.1016/j.swevo.2018.02.015.

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28

Willems, Sean P. "Data Set—Real-World Multiechelon Supply Chains Used for Inventory Optimization." Manufacturing & Service Operations Management 10, no. 1 (2008): 19–23. http://dx.doi.org/10.1287/msom.1070.0176.

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29

Ferrer, Javier, Manuel López-Ibáñez, and Enrique Alba. "Reliable simulation-optimization of traffic lights in a real-world city." Applied Soft Computing 78 (May 2019): 697–711. http://dx.doi.org/10.1016/j.asoc.2019.03.016.

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30

Tanabe, Ryoji, and Hisao Ishibuchi. "An easy-to-use real-world multi-objective optimization problem suite." Applied Soft Computing 89 (April 2020): 106078. http://dx.doi.org/10.1016/j.asoc.2020.106078.

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31

Akbari, Ebrahim, Abolfazl Rahimnejad, and Stephen Andrew Gadsden. "A greedy non‐hierarchical grey wolf optimizer for real‐world optimization." Electronics Letters 57, no. 13 (2021): 499–501. http://dx.doi.org/10.1049/ell2.12176.

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32

Gürses, Dildar, Nantiwat Pholdee, Sujin Bureerat, Sadiq M. Sait, and Ali Rıza Yıldız. "A novel hybrid water wave optimization algorithm for solving complex constrained engineering problems." Materials Testing 63, no. 6 (2021): 560–64. http://dx.doi.org/10.1515/mt-2020-0093.

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Abstract In this work, a new hybrid optimization algorithm (HWW-NM), which combines the Nelder-Mead local search algorithm with the water wave algorithm, is introduced to solve real-world engineering optimization problems. This paper is one of the first studies in which both the water wave algorithm and the HWW-NM are applied to processing parameters optimization for manufacturing processes. HWW-NM performance is measured using the cantilever beam problem. Additionally, a problem for milling manufacturing optimization is posed and solved to evaluate HWW-NM performance in real-world application

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33

CARLSON, J. DAVID. "MR FLUIDS AND DEVICES IN THE REAL WORLD." International Journal of Modern Physics B 19, no. 07n09 (2005): 1463–70. http://dx.doi.org/10.1142/s0217979205030451.

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Magnetorheological (MR) fluids are now well established as one of the leading materials for use in controllable structures and systems. Commercial application of MR fluids, particularly in the automotive industry, has grown rapidly over the past few years. Today, MR fluid devices can be found in tens of thousands of vehicles. Experience in developing commercially successful MR fluids and devices has led to the creation of a series of figures-of-merit, design guidelines and empirical equations for describing MR fluids. Such experience-based guidelines have proven to be invaluable tools in all p

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34

Dudek, Maciej K. "Living in an imaginary world that looks real." Journal of Economic Dynamics and Control 41 (April 2014): 209–23. http://dx.doi.org/10.1016/j.jedc.2014.01.021.

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35

Kobayashi, Kenichiro, Reinhard Hinkelmann, and Rainer Helmig. "Development of a simulation–optimization model for multiphase systems in the subsurface: a challenge to real-world simulation–optimization." Journal of Hydroinformatics 10, no. 2 (2008): 139–52. http://dx.doi.org/10.2166/hydro.2008.013.

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The main purpose of this paper is to demonstrate the capability of a new simulation–optimization model especially tailored to investigate the optimal management strategy of a closed coal mine in the Ruhr, Germany. This paper deals with the multiphase/multicomponent flow simulation; the optimization model (simulated annealing); the mesh generation function; the coupling of them; and the use of a parallel computer. Firstly, a mesh generation function is included in the total procedure for the modelling of complex system configurations often required when the real-world problem is dealt with. The

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36

Zhao, Yuchu, Zhengyu Liu, Fei Zheng, and Yishuai Jin. "Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model." Monthly Weather Review 147, no. 5 (2019): 1429–45. http://dx.doi.org/10.1175/mwr-d-18-0199.1.

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Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection t

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37

Hu, Xiao-Bing, Ming Wang, and Mark S. Leeson. "Ripple-Spreading Network Model Optimization by Genetic Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/176206.

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Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM) is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network

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38

Banihashemi, Mohamadreza, and Ali Haghani. "Optimization Model for Large-Scale Bus Transit Scheduling Problems." Transportation Research Record: Journal of the Transportation Research Board 1733, no. 1 (2000): 23–30. http://dx.doi.org/10.3141/1733-04.

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A procedure is presented for solving real-world large-scale multiple depot vehicle scheduling (MDVS) problems considering the route time constraints (RTCs). The procedure is applied to some test problems and then to a real-world problem. The real-world problem is the transit bus scheduling problem of the mass transit administration (MTA) in Baltimore, Maryland. The RTCs are added to the MDVS problem to account for real-world operational restrictions such as fuel consumption. Formulation of the MDVS problem, the set of constraints for considering the time restriction, and a heuristic procedure

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39

Cheifetz, Nicolas, Anne-Claire Sandraz, Cédric Féliers, Denis Gilbert, Olivier Piller, and Anne Lang. "An Incremental Sensor Placement Optimization in a Large Real-World Water System." Procedia Engineering 119 (2015): 947–52. http://dx.doi.org/10.1016/j.proeng.2015.08.977.

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40

Veiga, Jorge, Roberto R. Exposito, Bruno Raffin, and Juan Tourino. "Optimization of Real-World MapReduce Applications With Flame-MR: Practical Use Cases." IEEE Access 6 (2018): 69750–62. http://dx.doi.org/10.1109/access.2018.2880842.

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41

Zhao, Xin, and Shuang-xin Wang. "Convergence analysis of the tabu-based real-coded small-world optimization algorithm." Engineering Optimization 46, no. 4 (2013): 465–86. http://dx.doi.org/10.1080/0305215x.2013.776552.

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42

Schissler, Carl, Christian Loftin, and Dinesh Manocha. "Acoustic Classification and Optimization for Multi-Modal Rendering of Real-World Scenes." IEEE Transactions on Visualization and Computer Graphics 24, no. 3 (2018): 1246–59. http://dx.doi.org/10.1109/tvcg.2017.2666150.

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43

Ibarbia, Izaskun, Alexander Mendiburu, Maria Santos, and Jose A. Lozano. "An interactive optimization approach to a real-world oceanographic campaign planning problem." Applied Intelligence 36, no. 3 (2011): 721–34. http://dx.doi.org/10.1007/s10489-011-0291-2.

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44

Zokaee, Shiva, Armin Jabbarzadeh, Behnam Fahimnia, and Seyed Jafar Sadjadi. "Robust supply chain network design: an optimization model with real world application." Annals of Operations Research 257, no. 1-2 (2014): 15–44. http://dx.doi.org/10.1007/s10479-014-1756-6.

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45

Huang, Lingling, and Chuandong Qin. "A novel modified gravitational search algorithm for the real world optimization problem." International Journal of Machine Learning and Cybernetics 10, no. 11 (2019): 2993–3002. http://dx.doi.org/10.1007/s13042-018-00917-y.

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46

Fan, Yi, Pengjun Wang, Ali Asghar Heidari, et al. "Boosted hunting-based fruit fly optimization and advances in real-world problems." Expert Systems with Applications 159 (November 2020): 113502. http://dx.doi.org/10.1016/j.eswa.2020.113502.

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47

Chen, Bo, Darrell Robinette, Mahdi Shahbakhti, et al. "Connected Vehicles and Powertrain Optimization." Mechanical Engineering 139, no. 09 (2017): S12—S18. http://dx.doi.org/10.1115/1.2017-sep-5.

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This article discusses the basic concept of connected and automated vehicles (CAV) technology. The common methods to improve fuel economy are also introduced. The effects of connectivity on vehicle/powertrain control and optimization are also elaborated. The Michigan Tech NEXTCAR project is also presented to provide a more detailed view of predictive vehicle/powertrain control enabled by CAV technologies. The U.S. Department of transportation (DOT) and other federal/state funding agencies have supported research and pilot deployment efforts to develop crosscutting CV technologies and evaluate

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48

Wang, S. X., C. H. Yang, and Y. Dong. "Multi-Objective Optimization Nonlinear Predictive Control Based on Small-World Optimization." Advanced Materials Research 562-564 (August 2012): 2116–19. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.2116.

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Due to fast receding horizon speed and global optimization of small-world optimization algorithm with real-coding (RSW), a new nonlinear multi-objective predictive controller was presented based on RSW and neural network (NN) identification trained by BP. NN model which was obtained by off-line identification was used to predict the present and future output of the plant, and RSW was applied to receding horizon control. Finally, an application to 500MW unit load control system with multi-objective optimization was given, and simulation results indicated the effectiveness of this new approach.

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49

Miller, Preston J., and Richard M. Todd. "Real effects of monetary policy in a world economy." Journal of Economic Dynamics and Control 19, no. 1-2 (1995): 125–53. http://dx.doi.org/10.1016/0165-1889(93)00777-2.

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50

Doerr, Benjamin, Carola Doerr, Aneta Neumann, Frank Neumann, and Andrew Sutton. "Optimization of Chance-Constrained Submodular Functions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1460–67. http://dx.doi.org/10.1609/aaai.v34i02.5504.

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Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided. In this paper, we investigate submodular optimization problems with chance constraints. We provide a first analysis on the approximation behavior of popular greedy algorithms for submodular problems with chance constraints. Our results show that these algorithms are highly effective when using surrogate functions that estimate constraint violations bas

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