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Journal articles on the topic 'Benchmark problems'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Lee, Ho Min, Donghwi Jung, Ali Sadollah, Do Guen Yoo, and Joong Hoon Kim. "Generation of Benchmark Problems for Optimal Design of Water Distribution Systems." Water 11, no. 8 (2019): 1637. http://dx.doi.org/10.3390/w11081637.

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Engineering benchmark problems with specific characteristics have been used to compare the performance and reliability of metaheuristic algorithms, and water distribution system design benchmarks are also widely used. However, only a few benchmark design problems have been considered in the research community. Due to the limited set of previous benchmarks, it is challenging to identify the algorithm with the best performance and the highest reliability among a group of algorithms. Therefore, in this study, a new water distribution system design benchmark problem generation method is proposed c

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2

Connolly, Kevin John, Farzad Rahnema, and Pavel V. Tsvetkov. "Prismatic VHTR neutronic benchmark problems." Nuclear Engineering and Design 285 (April 2015): 207–40. http://dx.doi.org/10.1016/j.nucengdes.2014.11.044.

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Elser, Veit, Ti-Yen Lan, and Tamir Bendory. "Benchmark Problems for Phase Retrieval." SIAM Journal on Imaging Sciences 11, no. 4 (2018): 2429–55. http://dx.doi.org/10.1137/18m1170364.

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4

Schiehlen, Werner. "Benchmark problems from vehicle dynamics." Journal of Mechanical Science and Technology 29, no. 7 (2015): 2601–6. http://dx.doi.org/10.1007/s12206-015-0504-4.

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5

Aubry, Jean-Francois, Oscar Bates, Christian Boehm, et al. "Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models." Journal of the Acoustical Society of America 152, no. 2 (2022): 1003–19. http://dx.doi.org/10.1121/10.0013426.

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Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar b

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Wu, W., D. Montiel, J. E. Guyer, et al. "Phase field benchmark problems for nucleation." Computational Materials Science 193 (June 2021): 110371. http://dx.doi.org/10.1016/j.commatsci.2021.110371.

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7

Sroka, Jan, and Jacek Starzyński. "BENCHMARK TESTS FOR OPEN BOUNDARY PROBLEMS." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 11, no. 1 (1992): 81–84. http://dx.doi.org/10.1108/eb051757.

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8

PORTER, MICHAEL B., and A. TOLSTOY. "THE MATCHED FIELD PROCESSING BENCHMARK PROBLEMS." Journal of Computational Acoustics 02, no. 03 (1994): 161–85. http://dx.doi.org/10.1142/s0218396x94000129.

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In matched field processing sophisticated acoustic models are combined with signal processing techniques to localize an acoustic source in the ocean. A key challenge has been to develop schemes that work not just in idealized simulations but in realistic scenarios. Additionally it has been difficult to get a sense of the relative merits of different schemes: there has been no common set of problems to test the techniques. To assess the state of the art, a workshop was held in May 1993 at the Naval Research Laboratory where both simulated and experimental data were provided to the community of

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9

Collins, Michael D., Finn B. Jensen, Peter L. Nielsen, Mario Zampolli, Henrik Schmidt, and William L. Siegmann. "Range‐dependent seismo‐acoustics benchmark problems." Journal of the Acoustical Society of America 118, no. 3 (2005): 1970. http://dx.doi.org/10.1121/1.4781734.

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10

Wie, Bong, and Dennis S. Bernstein. "Benchmark problems for robust control design." Journal of Guidance, Control, and Dynamics 15, no. 5 (1992): 1057–59. http://dx.doi.org/10.2514/3.20949.

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11

Ivezic, Z., M. A. T. Groenewegen, A. Men'shchikov, and R. Szczerba. "Benchmark problems for dust radiative transfer." Monthly Notices of the Royal Astronomical Society 291, no. 1 (1997): 121–24. http://dx.doi.org/10.1093/mnras/291.1.121.

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12

de Mooij, Cornelis, Marcias Martinez, and Rinze Benedictus. "iFEM benchmark problems for solid elements." Smart Materials and Structures 28, no. 6 (2019): 065003. http://dx.doi.org/10.1088/1361-665x/ab136f.

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13

Sharipov, Felix. "Benchmark problems in rarefied gas dynamics." Vacuum 86, no. 11 (2012): 1697–700. http://dx.doi.org/10.1016/j.vacuum.2012.02.048.

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14

Chapman, C. J. "Some benchmark problems for computational aeroacoustics." Journal of Sound and Vibration 270, no. 3 (2004): 495–508. http://dx.doi.org/10.1016/j.jsv.2003.09.054.

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15

Fox, Patrick J., and Hefu Pu. "Benchmark Problems for Large Strain Consolidation." Journal of Geotechnical and Geoenvironmental Engineering 141, no. 11 (2015): 06015008. http://dx.doi.org/10.1061/(asce)gt.1943-5606.0001357.

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16

Pinte, C., T. J. Harries, M. Min, et al. "Benchmark problems for continuum radiative transfer." Astronomy & Astrophysics 498, no. 3 (2009): 967–80. http://dx.doi.org/10.1051/0004-6361/200811555.

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17

Akartunalı, Kerem, Natashia Boland, Ian Evans, Mark Wallace, and Hamish Waterer. "Airline planning benchmark problems—Part I:." Computers & Operations Research 40, no. 3 (2013): 775–92. http://dx.doi.org/10.1016/j.cor.2012.02.012.

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18

TANABE, Ryoji, and Akira OYAMA. "On Benchmark Problems for Constrained Multi-Objective Optimization Problems." Proceedings of OPTIS 2016.12 (2016): 2105. http://dx.doi.org/10.1299/jsmeoptis.2016.12.2105.

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19

G.Lakshmi, Kameswari. "Optimality Test cases of Particle Swarm optimization of single objective functions." Journal of Applied Mathematics and Statistical Analysis 1, no. 1 (2020): 1–9. https://doi.org/10.5281/zenodo.4010485.

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<em>Optimization problems are classified into continuous, discrete, constrained, unconstrained deterministic, stochastic, single objective and multi-objective optimization problems. Deterministic, Heuristics and Meta-Heuristic technique, mostly dominate the solution set of small and medium scale problems, whereas for large data class optimization problems, Evolutionary techniques ( mostly derivative -free) are used to address the near -optimal solution of these class of P, N-P, N-P Hard problems. In the present paper, evolutionary algorithmic approach without evolutionary operators, mimicking

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20

Vorlaender, Michael, and Lukas Aspoeck. "Diffraction problems in an architectural acoustics benchmark." Journal of the Acoustical Society of America 150, no. 4 (2021): A93. http://dx.doi.org/10.1121/10.0007735.

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21

Pantoja, Mario Fernndez, Amelia Rubio Bretones, and Rafael Gmez Martin. "Benchmark Antenna Problems for Evolutionary Optimization Algorithms." IEEE Transactions on Antennas and Propagation 55, no. 4 (2007): 1111–21. http://dx.doi.org/10.1109/tap.2007.893396.

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22

Hanahara, Kazuyuki, and Kaori Yamada. "Generating Benchmark Problems for Disassembling Process Inference." Proceedings of Manufacturing Systems Division Conference 2024 (2024): 307. http://dx.doi.org/10.1299/jsmemsd.2024.307.

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23

Mohamad, A. A. "BENCHMARK SOLUTION FOR UNSTEADY STATE CFD PROBLEMS." Numerical Heat Transfer, Part A: Applications 34, no. 6 (1998): 653–72. http://dx.doi.org/10.1080/10407789808914008.

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24

Chang, Haibin, Qinzhuo Liao, and Dongxiao Zhang. "Benchmark problems for subsurface flow uncertainty quantification." Journal of Hydrology 531 (December 2015): 168–86. http://dx.doi.org/10.1016/j.jhydrol.2015.09.040.

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25

Bäuerle, Nicole. "Benchmark and mean-variance problems for insurers." Mathematical Methods of Operations Research 62, no. 1 (2005): 159–65. http://dx.doi.org/10.1007/s00186-005-0446-1.

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26

Ziemian, Ronald D., and Jean C. Batista Abreu. "Design by advanced analysis - 3D benchmark problems." Steel Construction 11, no. 1 (2018): 24–29. http://dx.doi.org/10.1002/stco.201810011.

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27

Alonso, Omar, and Kenneth Church. "Evaluating the Evaluations: A Perspective on Benchmarks." ACM SIGIR Forum 58, no. 2 (2024): 1–27. https://doi.org/10.1145/3722449.3722467.

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More and more benchmarks, datasets, and evaluation tasks are becoming available. This is extremely useful for the community because it enables researchers and practitioners to test and evaluate new techniques. However, the construction, evaluation, and maintenance of data sets and benchmarks is opaque which creates problems with respect to stability and true representations. Our position is that we need to revisit how we design and implement benchmarks. The SPEC benchmark offers interesting perspectives that our community should consider. We use a data set of influential papers and resources t

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28

Macías, Jorge, Cipriano Escalante, and Manuel J. Castro. "Multilayer-HySEA model validation for landslide-generated tsunamis – Part 2: Granular slides." Natural Hazards and Earth System Sciences 21, no. 2 (2021): 791–805. http://dx.doi.org/10.5194/nhess-21-791-2021.

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Abstract. The final aim of the present work is to propose a NTHMP-benchmarked numerical tool for landslide-generated tsunami hazard assessment. To achieve this, the novel Multilayer-HySEA model is validated using laboratory experiment data for landslide-generated tsunamis. In particular, this second part of the work deals with granular slides, while the first part, in a companion paper, considers rigid slides. The experimental data used have been proposed by the US National Tsunami Hazard and Mitigation Program (NTHMP) and were established for the NTHMP Landslide Benchmark Workshop, held in Ja

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29

Warren, Richard H. "Benchmarking Quantum Optimization by Traveling Salesman Problems." International Journal on Applied Physics and Engineering 3 (December 31, 2024): 102–9. https://doi.org/10.37394/232030.2024.3.14.

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This paper recommends symmetric traveling salesman problems (TSPs) be used to benchmark quantum capability to find optimal solutions for combinatorial optimization problems. We add four features to the existing list of reasons supporting this recommendation. We discuss benchmark measures and how to overcome the lack of small TSP examples for standards. Significate open questions are identified. We comment about published articles related to the benchmark theme.

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30

Hass, Helge, Carolin Loos, Elba Raimúndez-Álvarez, Jens Timmer, Jan Hasenauer, and Clemens Kreutz. "Benchmark problems for dynamic modeling of intracellular processes." Bioinformatics 35, no. 17 (2019): 3073–82. http://dx.doi.org/10.1093/bioinformatics/btz020.

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Abstract Motivation Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark proble

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31

Riggs, H. R., K. M. Niimi, and L. L. Huang. "Two Benchmark Problems for Three-Dimensional, Linear Hydroelasticity." Journal of Offshore Mechanics and Arctic Engineering 129, no. 3 (2007): 149–57. http://dx.doi.org/10.1115/1.2746397.

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There has been substantial development in computer codes for three-dimensional linear hydroelasticity, but to the authors’ knowledge, there are no problems of sufficient complexity put forth in the open literature to serve as appropriate benchmark problems for a floating structure. We present in this paper detailed descriptions of two such problems for three-dimensional hydroelastic computer codes. The first structure is a rectangular, box-shaped “barge” with dimensions 100 m×10 m×2 m. The second is a Wigley hull with dimensions 100 m×10 m×4.5 m. In both cases, the deformational response is pr

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32

Hwang, Youngjin, Chaeyoung Lee, Soobin Kwak, et al. "Benchmark Problems for the Numerical Schemes of the Phase-Field Equations." Discrete Dynamics in Nature and Society 2022 (January 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/2751592.

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In this study, we present benchmark problems for the numerical methods of the phase-field equations. To find appropriate benchmark problems, we first perform a linear stability analysis and then take a growth mode solution as the benchmark problem, which is closely related to the dynamics of the original governing equations. As concrete examples, we perform convergence tests of the numerical methods of the Allen–Cahn (AC) and Cahn–Hilliard (CH) equations using the proposed benchmark problems. The one- and two-dimensional computational experiments confirm the accuracy and efficiency of the prop

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33

Su, Tzu Ching, and Hsien Te Lin. "Electricity Consumption Benchmark and Ranking System for Office Buildings in Taiwan." Applied Mechanics and Materials 71-78 (July 2011): 2352–56. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.2352.

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Energy use intensity (EUI) is a significant index for energy management in the building sector; however, previous research that determined an EUI benchmark for a type of building resulted in serious problems. Such earlier EUI benchmarks cannot fairly evaluate office buildings with different proportions of parking areas and different numbers of floors, becoming an obstacle for energy management. Therefore, this study proposes an area-weighted office building EUI benchmark that calculates according to office areas and parking areas. This study subsequently surveys the electricity consumption of

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34

Altinoz, Tolga. "Comparison of MOEA/D Variants on Benchmark Problems." International Journal of Multidisciplinary Studies and Innovative Technologies 6, no. 1 (2022): 11. http://dx.doi.org/10.36287/ijmsit.6.1.11.

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35

Ohtori, Y., R. E. Christenson, B. F. Spencer, and S. J. Dyke. "Benchmark Control Problems for Seismically Excited Nonlinear Buildings." Journal of Engineering Mechanics 130, no. 4 (2004): 366–85. http://dx.doi.org/10.1061/(asce)0733-9399(2004)130:4(366).

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36

Lin, Ying-Tsong, and Marcia J. Isakson. "Three-dimensional underwater sound propagation: Proposed benchmark problems." Journal of the Acoustical Society of America 137, no. 4 (2015): 2420. http://dx.doi.org/10.1121/1.4920830.

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37

Ebbeson, Gordon R., David J. Thomson, and Gary H. Brooke. "PE solutions to some internal‐wave benchmark problems." Journal of the Acoustical Society of America 109, no. 5 (2001): 2333. http://dx.doi.org/10.1121/1.4744189.

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38

Westwood, Evan K. "Ray model solutions to the benchmark wedge problems." Journal of the Acoustical Society of America 87, no. 4 (1990): 1539–45. http://dx.doi.org/10.1121/1.399454.

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39

Posypkin, Mikhail, and Alexander Usov. "Implementation and verification of global optimization benchmark problems." Open Engineering 7, no. 1 (2017): 470–78. http://dx.doi.org/10.1515/eng-2017-0050.

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AbstractThe paper considers the implementation and verification of a test suite containing 150 benchmarks for global deterministic box-constrained optimization. A C++ library for describing standard mathematical expressions was developed for this purpose. The library automate the process of generating the value of a function and its’ gradient at a given point and the interval estimates of a function and its’ gradient on a given box using a single description. Based on this functionality, we have developed a collection of tests for an automatic verification of the proposed benchmarks. The verif

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40

Frederick, D. K., and M. Rimer. "Benchmark Problems for Computer-Aided Control System Design." IFAC Proceedings Volumes 21, no. 8 (1988): 1–6. http://dx.doi.org/10.1016/s1474-6670(17)54924-1.

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41

Zhang, Zhan, Farzad Rahnema, Dingkang Zhang, Justin M. Pounders, and Abderrafi M. Ougouag. "Simplified two and three dimensional HTTR benchmark problems." Annals of Nuclear Energy 38, no. 5 (2011): 1172–85. http://dx.doi.org/10.1016/j.anucene.2010.11.020.

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42

Omidvar, Mohammad Nabi, Xiaodong Li, and Ke Tang. "Designing benchmark problems for large-scale continuous optimization." Information Sciences 316 (September 2015): 419–36. http://dx.doi.org/10.1016/j.ins.2014.12.062.

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43

Thompson, S., and P. G. Tuttle. "Benchmark fluid flow problems for continuous simulation languages." Computers & Mathematics with Applications 12, no. 3 (1986): 345–51. http://dx.doi.org/10.1016/0898-1221(86)90193-8.

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44

Idesman, A., H. Samajder, E. Aulisa, and P. Seshaiyer. "Benchmark problems for wave propagation in elastic materials." Computational Mechanics 43, no. 6 (2008): 797–814. http://dx.doi.org/10.1007/s00466-008-0346-3.

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45

Shores, Erik F. "Plutonium oxide benchmark problems for the SOURCES code." Applied Radiation and Isotopes 62, no. 5 (2005): 699–704. http://dx.doi.org/10.1016/j.apradiso.2004.12.002.

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46

Thrun, Michael C., and Alfred Ultsch. "Clustering benchmark datasets exploiting the fundamental clustering problems." Data in Brief 30 (June 2020): 105501. http://dx.doi.org/10.1016/j.dib.2020.105501.

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47

Wu, J. C., J. N. Yang, and A. K. Agrawal. "Applications of sliding mode control to benchmark problems." Earthquake Engineering & Structural Dynamics 27, no. 11 (1998): 1247–65. http://dx.doi.org/10.1002/(sici)1096-9845(1998110)27:11<1247::aid-eqe781>3.0.co;2-i.

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48

Trojanowski, Krzysztof, Mikołaj Raciborski, and Piotr Kaczyński. "Self-Adaptive Differential Evolution with Hybrid Rules of Perturbation for Dynamic Optimization." Journal of Telecommunications and Information Technology, no. 4 (June 28, 2023): 20–30. http://dx.doi.org/10.26636/jtit.2011.4.1173.

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In this paper an adaptive differential evolution approach for dynamic optimization problems is studied. A new benchmark suite Syringa is also presented. The suite allows to generate test-cases from a multiple number of dynamic optimization classes. Two dynamic benchmarks: Generalized Dynamic Benchmark Generator (GDBG) and Moving Peaks Benchmark (MPB) have been simulated in Syringa and in the presented research they were subject of the experimental research. Two versions of adaptive differential evolution approach, namely the jDE algorithm have been heavily tested: the pure version of jDE and j

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49

Sharma, Avinash, Rajesh Kumar, Akash Saxena, and B. K. Panigrahi. "Structured Clanning-Based Ensemble Optimization Algorithm: A Novel Approach for Solving Complex Numerical Problems." Modelling and Simulation in Engineering 2018 (December 9, 2018): 1–19. http://dx.doi.org/10.1155/2018/1851275.

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In this paper, a novel swarm intelligence-based ensemble metaheuristic optimization algorithm, called Structured Clanning-based Ensemble Optimization, is proposed for solving complex numerical optimization problems. The proposed algorithm is inspired by the complex and diversified behaviour present within the fission-fusion-based social structure of the elephant society. The population of elephants can consist of various groups with relationship between individuals ranging from mother-child bond, bond groups, independent males, and strangers. The algorithm tries to model this individualistic b

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50

Harmon, Thomas C., Robyn L. Smyth, Sudeep Chandra, et al. "Socioeconomic and Environmental Proxies for Comparing Freshwater Ecosystem Service Threats across International Sites: A Diagnostic Approach." Water 10, no. 11 (2018): 1578. http://dx.doi.org/10.3390/w10111578.

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In this work, we develop and test proxy-based diagnostic tools for comparing freshwater ecosystem services (FWES) risks across an international array of freshwater ecosystems. FWES threats are increasing rapidly under pressure from population, climate change, pollution, land use change, and other factors. We identified spatially explicit FWES threats estimates (referred to as threat benchmarks) and extracted watershed-specific values for an array of aquatic ecosystems in the Western Hemisphere (Ramsar sites). We compared these benchmark values to values extracted for sites associated with an i

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