Bibliographies thématiques / Optimization Benchmarking / Articles de revues

Articles de revues sur le sujet « Optimization Benchmarking »

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Auteur : Grafiati
Publié le 10 mars 2023

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1

Rojas-Labanda, Susana, et Mathias Stolpe. « Benchmarking optimization solvers for structural topology optimization ». Structural and Multidisciplinary Optimization 52, no 3 (17 mai 2015) : 527–47. http://dx.doi.org/10.1007/s00158-015-1250-z.

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Tedford, Nathan P., et Joaquim R. R. A. Martins. « Benchmarking multidisciplinary design optimization algorithms ». Optimization and Engineering 11, no 1 (20 mars 2009) : 159–83. http://dx.doi.org/10.1007/s11081-009-9082-6.

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Moré, Jorge J., et Stefan M. Wild. « Benchmarking Derivative-Free Optimization Algorithms ». SIAM Journal on Optimization 20, no 1 (janvier 2009) : 172–91. http://dx.doi.org/10.1137/080724083.

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Ajani, Oladayo S., Abhishek Kumar, Rammohan Mallipeddi, Swagatam Das et Ponnuthurai Nagaratnam Suganthan. « Benchmarking Optimization-Based Energy Disaggregation Algorithms ». Energies 15, no 5 (22 février 2022) : 1600. http://dx.doi.org/10.3390/en15051600.

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Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED.
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Hendrix, Eligius M. T., et Algirdas Lančinskas. « On Benchmarking Stochastic Global Optimization Algorithms ». Informatica 26, no 4 (1 janvier 2015) : 649–62. http://dx.doi.org/10.15388/informatica.2015.69.

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Korošec, Peter, et Tome Eftimov. « Multi-Objective Optimization Benchmarking Using DSCTool ». Mathematics 8, no 5 (22 mai 2020) : 839. http://dx.doi.org/10.3390/math8050839.

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By performing data analysis, statistical approaches are highly welcome to explore the data. Nowadays with the increases in computational power and the availability of big data in different domains, it is not enough to perform exploratory data analysis (descriptive statistics) to obtain some prior insights from the data, but it is a requirement to apply higher-level statistics that also require much greater knowledge from the user to properly apply them. One research area where proper usage of statistics is important is multi-objective optimization, where the performance of a newly developed algorithm should be compared with the performances of state-of-the-art algorithms. In multi-objective optimization, we are dealing with two or more usually conflicting objectives, which result in high dimensional data that needs to be analyzed. In this paper, we present a web-service-based e-Learning tool called DSCTool that can be used for performing a proper statistical analysis for multi-objective optimization. The tool does not require any special statistics knowledge from the user. Its usage and the influence of a proper statistical analysis is shown using data taken from a benchmarking study performed at the 2018 IEEE CEC (The IEEE Congress on Evolutionary Computation) is appropriate. Competition on Evolutionary Many-Objective Optimization.
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Doerr, Carola, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir et Thomas Bäck. « Benchmarking discrete optimization heuristics with IOHprofiler ». Applied Soft Computing 88 (mars 2020) : 106027. http://dx.doi.org/10.1016/j.asoc.2019.106027.

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Dolan, Elizabeth D., et Jorge J. Moré. « Benchmarking optimization software with performance profiles ». Mathematical Programming 91, no 2 (1 janvier 2002) : 201–13. http://dx.doi.org/10.1007/s101070100263.

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Liao, Yu-Ching, Chenyun Pan et Azad Naeemi. « Benchmarking and Optimization of Spintronic Memory Arrays ». IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 6, no 1 (juin 2020) : 9–17. http://dx.doi.org/10.1109/jxcdc.2020.2999270.

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Auger, Anne, Nikolaus Hansen et Marc Schoenauer. « Benchmarking of Continuous Black Box Optimization Algorithms ». Evolutionary Computation 20, no 4 (décembre 2012) : 481. http://dx.doi.org/10.1162/evco_e_00091.

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Parejo, José Antonio, Antonio Ruiz-Cortés, Sebastián Lozano et Pablo Fernandez. « Metaheuristic optimization frameworks : a survey and benchmarking ». Soft Computing 16, no 3 (28 août 2011) : 527–61. http://dx.doi.org/10.1007/s00500-011-0754-8.

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Auger, Anne, et Nikolaus Hansen. « A SIGEVO impact award for a paper arising from the COCO platform ». ACM SIGEVOlution 13, no 4 (22 janvier 2021) : 1–11. http://dx.doi.org/10.1145/3447929.3447930.

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The paper Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009 received the 2020 SIGEVO Impact Award for ten-year impact, which was announce at the ACM GECCO 2020 conference. The work compares the performance of 31 algorithms that had been benchmarked with the Comparing Continuous Optimizer platform (COCO) for the Black-Box-Optimization Benchmarking (BBOB) GECCO workshop in 2009.
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Kosecová, Janka, et Kateřina Kašpárková. « Optimization of the Lessons Learned Process in the Czech Armed Forces ». Land Forces Academy Review 24, no 2 (1 juin 2019) : 107–14. http://dx.doi.org/10.2478/raft-2019-0012.

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Abstract Lessons learned process enables an organization to learn from its mistakes and successes. Thanks to this process an organization can reduce the risk of repeating mistakes and increase the chance that successes are repeated. This article aims to present the results of the research, especially, results of the process analysis of sharing lessons in the Czech Armed Forces. Based on the procedural deficiencies identification, benchmarking comparisons with lessons learned process of other organizations were carried out. The purpose of the benchmarking was to do away with the above mentioned deficiencies. To use benchmarking was beneficial from the point of view of a simplified model processing. The final part of the article introduces set of recommendation including the optimized model of the lessons learned process.
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Kelly, Susan, Daphne McCleery, Judy King et Jay Madigan. « CUSTOMER SERVICE OPTIMIZATION AND BENCHMARKING MEASURING FOR SUCCESS ». Proceedings of the Water Environment Federation 2004, no 13 (1 janvier 2004) : 25–32. http://dx.doi.org/10.2175/193864704784137693.

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Yingcai Wu, Ka-Kei Chung, Huamin Qu, Xiaoru Yuan et S. C. Cheung. « Interactive Visual Optimization and Analysis for RFID Benchmarking ». IEEE Transactions on Visualization and Computer Graphics 15, no 6 (novembre 2009) : 1335–42. http://dx.doi.org/10.1109/tvcg.2009.156.

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Misherghi, Ghassan, Lihua Yuan, Zhendong Su, Chen-Nee Chuah et Hao Chen. « A general framework for benchmarking firewall optimization techniques ». IEEE Transactions on Network and Service Management 5, no 4 (décembre 2008) : 227–38. http://dx.doi.org/10.1109/tnsm.2009.041104.

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Pál, László, Tibor Csendes, Mihály Csaba Markót et Arnold Neumaier. « Black Box Optimization Benchmarking of the GLOBAL Method ». Evolutionary Computation 20, no 4 (décembre 2012) : 609–39. http://dx.doi.org/10.1162/evco_a_00089.

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GLOBAL is a multi-start type stochastic method for bound constrained global optimization problems. Its goal is to find the best local minima that are potentially global. For this reason it involves a combination of sampling, clustering, and local search. The role of clustering is to reduce the number of local searches by forming groups of points around the local minimizers from a uniformly sampled domain and to start few local searches in each of those groups. We evaluate the performance of the GLOBAL algorithm on the BBOB 2009 noiseless testbed, containing problems which reflect the typical difficulties arising in real-world applications. The obtained results are also compared with those obtained form the simple multi-start procedure in order to analyze the effects of the applied clustering rule. An improved parameterization is introduced in the GLOBAL method and the performance of the new procedure is compared with the performance of the MATLAB GlobalSearch solver by using the BBOB 2010 test environment.
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Pintér, János D., et Frank J. Kampas. « Benchmarking nonlinear optimization software in technical computing environments ». TOP 21, no 1 (17 août 2011) : 133–62. http://dx.doi.org/10.1007/s11750-011-0209-5.

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Abdullah, D., Tulus, S. Suwilo, S. Effendi et Hartono. « DEA Optimization with Neural Network in Benchmarking Process ». IOP Conference Series : Materials Science and Engineering 288 (janvier 2018) : 012041. http://dx.doi.org/10.1088/1757-899x/288/1/012041.

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Costa, Lino, Isabel Espírito Santo et Pedro Oliveira. « Benchmarking deterministic optimization algorithms using an outranking approach ». Optimization Methods and Software 31, no 6 (6 juin 2016) : 1149–68. http://dx.doi.org/10.1080/10556788.2016.1191484.

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Huang, Hsu-Yao, Lung-Chieh Lin, Ming-Tsun Ke, Tamilarasan Sathesh et Wen-Shing Lee. « Energy performance evaluation for benchmarking school buildings using dynamic clustering analysis and particle swarm optimization ». Building Services Engineering Research and Technology 41, no 4 (1 octobre 2019) : 429–40. http://dx.doi.org/10.1177/0143624419879001.

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Benchmarking the energy performance of buildings has received increasing attention as striving for energy efficiency through more effective energy management has become a major concern of governments. Various methods for classifying building energy performance have been developed, and the clustering technique is considered one of the best approaches. This paper proposes a method utilizing dynamic clustering to analyze the electricity consumption patterns of buildings to decide the optimal cluster number and allocate the buildings to corresponding clusters for energy benchmarking. For the evaluation of number of clusters, this article has employed the inter–intra clustering method with particle swarm optimization algorithm. The electricity consumption data were collected through an energy survey performed in 30 junior high schools in Taipei, Taiwan. In a traditional method, the 30 schools would be grouped into one same cluster and the energy benchmarking report an average value of 541.4 kWh/year per student. The proposed method that took different electricity consumption patterns of the schools into consideration produced more detailed results as follows: the optimal cluster number was 3 with an inter–intra index value of 0.708, and the energy benchmarking index of these three clusters read, respectively, 362, 512, and 851 kWh/year per student. Practical application: The study proposed an innovative dynamic clustering technique to decide the optimal cluster number and allocate the assessed buildings. The results showed that compared to a traditional approach that tended to group assessed buildings into one cluster, the proposed method was able to classify the buildings into three clusters for further benchmarking. This method can be used by governments and large corporations. For example, in Hong Kong, primary schools are grouped into one cluster for energy benchmarking. Using the proposed method can further classify primary schools into more clusters; benchmarking index can then be developed for each cluster.
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Mettler, Bérénice, Zhaodan Kong, Chad Goerzen et Matthew Whalley. « Guidance Performance Benchmarking for Autonomous Rotorcraft ». Journal of the American Helicopter Society 59, no 4 (1 octobre 2014) : 1–16. http://dx.doi.org/10.4050/jahs.59.042009.

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This paper describes a framework for performance evaluation of autonomous guidance systems. The elements of the framework consist of a set of spatial geometries, flight tasks, performance metrics, a flightdynamic model, and baseline solutions. The spatial benchmarks consist of six tasks in simple geometrical environments and 10 tasks in more complex urban environments based on a real digital terrain elevation map. The framework also includes a set of performance metrics used to compare trajectories. The performance baselines used in the proposed framework are near-optimal solutions computed using one of two trajectory optimization methods: numerical optimization based on nonlinear programming for the simple geometric environments and a motion primitive automaton for problems involving the urban environments. The paper concludes with a demonstration of the benchmarking framework using the Obstacle Field Navigation system developed by the Army Aeroflightdynamics Directorate.
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Plevris, Vagelis, et German Solorzano. « A Collection of 30 Multidimensional Functions for Global Optimization Benchmarking ». Data 7, no 4 (11 avril 2022) : 46. http://dx.doi.org/10.3390/data7040046.

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A collection of thirty mathematical functions that can be used for optimization purposes is presented and investigated in detail. The functions are defined in multiple dimensions, for any number of dimensions, and can be used as benchmark functions for unconstrained multidimensional single-objective optimization problems. The functions feature a wide variability in terms of complexity. We investigate the performance of three optimization algorithms on the functions: two metaheuristic algorithms, namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and one mathematical algorithm, Sequential Quadratic Programming (SQP). All implementations are done in MATLAB, with full source code availability. The focus of the study is both on the objective functions, the optimization algorithms used, and their suitability for solving each problem. We use the three optimization methods to investigate the difficulty and complexity of each problem and to determine whether the problem is better suited for a metaheuristic approach or for a mathematical method, which is based on gradients. We also investigate how increasing the dimensionality affects the difficulty of each problem and the performance of the optimizers. There are functions that are extremely difficult to optimize efficiently, especially for higher dimensions. Such examples are the last two new objective functions, F29 and F30, which are very hard to optimize, although the optimum point is clearly visible, at least in the two-dimensional case.
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Dempster, M. A. H., E. A. Germano, M. Medova, M. I. Rietbergen, F. Sandrini, M. Scrowston et N. Zhang. « DC pension fund benchmarking with fixed-mix portfolio optimization ». Quantitative Finance 7, no 4 (août 2007) : 365–70. http://dx.doi.org/10.1080/14697680701511196.

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Tokos, Hella, Zorka Novak Pintarič et Yongrong Yang. « Bi-objective optimization of a water network via benchmarking ». Journal of Cleaner Production 39 (janvier 2013) : 168–79. http://dx.doi.org/10.1016/j.jclepro.2012.07.051.

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Schneider, Philipp-Immanuel, Xavier Garcia Santiago, Victor Soltwisch, Martin Hammerschmidt, Sven Burger et Carsten Rockstuhl. « Benchmarking Five Global Optimization Approaches for Nano-optical Shape Optimization and Parameter Reconstruction ». ACS Photonics 6, no 11 (17 septembre 2019) : 2726–33. http://dx.doi.org/10.1021/acsphotonics.9b00706.

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Wortmann, Thomas. « Genetic evolution vs. function approximation : Benchmarking algorithms for architectural design optimization ». Journal of Computational Design and Engineering 6, no 3 (20 septembre 2018) : 414–28. http://dx.doi.org/10.1016/j.jcde.2018.09.001.

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Abstract This article presents benchmark results from seven simulation-based problems from structural, building energy, and daylight optimization. Growing applications of parametric design and performance simulations in architecture, engineering, and construction allow the harnessing of simulation-based, or black-box, optimization in the search for less resource- and/or energy consuming designs. In architectural design optimization (ADO) practice and research, the most commonly applied black-box algorithms are genetic algorithms or other metaheuristics, to the neglect of more current, global direct search or model-based, methods. Model-based methods construct a surrogate model (i.e., an approximation of a fitness landscape) that they refine during the optimization process. This benchmark compares metaheuristic, direct search, and model-based methods, and concludes that, for the given evaluation budget and problems, the model-based method (RBFOpt) is the most efficient and robust, while the tested genetic algorithms perform poorly. As such, this article challenges the popularity of genetic algorithms in ADO, as well as the practice of using them for one-to-one comparisons to justify algorithmic innovations. Highlights Benchmarks optimization algorithms on structural, energy, and daylighting problems. Benchmarks metaheuristic, direct search, and model-based optimization methods. Challenges the popularity of genetic algorithms in architectural design optimization. Presents model-based methods as a more efficient and reliable alternative.
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B. SANTOS, RICARDO, PETER W. HART, DOUGLAS C. PRYKE et JOHN VANDERHEIDE. « Bleaching optimization at WestRock mill in Covington, Virginia ». September 2016 15, no 9 (1 octobre 2016) : 581–86. http://dx.doi.org/10.32964/tj15.9.581.

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The WestRock mill in Covington, VA, USA, initiated a long term diagnostic and optimization program for all three of its bleaching lines. Benchmarking studies were used to help identify optimization opportunities. Capital expenditures for mixing improvement, filtrate changes, equipment repair, other equipment changes, and species changes were outside the scope of this work. This focus of this paper is the B line, producing southern hardwood pulp in a D(EP)DD sequence at 88% GE brightness. The benchmarking study and optimization work identified the following opportunities for improved performance: nonoptimal addition of caustic and hydrogen peroxide to the (EP) stage, carryover of D0 filtrate to the (EP) stage, and carryover of (EP) filtrate to the D1 stage. As a result of actions the mill undertook to address these opportunities, D0 kappa factor decreased about 5%, sodium hydroxide consumption in the (EP) stage decreased about 35%, chlorine dioxide consumption in the D1 stage decreased about 25%, and overall bleaching cost decreased about 15%.
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Sugiarto, Indar, Doddy Prayogo, Henry Palit, Felix Pasila, Resmana Lim, Agustinus Noertjahyana, I. Gede Widyadana, Surya Hermawan, Agustinus Bimo Gumelar et Bernardo Nugroho Yahya. « Custom Built of Smart Computing Platform for Supporting Optimization Methods and Artificial Intelligence Research ». Proceedings of the Pakistan Academy of Sciences : A. Physical and Computational Sciences 58, S (11 octobre 2021) : 59–64. http://dx.doi.org/10.53560/ppasa(58-sp1)733.

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This paper describes a prototype of a computing platform dedicated to artificial intelligence explorations. The platform, dubbed as PakCarik, is essentially a high throughput computing platform with GPU (graphics processing units) acceleration. PakCarik is an Indonesian acronym for Platform Komputasi Cerdas Ramah Industri Kreatif, which can be translated as “Creative Industry friendly Intelligence Computing Platform”. This platform aims to provide complete development and production environment for AI-based projects, especially to those that rely on machine learning and multiobjective optimization paradigms. The method for constructing PakCarik was based on a computer hardware assembling technique that uses commercial off-the-shelf hardware and was tested on several AI-related application scenarios. The testing methods in this experiment include: high-performance lapack (HPL) benchmarking, message passing interface (MPI) benchmarking, and TensorFlow (TF) benchmarking. From the experiment, the authors can observe that PakCarik's performance is quite similar to the commonly used cloud computing services such as Google Compute Engine and Amazon EC2, even though falls a bit behind the dedicated AI platform such as Nvidia DGX-1 used in the benchmarking experiment. Its maximum computing performance was measured at 326 Gflops. The authors conclude that PakCarik is ready to be deployed in real-world applications and it can be made even more powerful by adding more GPU cards in it.
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Mittelmann, H. D. « Benchmarking interior point Lp/Qp solvers ». Optimization Methods and Software 11, no 1-4 (janvier 1999) : 655–70. http://dx.doi.org/10.1080/10556789908805767.

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Häse, Florian, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Melodie Christensen, Elena Liles, Jason E. Hein et Alán Aspuru-Guzik. « Olympus : a benchmarking framework for noisy optimization and experiment planning ». Machine Learning : Science and Technology 2, no 3 (12 juillet 2021) : 035021. http://dx.doi.org/10.1088/2632-2153/abedc8.

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Christoforidou, Popi, George Bariamis, Maria Iosifidou, Eri Nikolaidou et Petros Samaras. « Energy Benchmarking and Optimization of Wastewater Treatment Plants in Greece ». Environmental Sciences Proceedings 2, no 1 (31 août 2020) : 36. http://dx.doi.org/10.3390/environsciproc2020002036.

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Wastewater treatment, as a crucial component of the urban water environment, consists of several energy-consumptive stages, therefore efficiency and energy savings measures are essential to maintain them as environmentally sustainable and economically viable. Operational and technical data from WWTPs in Greece have been collected as well as a sample from 61 facilities with key energy profile components. Energy consumption was assessed by specific key performance indicators (KPIs); specific energy consumption expressed per population equivalent (from 3 to 150 kWh/PE), per cubic meter treated (from 0.2 to 2.0 kWh/m3) and per unit of organic load removed (from 0.03 to 7.13 kWh/CODremoved).
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Yum, Keun-Chul, et Se Jung Lee. « Benchmarking of Design Optimization Frameworks In View of Excel Interface ». Transactions of the Korean Society of Mechanical Engineers A 29, no 3 (1 mars 2005) : 403–10. http://dx.doi.org/10.3795/ksme-a.2005.29.3.403.

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Gray, Justin, Kenneth T. Moore, Tristan A. Hearn et Bret A. Naylor. « Standard Platform for Benchmarking Multidisciplinary Design Analysis and Optimization Architectures ». AIAA Journal 51, no 10 (octobre 2013) : 2380–94. http://dx.doi.org/10.2514/1.j052160.

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Mittal, Ruchi R., Ross A. McKinnon et Michael J. Sorich. « Comparison Data Sets for Benchmarking QSAR Methodologies in Lead Optimization ». Journal of Chemical Information and Modeling 49, no 7 (2 juillet 2009) : 1810–20. http://dx.doi.org/10.1021/ci900117m.

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Villaverde, Alejandro F., Fabian Fröhlich, Daniel Weindl, Jan Hasenauer et Julio R. Banga. « Benchmarking optimization methods for parameter estimation in large kinetic models ». Bioinformatics 35, no 5 (23 août 2018) : 830–38. http://dx.doi.org/10.1093/bioinformatics/bty736.

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Matott, L. Shawn, Bryan A. Tolson et Masoud Asadzadeh. « A benchmarking framework for simulation-based optimization of environmental models ». Environmental Modelling & ; Software 35 (juillet 2012) : 19–30. http://dx.doi.org/10.1016/j.envsoft.2012.02.002.

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El-Abbasy, Mohammed S., Ashraf Elazouni et Tarek Zayed. « Finance-based scheduling multi-objective optimization : Benchmarking of evolutionary algorithms ». Automation in Construction 120 (décembre 2020) : 103392. http://dx.doi.org/10.1016/j.autcon.2020.103392.

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Mohammed, R. T., R. Yaakob, A. A. Zaidan, N. M. Sharef, R. H. Abdullah, B. B. Zaidan et K. A. Dawood. « Review of the Research Landscape of Multi-Criteria Evaluation and Benchmarking Processes for Many-Objective Optimization Methods : Coherent Taxonomy, Challenges and Recommended Solution ». International Journal of Information Technology & ; Decision Making 19, no 06 (17 octobre 2020) : 1619–93. http://dx.doi.org/10.1142/s0219622020300049.

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Evaluation and benchmarking of many-objective optimization (MaOO) methods are complicated. The rapid development of new optimization algorithms for solving problems with many objectives has increased the necessity of developing performance indicators or metrics for evaluating the performance quality and comparing the competing optimization algorithms fairly. Further investigations are required to highlight the limitations of how criteria/metrics are determined and the consistency of the procedures with the evaluation and benchmarking processes of MaOO. A review is conducted in this study to map the research landscape of multi-criteria evaluation and benchmarking processes for MaOO into a coherent taxonomy. Then contentious and challenging issues related to evaluation are highlighted, and the performance of optimization algorithms for MaOO is benchmarked. The methodological aspects of the evaluation and selection of MaOO algorithms are presented as the recommended solution on the basis of four distinct and successive phases. First, in the determination phase, the evaluation criteria of MaOO are collected, classified and grouped for testing experts’ consensus on the most suitable criteria. Second, the identification phase involves the process of establishing a decision matrix via a crossover of the ‘evaluation criteria’ and MaOO’, and the level of importance of each selective criteria and sub-criteria from phase one is computed to identify its weight value by using the best–worst method (BWM). Third, the development phase involves the creation of a decision matrix for MaOO selection on the basis of the integrated BWM and VIKOR method. Last, the validation phase involves the validation of the proposed solution.
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Kreitlein, Sven, Tobias Rackow et Jörg Franke. « Energy KPI’s, Challenges for Sustainable Manufacturing Strategies, Analysis of Existing Rules and Indicators in an Industrial Environment in Relation to the Establishment of Energy Benchmark ». Advanced Materials Research 1018 (septembre 2014) : 501–8. http://dx.doi.org/10.4028/www.scientific.net/amr.1018.501.

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As this paper demonstrates, benchmarking is a powerful concept for increasing a company's success. It visualizes the differences in performance through internal or external indicators and is applied in almost all corporate divisions successfully. An important part of the benchmarking process is the selection of indicators that allow for a significant comparison. The requirements linked to the indicators are derived from the objectives of the specific benchmarking project. The use of benchmarking with respect to energy efficiency in product manufacturing promises a transparent presentation of performance differences and the possibility to derive optimization potentials - provided that appropriate energy figures can be found. The future challenge is to develop a system with standardized billing and key energy indicators that enable a cross-comparison of the energy efficiency in the production. This project is described in the research project E|Benchmark, part of the research network Green Factory Bavaria and funded by the Bavarian State Ministry for Science, Education and Arts.
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Brockfeld, Elmar, Reinhart D. Kühne, Alexander Skabardonis et Peter Wagner. « Toward Benchmarking of Microscopic Traffic Flow Models ». Transportation Research Record : Journal of the Transportation Research Board 1852, no 1 (janvier 2003) : 124–29. http://dx.doi.org/10.3141/1852-16.

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Several microscopic traffic flow models were tested with a publicly available data set. The task was to predict the travel times between several observers along a one-lane rural road, given as boundary conditions the flow into this road and the flow out of it. By using nonlinear optimization, the best matching set of parameters for each of the models was estimated. For this particular data set, the models that performed best were the ones with the smallest number of parameters. The average error rate of the best models is about 16%; however, this value is not very reliable: the error rate fluctuates between 2.5% and 25% for different parts of the data set.
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Błaszczyk, Jacek, Andrzej Karbowski et Krzysztof Malinowski. « Object Library of Algorithms for Dynamic Optimization Problems : Benchmarking SQP and Nonlinear Interior Point Methods ». International Journal of Applied Mathematics and Computer Science 17, no 4 (1 décembre 2007) : 515–37. http://dx.doi.org/10.2478/v10006-007-0043-y.

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Object Library of Algorithms for Dynamic Optimization Problems: Benchmarking SQP and Nonlinear Interior Point MethodsThe main purpose of this paper is to describe the design, implementation and possibilities of our object-oriented library of algorithms for dynamic optimization problems. We briefly present library classes for the formulation and manipulation of dynamic optimization problems, and give a general survey of solver classes for unconstrained and constrained optimization. We also demonstrate methods of derivative evaluation that we used, in particular automatic differentiation. Further, we briefly formulate and characterize the class of problems solved by our optimization classes. The solution of dynamic optimization problems with general constraints is performed by transformation into structured large-scale nonlinear programming problems and applying methods for nonlinear optimization. Two main algorithms of solvers for constrained dynamic optimization are presented in detail: the sequential quadratic programming (SQP) exploring the multistage structure of the dynamic optimization problem during the solution of a sequence of quadratic subproblems, and the nonlinear interior-point method implemented in a general-purpose large-scale optimizer IPOPT. At the end, we include a typical numerical example of the application of the constrained solvers to a large-scale discrete-time optimal control problem and we use the performance profiles methodology to compare the efficiency and robustness of different solvers or different options of the same solver. In conclusions, we summarize our experience gathered during the library development.
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Castano, Juan A., Joseph Humphreys, Enrico Mingo Hoffman, Noelia Fernández Talavera, Maria Cristina Rodriguez Sanchez et Chengxu Zhou. « Benchmarking Dynamic Balancing Controllers for Humanoid Robots ». Robotics 11, no 5 (19 octobre 2022) : 114. http://dx.doi.org/10.3390/robotics11050114.

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This paper presents a comparison study of three control design approaches for humanoid balancing based on the Center of Mass (CoM) stabilization and body posture adjustment. The comparison was carried out under controlled circumstances allowing other researchers to replicate and compare our results with their own. The feedback control from state space design is based on simple models and provides sufficient robustness to control complex and high Degrees of Freedom (DoFs) systems, such as humanoids. The implemented strategies allow compliant behavior of the robot in reaction to impulsive or periodical disturbances, resulting in a smooth and human-like response while considering constraints. In this respect, we implemented two balancing strategies to compensate for the CoM deviation. The first one uses the robot’s capture point as a stability principle and the second one uses the Force/Torque sensors at the ankles to define a CoM reference that stabilizes the robot. In addition, was implemented a third strategy based on upper body orientation to absorb external disturbances and counterbalance them. Even though the balancing strategies are implemented independently, they can be merged to further increase balancing performance. The proposed strategies were previously applied on different humanoid bipedal platforms, however, their performance could not be properly benchmarked before. With this concern, this paper focuses on benchmarking in controlled scenarios to help the community in comparing different balance techniques. The key performance indicators (KPIs) used in our comparison are the CoM deviation, the settling time, the maximum measured orientation, passive gait measure, measured ankles torques, and reconstructed Center of Pressure (CoP). The benchmarking experiments were carried out in simulations and using the facility at Istituto Italiano di Tecnologia on the REEM-C humanoid robot provided by PAL robotics inside the EU H2020 project EUROBENCH framework.
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Duquesnoy, Marc, Elixabete Ayerbe, Iker Boyano et Alejandro A. Franco. « Electrode Heterogeneity Understanding through Data-Driven Benchmarking for Manufacturing Process Optimization ». ECS Meeting Abstracts MA2021-01, no 2 (30 mai 2021) : 192. http://dx.doi.org/10.1149/ma2021-012192mtgabs.

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Van Woensel, William, et Syed Sibte Raza Abidi. « Benchmarking semantic reasoning on mobile platforms : Towards optimization using OWL2 RL ». Semantic Web 10, no 4 (23 mai 2019) : 637–63. http://dx.doi.org/10.3233/sw-180315.

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Al Khatib, Sultan M., et Joost Noppen. « Benchmarking and Comparison of Software Project Human Resource Allocation Optimization Approaches ». ACM SIGSOFT Software Engineering Notes 41, no 6 (5 janvier 2017) : 1–6. http://dx.doi.org/10.1145/3011286.3011305.

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Costentin, Cyrille, et Jean-Michel Savéant. « Homogeneous Molecular Catalysis of Electrochemical Reactions : Catalyst Benchmarking and Optimization Strategies ». Journal of the American Chemical Society 139, no 24 (13 juin 2017) : 8245–50. http://dx.doi.org/10.1021/jacs.7b02879.

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Ahmad, Ishfaq, Hafiz Fahad Sheikh et Alex Aved. « Benchmarking the task scheduling algorithms for performance, energy, and temperature optimization ». Sustainable Computing : Informatics and Systems 25 (mars 2020) : 100339. http://dx.doi.org/10.1016/j.suscom.2019.07.002.

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Gallagher, Marcus. « Towards improved benchmarking of black-box optimization algorithms using clustering problems ». Soft Computing 20, no 10 (10 mars 2016) : 3835–49. http://dx.doi.org/10.1007/s00500-016-2094-1.

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Tanabe, Ryoji, Hisao Ishibuchi et Akira Oyama. « Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios ». IEEE Access 5 (2017) : 19597–619. http://dx.doi.org/10.1109/access.2017.2751071.

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