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Artículos de revistas sobre el tema "Multi-objective Design"

Autor: Grafiati

Publicado: 14 de diciembre de 2024

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1

Min, Xinyuan, Jaap Sok, Feije de Zwart y Alfons Oude Lansink. "Multi-stakeholder multi-objective greenhouse design optimization". Agricultural Systems 215 (marzo de 2024): 103855. http://dx.doi.org/10.1016/j.agsy.2024.103855.

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2

Freier, Lars y Eric von Lieres. "Robust multi-objective process design". New Biotechnology 33 (julio de 2016): S27. http://dx.doi.org/10.1016/j.nbt.2016.06.822.

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3

Sun, Qi, Tinghuan Chen, Siting Liu, Jianli Chen, Hao Yu y Bei Yu. "Correlated Multi-objective Multi-fidelity Optimization for HLS Directives Design". ACM Transactions on Design Automation of Electronic Systems 27, n.º 4 (31 de julio de 2022): 1–27. http://dx.doi.org/10.1145/3503540.

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High-level synthesis (HLS) tools have gained great attention in recent years because it emancipates engineers from the complicated and heavy hardware description language writing and facilitates the implementations of modern applications (e.g., deep learning models) on Field-programmable Gate Array (FPGA) , by using high-level languages and HLS directives. However, finding good HLS directives is challenging, due to the time-consuming design processes, the balances among different design objectives, and the diverse fidelities (accuracies of data) of the performance values between the consecutive FPGA design stages. To find good HLS directives, a novel automatic optimization algorithm is proposed to explore the Pareto designs of the multiple objectives while making full use of the data with different fidelities from different FPGA design stages. Firstly, a non-linear Gaussian process (GP) is proposed to model the relationships among the different FPGA design stages. Secondly, for the first time, the GP model is enhanced as correlated GP (CGP) by considering the correlations between the multiple design objectives, to find better Pareto designs. Furthermore, we extend our model to be a deep version deep CGP (DCGP) by using the deep neural network to improve the kernel functions in Gaussian process models, to improve the characterization capability of the models, and learn better feature representations. We test our design method on some public benchmarks (including general matrix multiplication and sparse matrix-vector multiplication) and deep learning-based object detection model iSmart2 on FPGA. Experimental results show that our methods outperform the baselines significantly and facilitate the deep learning designs on FPGA.

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4

YAMASHINA, Hajime, Susumu OKUMURA y Yoshimasa KONDO. "Parameter Design with Multi Objective Characteristics." Journal of the Japan Society for Precision Engineering 58, n.º 3 (1992): 516–20. http://dx.doi.org/10.2493/jjspe.58.516.

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5

Kor, Jean, Xiang Chen, Zhizhong Sun y Henry Hu. "Casting Design Through Multi-Objective Optimization". IFAC Proceedings Volumes 44, n.º 1 (enero de 2011): 11642–47. http://dx.doi.org/10.3182/20110828-6-it-1002.01726.

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6

Joseph, Shaine, Hyung W. Kang y Uday K. Chakraborty. "Lens design as multi-objective optimisation". International Journal of Automation and Control 5, n.º 3 (2011): 189. http://dx.doi.org/10.1504/ijaac.2011.042851.

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7

Sanchis, J., M. Martinez y X. Blasco. "Multi-objective engineering design using preferences". Engineering Optimization 40, n.º 3 (marzo de 2008): 253–69. http://dx.doi.org/10.1080/03052150701693057.

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8

Eckert, Jony Javorski, Fabio Mazzariol Santiciolli, Ludmila C. A. Silva y Franco Giuseppe Dedini. "Vehicle drivetrain design multi-objective optimization". Mechanism and Machine Theory 156 (febrero de 2021): 104123. http://dx.doi.org/10.1016/j.mechmachtheory.2020.104123.

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9

Pelinescu, Diana M. y Michael Yu Wang. "Multi-objective optimal fixture layout design". Robotics and Computer-Integrated Manufacturing 18, n.º 5-6 (octubre de 2002): 365–72. http://dx.doi.org/10.1016/s0736-5845(02)00027-3.

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10

Lim, Dudy, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff y Bu Sung Lee. "Inverse multi-objective robust evolutionary design". Genetic Programming and Evolvable Machines 7, n.º 4 (16 de septiembre de 2006): 383–404. http://dx.doi.org/10.1007/s10710-006-9013-7.

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11

Kiranoudis, C. T., Z. B. Maroulis y D. Marinos-Kouris. "PRODUCT QUALITY MULTI-OBJECTIVE DRYER DESIGN". Drying Technology 17, n.º 10 (noviembre de 1999): 2251–70. http://dx.doi.org/10.1080/07373939908917682.

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12

C.Kavitha, C. Kavitha y C. Vijayalakshmi C. Vijayalakshmi. "Design and Implementation of Fuzzy Multi Objective Optimization Model for Production Planning". Indian Journal of Applied Research 3, n.º 12 (1 de octubre de 2011): 372–75. http://dx.doi.org/10.15373/2249555x/dec2013/113.

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13

Deb, Kalyanmoy y Sachin Jain. "Multi-Speed Gearbox Design Using Multi-Objective Evolutionary Algorithms". Journal of Mechanical Design 125, n.º 3 (1 de septiembre de 2003): 609–19. http://dx.doi.org/10.1115/1.1596242.

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Optimal design of a multi-speed gearbox involves different types of decision variables and objectives. Due to lack of efficient classical optimization techniques, such problems are usually decomposed into tractable subproblems and solved. Moreover, in most cases the explicit mathematical expressions of the problem formulation is exploited to arrive at the optimal solutions. In this paper, we demonstrate the use of a multi-objective evolutionary algorithm, which is capable of solving the original problem involving mixed discrete and real-valued parameters and more than one objectives, and is capable of finding multiple nondominated solutions in a single simulation run. On a number of instantiations of the gearbox design problem having different complexities, the efficacy of NSGA-II in handling different types of decision variables, constraints, and multiple objectives are demonstrated. A highlight of the suggested procedure is that a post-optimal investigation of the obtained solutions allows a designer to discover important design principles which are otherwise difficult to obtain using other means.

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14

Mohamed, Nejlaoui, Najlawi Bilel y Ali Sulaiman Alsagri. "A multi-objective methodology for multi-criteria engineering design". Applied Soft Computing 91 (junio de 2020): 106204. http://dx.doi.org/10.1016/j.asoc.2020.106204.

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15

Frank, Christopher P., Renaud A. Marlier, Olivia J. Pinon-Fischer y Dimitri N. Mavris. "Evolutionary multi-objective multi-architecture design space exploration methodology". Optimization and Engineering 19, n.º 2 (22 de enero de 2018): 359–81. http://dx.doi.org/10.1007/s11081-018-9373-x.

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16

Tawhid, Mohamed A. y Vimal Savsani. "Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems". Neural Computing and Applications 31, S2 (12 de junio de 2017): 915–29. http://dx.doi.org/10.1007/s00521-017-3049-x.

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17

Patel, Prashant, Paras Shah y Zissimos P. Mourelatos. "Piston Design Using Multi-Objective Reliability-Based Design Optimization". SAE International Journal of Materials and Manufacturing 3, n.º 1 (12 de abril de 2010): 493–511. http://dx.doi.org/10.4271/2010-01-0907.

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18

Adámek, Mikuláš y Rastislav Toman. "RANGE EXTENDER ICE MULTI-PARAMETRIC MULTI-OBJECTIVE OPTIMIZATION". MECCA Journal of Middle European Construction and Design of Cars 18, n.º 1 (10 de noviembre de 2021): 10. http://dx.doi.org/10.14311/mecdc.2021.01.02.

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Range Extended Electric Vehicles (REEV) are still one of the suitable concepts for modern sustainable low emission vehicles. REEV is equipped with a small and lightweight unit, comprised usually of an internal combustion engine with an electric generator, and has thus the technical potential to overcome the main limitations of a pure electric vehicle – range anxiety, overall driving range, heating, and air-conditioning demands – using smaller battery: saving money, and raw materials. Even though several REx ICE concepts were designed in past, most of the available studies lack more complex design and optimization approach, not exploiting the advantageous single point operation of these engines. Resulting engine designs are usually rather conservative, not optimized for the best efficiency. This paper presents a multi-parametric and multi-objective optimization approach, that is applied on a REx ICE. Our optimization toolchain combines a parametric GT-Suite ICE simulation model, modeFRONTIER optimization software with various optimization strategies, and a parametric CAD model, that first provides some simulation model inputs, and second also serves for the final designs’ feasibility check. The chosen ICE concept is a 90 degrees V-twin engine, four-stroke, spark-ignition, naturally aspirated, port injected, OHV engine. The optimization goal is to find the thermodynamic optima for three different design scenarios of our concept – three different engine displacements – addressing the compactness requirement of a REx ICE. The optimization results show great fuel efficiency potential by applying our optimization methodology, following the general trends in increasing ICE efficiency, and power for a naturally aspirated concept.

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19

Martz, M. y W. L. Neu. "Multi-Objective Optimization of an Autonomous Underwater Vehicle". Marine Technology Society Journal 43, n.º 2 (1 de mayo de 2009): 48–60. http://dx.doi.org/10.4031/mtsj.43.2.6.

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AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

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20

Sutagundar, M., B. G. Sheeparamatti y D. S. Jangamshetti. "Multi-objective Design Optimization of Microdisk Resonator". Nanoscience & Nanotechnology-Asia 10, n.º 4 (26 de agosto de 2020): 478–85. http://dx.doi.org/10.2174/2210681209666190912152649.

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Objective: This paper presents a multi-objective design optimization of MEMS disk resonator using two techniques. Methods: Determining the optimized dimensions of disk resonator for a particular resonance frequency so as to achieve higher quality factor and lower motional resistance is attempted. One technique used is constraint-based multi-objective optimization using the interior-point algorithm. The second technique is based on multi-objective genetic algorithm. Results: The algorithms are implemented using MATLAB. The two techniques of optimization are compared. Conclusion: The developed optimization methods can provide faster design optimization compared to full-wave simulators resulting in significant reduction of design time.

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21

Devi, R. Vasundhara, S. Siva Sathya, Nilabh Kumar y Mohane Selvaraj Coumar. "Multi-objective Monkey Algorithm for Drug Design". International Journal of Intelligent Systems and Applications 11, n.º 3 (8 de marzo de 2019): 31–41. http://dx.doi.org/10.5815/ijisa.2019.03.04.

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22

Barone, Salvatore, Marcello Traiola, Mario Barbareschi y Alberto Bosio. "Multi-Objective Application-Driven Approximate Design Method". IEEE Access 9 (2021): 86975–93. http://dx.doi.org/10.1109/access.2021.3087858.

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23

Pereira, Filipe, Patrick M. Reed, y Daniel Selva. "Multi-Objective Design of a Lunar GNSS". NAVIGATION: Journal of the Institute of Navigation 69, n.º 1 (2022): navi.504. http://dx.doi.org/10.33012/navi.504.

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24

Zheng, Shan Suo, Zhi Qiang Li, Yi Hu, Qing Lin Tao y Wei Wang. "Multi-Objective Optimization Design of Hybrid Structure". Advanced Materials Research 243-249 (mayo de 2011): 20–25. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.20.

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The primary goal of failure modes-based optimization design which is to study the performance of structure without shocking absorption device is to transform the non-ideal failure modes of structure into the ideal failure modes, and then a small probability of the structure damage can be obtained. Although the study of this field is significant, no paper has so far attempted to study. Taking the cost of the structure into consideration, this paper aims at the failure modes-based optimization design. Therefore, an optimal approach based on failure modes with the ability to limit the cost is proposed. The procedure to obtain the failure modes-based optimization includes two phases, the concrete optimization and the shaped steel optimization. At last a reinforced concrete frame-shear wall structure is cited to verify the method developed. It is concluded that the method can supply an effective way to reduce both the damage and the cost of steel reinforced concrete framework-core tube structure.

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25

Brauers, Willem Karel M., Edmundas Kazimieras Zavadskas, Friedel Peldschus y Zenonas Turskis. "MULTI‐OBJECTIVE DECISION‐MAKING FOR ROAD DESIGN". TRANSPORT 23, n.º 3 (30 de septiembre de 2008): 183–93. http://dx.doi.org/10.3846/1648-4142.2008.23.183-193.

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Multi‐objective analysis is a popular tool to solve many economic, managerial and construction problems. The objective of this research is to develop and implement a methodology for multi‐objective optimization of multi‐alternative decisions in road construction. After a rough overview of the articles dealing with the multi‐objective decision and assessment of road design alternatives described by discrete values, Multi‐Objective Optimization on the basis of the Ratio Analysis (MOORA) method was selected. This method focuses on a matrix of alternative responses on the objectives. A case study demonstrates the concept of multi‐objective optimization of road design alternatives and the best road design alternative is determined.

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26

Dimopoulos, C. "Multi-objective optimization of manufacturing cell design". International Journal of Production Research 44, n.º 22 (15 de noviembre de 2006): 4855–75. http://dx.doi.org/10.1080/00207540600620773.

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27

Obayashi, Shigeru, Shin-Kyu Jeong, Koji Shimoyama, Kazuhisa Chiba y Hiroyuki Morino. "Multi-Objective Design Exploration and its Applications". International Journal of Aeronautical and Space Sciences 11, n.º 4 (15 de diciembre de 2010): 247–65. http://dx.doi.org/10.5139/ijass.2010.11.4.247.

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28

ölvander, Johan. "Robustness considerations in multi-objective optimal design". Journal of Engineering Design 16, n.º 5 (octubre de 2005): 511–23. http://dx.doi.org/10.1080/09544820500287300.

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29

YOKONO, Yasuyuki. "Multi-objective Optimization for Power Unit Design". Proceedings of The Computational Mechanics Conference 2004.17 (2004): 249–50. http://dx.doi.org/10.1299/jsmecmd.2004.17.249.

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30

Alaimo, G., F. Auricchio, M. Conti y M. Zingales. "Multi-objective optimization of nitinol stent design". Medical Engineering & Physics 47 (septiembre de 2017): 13–24. http://dx.doi.org/10.1016/j.medengphy.2017.06.026.

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31

Papanikolaou, Apostolos, George Zaraphonitis, Evangelos Boulougouris, Uwe Langbecker, Sven Matho y Pierre Sames. "Multi-objective optimization of oil tanker design". Journal of Marine Science and Technology 15, n.º 4 (22 de julio de 2010): 359–73. http://dx.doi.org/10.1007/s00773-010-0097-7.

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32

ISHIKAWA, Haruo y Yoon-Eui NAHM. "3102 Set-Based Multi-Objective Design Optimization". Proceedings of Design & Systems Conference 2005.15 (2005): 425–28. http://dx.doi.org/10.1299/jsmedsd.2005.15.425.

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33

Lormeau, Claude, Mikołaj Rybiński y Jörg Stelling. "Multi-objective design of synthetic biological circuits". IFAC-PapersOnLine 50, n.º 1 (julio de 2017): 9871–76. http://dx.doi.org/10.1016/j.ifacol.2017.08.1601.

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34

Cobos Sanchez, Clemente, Mario Fernandez Pantoja, Michael Poole y Amelia Rubio Bretones. "Gradient-Coil Design: A Multi-Objective Problem". IEEE Transactions on Magnetics 48, n.º 6 (junio de 2012): 1967–75. http://dx.doi.org/10.1109/tmag.2011.2179943.

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35

Rachlin, J., C. Ding, C. Cantor y S. Kasif. "MuPlex: multi-objective multiplex PCR assay design". Nucleic Acids Research 33, Web Server (1 de julio de 2005): W544—W547. http://dx.doi.org/10.1093/nar/gki377.

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36

Sanchis, Javier, Miguel A. Martínez, Xavier Blasco y Gilberto Reynoso-Meza. "Modelling preferences in multi-objective engineering design". Engineering Applications of Artificial Intelligence 23, n.º 8 (diciembre de 2010): 1255–64. http://dx.doi.org/10.1016/j.engappai.2010.07.005.

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37

Nicolaou, Christos A. y Nathan Brown. "Multi-objective optimization methods in drug design". Drug Discovery Today: Technologies 10, n.º 3 (septiembre de 2013): e427-e435. http://dx.doi.org/10.1016/j.ddtec.2013.02.001.

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38

Goteng, G., A. Tiwari y R. Roy. "Grid services for multi-objective design optimisation". CIRP Journal of Manufacturing Science and Technology 3, n.º 4 (enero de 2010): 249–61. http://dx.doi.org/10.1016/j.cirpj.2011.01.005.

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39

Ochoa, Gabriela, Lee A. Christie, Alexander E. Brownlee y Andrew Hoyle. "Multi-objective evolutionary design of antibiotic treatments". Artificial Intelligence in Medicine 102 (enero de 2020): 101759. http://dx.doi.org/10.1016/j.artmed.2019.101759.

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40

Luukkonen, Sohvi, Helle W. van den Maagdenberg, Michael T. M. Emmerich y Gerard J. P. van Westen. "Artificial intelligence in multi-objective drug design". Current Opinion in Structural Biology 79 (abril de 2023): 102537. http://dx.doi.org/10.1016/j.sbi.2023.102537.

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41

Zhang, Bodong. "Multi-objective design and optimization of wings". Theoretical and Natural Science 9, n.º 1 (13 de noviembre de 2023): 243–47. http://dx.doi.org/10.54254/2753-8818/9/20240766.

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Nowadays, with the fast development of technology, aircrafts have been successful advanced. As we all know, wings are one of the most significant parts of the aircrafts, therefore, multi-objective design and optimization of wings has become a critical issue among many researchers. This paper mainly introduces several approaches to improve the wing in different conditions, including optimization design of civil airplanes, design and optimization of aircraft wing structure at low Reynolds numbers, Wing Design and Aerodynamic Characteristics of Biomimetic Flapping Wing Micro Air Vehicles and so on, the purpose of this article is to give a brief introduction to these relatively new research methods and make the public understand more about this field. The primary approaches of this article are literature analysis and review. This paper finds that algorithm and artificial intelligence are fundamental to improving wings, and all the design needs to reduce the amount of energy as much as possible, which is also the most important requirement.

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42

Miandoabchi, Elnaz, Farzaneh Daneshzand, W. Y. Szeto y Reza Zanjirani Farahani. "Multi-objective discrete urban road network design". Computers & Operations Research 40, n.º 10 (octubre de 2013): 2429–49. http://dx.doi.org/10.1016/j.cor.2013.03.016.

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43

Kuang, Shanlong y Adam Chrzanowski. "Multi-objective optimization design of geodetic networks". manuscripta geodaetica 17, n.º 4 (agosto de 1992): 233–44. http://dx.doi.org/10.1007/bf03655487.

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44

Liu, Haichao, Xiangjie Jin y Fagui Zhang. "Multi-objective robust design of vehicle structure based on multi-objective particle swarm optimization". Journal of Intelligent & Fuzzy Systems 39, n.º 6 (4 de diciembre de 2020): 9063–71. http://dx.doi.org/10.3233/jifs-189305.

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With the continuous spread of COVID-19 epidemic, the strict control of personnel makes it a problem to optimize the design of vehicle parameters after field measurement. The energy absorption characteristics and deformation mode of the front structure of the vehicle determine the acceleration or force response of the vehicle body during the impact, which plays an important role in occupant protection. The traditional multi-objective optimization method is to transform multi-objective problems into single objective optimization problems through weighted combination, objective planning, efficiency coefficient and other methods. This method requires a strong prior knowledge. The purpose of this paper is to combine the experimental design with the Multi-objective Particle Swarm Optimization (MPSO) method to achieve the optimization of the crash worthiness of automobile structure. This method can effectively overcome the defect of low precision caused by the conventional response surface method in the whole design space. In this paper, the multi-objective particle swarm optimization method is applied to the research of Crash worthiness optimization of automobile structure, which expands the application field of the multi-objective particle swarm optimization method, and also has a very big role in the optimization of other complex systems. It can be seen from the experiment that the speed of multi-objective particle swarm optimization is much faster than that of other methods. Only 100 iterations can get the relative better results. The case study on the front structure of a car shows that the method has a good result. It is of great significance to apply the method to the optimization design of the crash worthiness of the car structure to improve the crash safety of the car under the influence of COVID-19 epidemic.

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45

MIYASHITA, Tomoyuki y Hiroshi YAMAKAWA. "3402 A Study on Approximate Multi-Objective Optimization in Multi-Objective Design Considering Accuracy". Proceedings of Design & Systems Conference 2001.11 (2001): 331–34. http://dx.doi.org/10.1299/jsmedsd.2001.11.331.

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46

Gautier, Quentin, Alric Althoff, Christopher L. Crutchfield y Ryan Kastner. "Sherlock: A Multi-Objective Design Space Exploration Framework". ACM Transactions on Design Automation of Electronic Systems 27, n.º 4 (31 de julio de 2022): 1–20. http://dx.doi.org/10.1145/3511472.

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Design space exploration (DSE) provides intelligent methods to tune the large number of optimization parameters present in modern FPGA high-level synthesis tools. High-level synthesis parameter tuning is a time-consuming process due to lengthy hardware compilation times—synthesizing an FPGA design can take tens of hours. DSE helps find an optimal solution faster than brute-force methods without relying on designer intuition to achieve high-quality results. Sherlock is a DSE framework that can handle multiple conflicting optimization objectives and aggressively focuses on finding Pareto-optimal solutions. Sherlock integrates a model selection process to choose the regression model that helps reach the optimal solution faster. Sherlock designs a strategy based around the multi-armed bandit problem, opting to balance exploration and exploitation based on the learned and expected results. Sherlock can decrease the importance of models that do not provide correct estimates, reaching the optimal design faster. Sherlock is capable of tailoring its choice of regression models to the problem at hand, leading to a model that best reflects the application design space. We have tested the framework on a large dataset of FPGA design problems and found that Sherlock converges toward the set of optimal designs faster than similar frameworks.

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47

Zhang, Jiangnan y Mehrdad Zangeneh. "Multi-Point, Multi-Objective Optimisation of Centrifugal Fans by 3D Inverse Design Method". International Journal of Turbomachinery, Propulsion and Power 8, n.º 1 (2 de marzo de 2023): 8. http://dx.doi.org/10.3390/ijtpp8010008.

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In this paper, we present the design and optimization of a centrifugal fan with requirements of maximizing the total-to-static pressure rise and total-to-static efficiency at two operating points and the maximum torque provided by the motor power using a 3D inverse design method, a DOE (design of experiment) study, an RSM (response surface model) and a MOGA (multi-objective genetic algorithm). The fan geometry is parametrized using 13 design parameters, and 120 different designs are generated. The fan performances of all the designs at two operating conditions are evaluated through steady-state CFD simulations. The resulting design matrix is used to create an RSM based on the Kriging method and MOGA is used to search the design space using the RSM and find the optimal design.

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48

Cameron, L., J. Early, R. McRoberts y M. Price. "Constrained multi-objective aerofoil design using a multi-level optimisation strategy". Aeronautical Journal 119, n.º 1217 (julio de 2015): 833–54. http://dx.doi.org/10.1017/s0001924000010940.

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AbstractA novel approach for the multi-objective design optimisation of aerofoil profiles is presented. The proposed method aims to exploit the relative strengths of global and local optimisation algorithms, whilst using surrogate models to limit the number of computationally expensive CFD simulations required. The local search stage utilises a re-parameterisation scheme that increases the flexibility of the geometry description by iteratively increasing the number of design variables, enabling superior designs to be generated with minimal user intervention. Capability of the algorithm is demonstrated via the conceptual design of aerofoil sections for use on a lightweight laminar flow business jet. The design case is formulated to account for take-off performance while reducing sensitivity to leading edge contamination. The algorithm successfully manipulates boundary layer transition location to provide a potential set of aerofoils that represent the trade-offs between drag at cruise and climb conditions in the presence of a challenging constraint set. Variations in the underlying flow physics between Pareto-optimal aerofoils are examined to aid understanding of the mechanisms that drive the trade-offs in objective functions.

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Bortolini, Marco, Maurizio Faccio, Mauro Gamberi y Francesco Pilati. "Multi-objective design of multi-modal fresh food distribution networks". International Journal of Logistics Systems and Management 24, n.º 2 (2016): 155. http://dx.doi.org/10.1504/ijlsm.2016.076470.

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Priyadarshi, Pankaj, Mofeez Alam y Kamal Saroha. "Multi-disciplinary multi-objective design optimization of sounding rocket fins". International Journal of Advances in Engineering Sciences and Applied Mathematics 6, n.º 3-4 (diciembre de 2014): 166–82. http://dx.doi.org/10.1007/s12572-015-0121-6.

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