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Articoli di riviste sul tema "Multi-objective Design"

Autore: Grafiati

Pubblicato: 14 dicembre 2024

Ultima modifica: 31 luglio 2025

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1

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

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2

Freier, Lars, and Eric von Lieres. "Robust multi-objective process design." New Biotechnology 33 (July 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, and Bei Yu. "Correlated Multi-objective Multi-fidelity Optimization for HLS Directives Design." ACM Transactions on Design Automation of Electronic Systems 27, no. 4 (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 consecutiv

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4

YAMASHINA, Hajime, Susumu OKUMURA, and Yoshimasa KONDO. "Parameter Design with Multi Objective Characteristics." Journal of the Japan Society for Precision Engineering 58, no. 3 (1992): 516–20. http://dx.doi.org/10.2493/jjspe.58.516.

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5

Kor, Jean, Xiang Chen, Zhizhong Sun, and Henry Hu. "Casting Design Through Multi-Objective Optimization." IFAC Proceedings Volumes 44, no. 1 (2011): 11642–47. http://dx.doi.org/10.3182/20110828-6-it-1002.01726.

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6

Joseph, Shaine, Hyung W. Kang, and Uday K. Chakraborty. "Lens design as multi-objective optimisation." International Journal of Automation and Control 5, no. 3 (2011): 189. http://dx.doi.org/10.1504/ijaac.2011.042851.

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7

Sanchis, J., M. Martinez, and X. Blasco. "Multi-objective engineering design using preferences." Engineering Optimization 40, no. 3 (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, and Franco Giuseppe Dedini. "Vehicle drivetrain design multi-objective optimization." Mechanism and Machine Theory 156 (February 2021): 104123. http://dx.doi.org/10.1016/j.mechmachtheory.2020.104123.

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9

Pelinescu, Diana M., and Michael Yu Wang. "Multi-objective optimal fixture layout design." Robotics and Computer-Integrated Manufacturing 18, no. 5-6 (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, and Bu Sung Lee. "Inverse multi-objective robust evolutionary design." Genetic Programming and Evolvable Machines 7, no. 4 (2006): 383–404. http://dx.doi.org/10.1007/s10710-006-9013-7.

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11

Kiranoudis, C. T., Z. B. Maroulis, and D. Marinos-Kouris. "PRODUCT QUALITY MULTI-OBJECTIVE DRYER DESIGN." Drying Technology 17, no. 10 (1999): 2251–70. http://dx.doi.org/10.1080/07373939908917682.

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12

C.Kavitha, C. Kavitha, and C. Vijayalakshmi C. Vijayalakshmi. "Design and Implementation of Fuzzy Multi Objective Optimization Model for Production Planning." Indian Journal of Applied Research 3, no. 12 (2011): 372–75. http://dx.doi.org/10.15373/2249555x/dec2013/113.

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13

Deb, Kalyanmoy, and Sachin Jain. "Multi-Speed Gearbox Design Using Multi-Objective Evolutionary Algorithms." Journal of Mechanical Design 125, no. 3 (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 ca

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14

Mohamed, Nejlaoui, Najlawi Bilel, and Ali Sulaiman Alsagri. "A multi-objective methodology for multi-criteria engineering design." Applied Soft Computing 91 (June 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, and Dimitri N. Mavris. "Evolutionary multi-objective multi-architecture design space exploration methodology." Optimization and Engineering 19, no. 2 (2018): 359–81. http://dx.doi.org/10.1007/s11081-018-9373-x.

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16

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

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17

Patel, Prashant, Paras Shah, and Zissimos P. Mourelatos. "Piston Design Using Multi-Objective Reliability-Based Design Optimization." SAE International Journal of Materials and Manufacturing 3, no. 1 (2010): 493–511. http://dx.doi.org/10.4271/2010-01-0907.

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18

Adámek, Mikuláš, and Rastislav Toman. "RANGE EXTENDER ICE MULTI-PARAMETRIC MULTI-OBJECTIVE OPTIMIZATION." MECCA Journal of Middle European Construction and Design of Cars 18, no. 1 (2021): 10. http://dx.doi.org/10.14311/mecdc.2021.01.02.

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Abstract (sommario):

    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 s

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19

Martz, M., and W. L. Neu. "Multi-Objective Optimization of an Autonomous Underwater Vehicle." Marine Technology Society Journal 43, no. 2 (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 multidim

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20

Jain, Chitra, and Gaurav Shrivastava. "Design a Classifier by Using Multi Objective Simultaneous Learning Framework with KFCM Algorithm." International Journal of Scientific Engineering and Research 1, no. 4 (2013): 10–14. https://doi.org/10.70729/2131211.

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21

Zhang, Jiangnan, and Mehrdad Zangeneh. "Multi-Point, Multi-Objective Optimisation of Centrifugal Fans by 3D Inverse Design Method." International Journal of Turbomachinery, Propulsion and Power 8, no. 1 (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-stat

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22

Brauers, Willem Karel M., Edmundas Kazimieras Zavadskas, Friedel Peldschus, and Zenonas Turskis. "MULTI‐OBJECTIVE DECISION‐MAKING FOR ROAD DESIGN." TRANSPORT 23, no. 3 (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

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23

Sutagundar, M., B. G. Sheeparamatti, and D. S. Jangamshetti. "Multi-objective Design Optimization of Microdisk Resonator." Nanoscience & Nanotechnology-Asia 10, no. 4 (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 o

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24

Devi, R. Vasundhara, S. Siva Sathya, Nilabh Kumar, and Mohane Selvaraj Coumar. "Multi-objective Monkey Algorithm for Drug Design." International Journal of Intelligent Systems and Applications 11, no. 3 (2019): 31–41. http://dx.doi.org/10.5815/ijisa.2019.03.04.

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25

Barone, Salvatore, Marcello Traiola, Mario Barbareschi, and 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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26

Pereira, Filipe, Patrick M. Reed,, and Daniel Selva. "Multi-Objective Design of a Lunar GNSS." NAVIGATION: Journal of the Institute of Navigation 69, no. 1 (2022): navi.504. http://dx.doi.org/10.33012/navi.504.

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27

Zheng, Shan Suo, Zhi Qiang Li, Yi Hu, Qing Lin Tao, and Wei Wang. "Multi-Objective Optimization Design of Hybrid Structure." Advanced Materials Research 243-249 (May 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 pro

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28

Dimopoulos, C. "Multi-objective optimization of manufacturing cell design." International Journal of Production Research 44, no. 22 (2006): 4855–75. http://dx.doi.org/10.1080/00207540600620773.

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29

Obayashi, Shigeru, Shin-Kyu Jeong, Koji Shimoyama, Kazuhisa Chiba, and Hiroyuki Morino. "Multi-Objective Design Exploration and its Applications." International Journal of Aeronautical and Space Sciences 11, no. 4 (2010): 247–65. http://dx.doi.org/10.5139/ijass.2010.11.4.247.

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30

Kuang, Shanlong, and Adam Chrzanowski. "Multi-objective optimization design of geodetic networks." manuscripta geodaetica 17, no. 4 (1992): 233–44. http://dx.doi.org/10.1007/bf03655487.

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31

ölvander, Johan. "Robustness considerations in multi-objective optimal design." Journal of Engineering Design 16, no. 5 (2005): 511–23. http://dx.doi.org/10.1080/09544820500287300.

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32

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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33

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

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34

Papanikolaou, Apostolos, George Zaraphonitis, Evangelos Boulougouris, Uwe Langbecker, Sven Matho, and Pierre Sames. "Multi-objective optimization of oil tanker design." Journal of Marine Science and Technology 15, no. 4 (2010): 359–73. http://dx.doi.org/10.1007/s00773-010-0097-7.

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35

ISHIKAWA, Haruo, and 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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36

Lormeau, Claude, Mikołaj Rybiński, and Jörg Stelling. "Multi-objective design of synthetic biological circuits." IFAC-PapersOnLine 50, no. 1 (2017): 9871–76. http://dx.doi.org/10.1016/j.ifacol.2017.08.1601.

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37

Cobos Sanchez, Clemente, Mario Fernandez Pantoja, Michael Poole, and Amelia Rubio Bretones. "Gradient-Coil Design: A Multi-Objective Problem." IEEE Transactions on Magnetics 48, no. 6 (2012): 1967–75. http://dx.doi.org/10.1109/tmag.2011.2179943.

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38

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

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39

Sanchis, Javier, Miguel A. Martínez, Xavier Blasco, and Gilberto Reynoso-Meza. "Modelling preferences in multi-objective engineering design." Engineering Applications of Artificial Intelligence 23, no. 8 (2010): 1255–64. http://dx.doi.org/10.1016/j.engappai.2010.07.005.

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40

Nicolaou, Christos A., and Nathan Brown. "Multi-objective optimization methods in drug design." Drug Discovery Today: Technologies 10, no. 3 (2013): e427-e435. http://dx.doi.org/10.1016/j.ddtec.2013.02.001.

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41

Goteng, G., A. Tiwari, and R. Roy. "Grid services for multi-objective design optimisation." CIRP Journal of Manufacturing Science and Technology 3, no. 4 (2010): 249–61. http://dx.doi.org/10.1016/j.cirpj.2011.01.005.

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42

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

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43

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

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44

Zhang, Bodong. "Multi-objective design and optimization of wings." Theoretical and Natural Science 9, no. 1 (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 o

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45

Miandoabchi, Elnaz, Farzaneh Daneshzand, W. Y. Szeto, and Reza Zanjirani Farahani. "Multi-objective discrete urban road network design." Computers & Operations Research 40, no. 10 (2013): 2429–49. http://dx.doi.org/10.1016/j.cor.2013.03.016.

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46

Hadj Mohamed, Baghdad, M. Belkadi, M. Aounallah, and L. Adjlout. "Multi-objective design optimization of bulk carriers." Journal of Naval Architecture and Marine Engineering 20, no. 2 (2023): 77–88. https://doi.org/10.3329/jname.v20i2.66373.

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This paper deals with the numerical optimization of bulk carrier design. The main objective is to minimize the construction cost, the transportation cost and to maximize the annual transported cargo. Four multi-objective optimization methods are used: the weighted aggregation method based on multi-attribute decision making (MADM), the control function, the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the hybrid method. The obtained results show that the MADM, NSGA-II and hybrid methods give more or less similar results for the three objective functions compared to the control function

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47

Liu, Haichao, Xiangjie Jin, and Fagui Zhang. "Multi-objective robust design of vehicle structure based on multi-objective particle swarm optimization." Journal of Intelligent & Fuzzy Systems 39, no. 6 (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, efficien

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48

MIYASHITA, Tomoyuki, and 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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Gautier, Quentin, Alric Althoff, Christopher L. Crutchfield, and Ryan Kastner. "Sherlock: A Multi-Objective Design Space Exploration Framework." ACM Transactions on Design Automation of Electronic Systems 27, no. 4 (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

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Cameron, L., J. Early, R. McRoberts, and M. Price. "Constrained multi-objective aerofoil design using a multi-level optimisation strategy." Aeronautical Journal 119, no. 1217 (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 demo

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