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Статті в журналах з теми "Trade-off optimization"

Автор: Grafiati

Опубліковано: 10 березня 2023

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1

Audet, Charles, J. E. Dennis, and Sébastien Le Digabel. "Trade-off studies in blackbox optimization." Optimization Methods and Software 27, no. 4-5 (October 2012): 613–24. http://dx.doi.org/10.1080/10556788.2011.571687.

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2

Nakayama, Hirotaka. "Trade-off analysis using parametric optimization techniques." European Journal of Operational Research 60, no. 1 (July 1992): 87–98. http://dx.doi.org/10.1016/0377-2217(92)90336-8.

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3

Phillips, Christopher W. G., Dominic A. Hudson, Stephen R. Turnock, and Alexander I. J. Forrester. "Thrust-versus-endurance trade-off optimization in swimming." Engineering Optimization 52, no. 6 (July 17, 2019): 1068–81. http://dx.doi.org/10.1080/0305215x.2019.1636980.

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4

Giannakis, Mihalis, and Marwa Bouka. "A Trade-off Optimization Model for Container Fulfilment." Supply Chain Forum: An International Journal 16, no. 1 (January 2015): 46–63. http://dx.doi.org/10.1080/16258312.2015.11517366.

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5

Lee, G. M., and H. Nakayama. "Generalized trade-off directions in multiobjective optimization problems." Applied Mathematics Letters 10, no. 1 (January 1997): 119–22. http://dx.doi.org/10.1016/s0893-9659(96)00122-x.

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6

Voronin, Albert N. "Trade-Off and Consensus in Vector-Valued Optimization Theory." Journal of Automation and Information Sciences 33, no. 9 (2001): 10. http://dx.doi.org/10.1615/jautomatinfscien.v33.i9.20.

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7

Alvarez-Hamelin, J. I., and N. Schabanel. "An internet graph model based on trade-off optimization." European Physical Journal B - Condensed Matter 38, no. 2 (March 1, 2004): 231–37. http://dx.doi.org/10.1140/epjb/e2004-00116-y.

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8

Kuk, Hun, Tetsuzo Tanino, and Masahiro Tanaka. "Trade-off analysis for vector optimization problems via scalarization." Journal of Information and Optimization Sciences 18, no. 1 (January 1997): 75–87. http://dx.doi.org/10.1080/02522667.1997.10699313.

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9

Lupo, Toni. "Non-dominated “trade-off” solutions in television scheduling optimization." International Transactions in Operational Research 22, no. 3 (December 4, 2014): 563–84. http://dx.doi.org/10.1111/itor.12137.

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10

Miettinen, Kaisa, and Marko M. Mäkelä. "On generalized trade-off directions in nonconvex multiobjective optimization." Mathematical Programming 92, no. 1 (March 1, 2002): 141–51. http://dx.doi.org/10.1007/s101070100282.

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11

Eskelinen, Petri, and Kaisa Miettinen. "Trade-off analysis approach for interactive nonlinear multiobjective optimization." OR Spectrum 34, no. 4 (July 7, 2011): 803–16. http://dx.doi.org/10.1007/s00291-011-0266-z.

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12

Chan, Timothy C. Y., and Taewoo Lee. "Trade-off preservation in inverse multi-objective convex optimization." European Journal of Operational Research 270, no. 1 (October 2018): 25–39. http://dx.doi.org/10.1016/j.ejor.2018.02.045.

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13

Zhou, Gui-Jiang, Wai-Yeung Wong, Zhenyang Lin, and Cheng Ye. "White Metallopolyynes for Optical Limiting/Transparency Trade-off Optimization." Angewandte Chemie 118, no. 37 (September 18, 2006): 6335–39. http://dx.doi.org/10.1002/ange.200601651.

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14

Toyoda, Masahiro, and Nozomu Kogiso. "Robust multiobjective optimization method using satisficing trade-off method." Journal of Mechanical Science and Technology 29, no. 4 (April 2015): 1361–67. http://dx.doi.org/10.1007/s12206-015-0305-9.

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15

Zhou, Gui-Jiang, Wai-Yeung Wong, Zhenyang Lin, and Cheng Ye. "White Metallopolyynes for Optical Limiting/Transparency Trade-off Optimization." Angewandte Chemie International Edition 45, no. 37 (September 18, 2006): 6189–93. http://dx.doi.org/10.1002/anie.200601651.

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16

Strelin, Marina M., Federico Sazatornil, Santiago Benitez-Vieyra, and Mariano Ordano. "Bee, hummingbird, or mixed-pollinated Salvia species mirror pathways to pollination optimization: a morphometric analysis based on the Pareto front concept." Botany 95, no. 2 (February 2017): 139–46. http://dx.doi.org/10.1139/cjb-2016-0145.

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Анотація:

Optimization of flower phenotypes to ensure pollination by agents differing in their match with fertile flower structures can involve fitness trade-offs if the aspects of the phenotype that enhance the fitness contribution of one pollinator are detrimental for pollination by the other agents. If these trade-offs are substantial, flower optimization for specialized pollination is expected. However, optimization for generalized pollination may also take place in trade-off scenarios, as long as the joint contribution of two or more types of pollinators to global pollination fitness is greater than each individual contribution. We used an observational approach to evaluate the role of pollination fitness trade-offs in flower trait optimization, a matter seldom addressed because of the difficulties in conducting experiments. A pattern-searching tool based on the Pareto front concept, borrowed from the fields of economics and engineering, was used to test for fitness trade-off patterns in the flower shape of four Salvia (Lamiaceae) species. Two are pollinated exclusively either by bees or by hummingbirds; the remaining species have mixed-pollination systems, with varying contributions of bee and hummingbird pollination. The patterning of flower shape in this study suggests a bee–hummingbird pollination trade-off in Salvia, and the optimization of generalized flower shapes.

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17

Sooktip, Tipwimol, and Naruemon Wattanapongsakorn. "A Decision Making Approach for Multi-Objective Optimization Considering A Trade-off Method." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 11, no. 2 (December 9, 2017): 178–89. http://dx.doi.org/10.37936/ecti-cit.2017112.98907.

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Анотація:

In multi-objective optimization problem, a set of optimal solutions is obtained from an optimization algorithm. There are many trade-off optimal solutions. However, in practice, a decision maker or user only needs one or very few solutions for implementation. Moreover, these solutions are difficult to determine from a set of optimal solutions of complex system. Therefore, a trade-off method for multi-objective optimization is proposed for identifying the preferred solutions according to the decision maker’s preference. The preference is expressed by using the trade-off between any two objectives where the decision maker is willing to worsen in one objective value in order to gain improvement in the other objective value. The trade-off method is demonstrated by using well-known two-objective and three-objective benchmark problems. Furthermore, a system design problem with component allocation is also considered to illustrate the applicability of the proposed method. The results show that the trade-off method can be applied for solving practical problems to identify the final solution(s) and easy to use even when the decision maker lacks some knowledge or not an expert in the problem solving. The decision maker only gives his/her preference information. Then, the corresponding optimal solutions will be obtained, accordingly.

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18

Chang, Peter, and Leonhard Bernold. "Multi-objective process optimization for construction." Canadian Journal of Civil Engineering 19, no. 1 (February 1, 1992): 129–36. http://dx.doi.org/10.1139/l92-013.

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Анотація:

Much of the existing work in construction analysis focuses on determining the construction cost based on an allowable project duration. In this type of construction analysis, two important questions are not considered. First, is the construction cost minimized for the allowable process duration? Second, would a small change in the process duration result in a significant change in the cost of the project? An optimization method is proposed to answer these questions. The approach consists of an integration of computer simulation with goal programming. The optimization method proposed allows one to assign priorities to the various design objectives such as cost and duration, which avoids the need to use subjective weights. Furthermore, since the approach simulates the construction process by computer, it can be applied to any repetitive construction process. In addition to the capability of the model to provide a single optimal solution to a construction optimization problem, it can be used to determine the trade-off between conflicting objectives. Examples are presented to illustrate the formulation process and the capabilities as a decision-making tool for construction. It is shown that the trade-off curves produced by the proposed model can provide useful information on the cost implications of various design variables, as well as on the trade-offs that exist among them. Key words: construction optimization, multi-objective optimization, goal programming, trade-off analysis, simulation.

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19

de Kock, D. J., and J. A. Visser. "Trade-off Design of Extruded Heat Sinks Using Mathematical Optimization." Journal of Electronic Packaging 126, no. 3 (September 1, 2004): 333–41. http://dx.doi.org/10.1115/1.1772414.

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Heat sink designers have to balance a number of conflicting parameters to maximize the performance of heat sinks. This multi-parameter problem lends itself naturally to mathematical optimization techniques. The paper illustrates how mathematical optimization techniques combined with a semi-empirical thermal simulation program can be used to construct a trade-off curve (Pareto-optimal set) between the heat sink mass and thermal resistance for a given heat sink configuration. This trade-off curve can be used by the engineer to decide on the optimal heat sink design that is the best compromise between heat sink mass and thermal resistance for the specific application under consideration.

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20

Johansson, Cristina, Johan Ölvander, and Micael Derelöv. "Multi-objective optimization for safety and reliability trade-off: Optimization and results processing." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 6 (February 26, 2018): 661–76. http://dx.doi.org/10.1177/1748006x18757075.

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In early design phases, it is vital to be able to screen the design space for a set of promising design alternatives for further study. This article presents a method able to balance several objectives of different mathematical natures, with high impact on the design choices. The method (MOSART) handles multi-objective optimization for safety and reliability trade-offs. The article focuses on optimization problem approach and processing of results as a base for decision-making. The output of the optimization step is the selection of specific system elements obtaining the best balance between the targets. However, what is a good base for decision can easily transform into too much information and overloading of the decision-maker. To solve this potential issue, from a set of Pareto optimal solutions, a smaller sub-set of selected solutions are visualized and filtered out using preference levels of the objectives, yielding a solid base for decision-making and valuable information on potential solutions. Trends were observed regarding each system element and discussed while processing the results of the analysis, supporting the decision of one final best solution.

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21

Toğan, Vedat, and M. Azim Eirgash. "Time-Cost Trade-off Optimization of Construction Projects using Teaching Learning Based Optimization." KSCE Journal of Civil Engineering 23, no. 1 (December 3, 2018): 10–20. http://dx.doi.org/10.1007/s12205-018-1670-6.

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22

Zhang, Qian, Jinjin Ding, Weixiang Shen, Jinhui Ma, and Guoli Li. "Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch." Mathematical Problems in Engineering 2020 (March 25, 2020): 1–13. http://dx.doi.org/10.1155/2020/5695917.

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Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.

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23

Gambeta, Vaughn, and Roy Kwon. "Risk Return Trade-Off in Relaxed Risk Parity Portfolio Optimization." Journal of Risk and Financial Management 13, no. 10 (October 4, 2020): 237. http://dx.doi.org/10.3390/jrfm13100237.

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This paper formulates a relaxed risk parity optimization model to control the balance of risk parity violation against the total portfolio performance. Risk parity has been criticized as being overly conservative and it is improved by re-introducing the asset expected returns into the model and permitting the portfolio to violate the risk parity condition. This paper proposes the incorporation of an explicit target return goal with an intuitive target return approach into a second-order-cone model of a risk parity optimization. When the target return is greater than risk parity return, a violation to risk parity allocations occurs that is controlled using a computational construct to obtain near-risk parity portfolios to retain as much risk parity-like traits as possible. This model is used to demonstrate empirically that higher returns can be achieved than risk parity without the risk contributions deviating dramatically from the risk parity allocations. Furthermore, this study reveals that the relaxed risk parity model exhibits advantageous traits of robustness to expected returns, which should not deter the use of expected returns in risk parity model.

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24

Kaur, Kirandeep, Varinder Singh, Jatin Ghai, Satyajit Jena, and Özgür E. Müstecaplıoğlu. "Unified trade-off optimization of a three-level quantum refrigerator." Physica A: Statistical Mechanics and its Applications 576 (August 2021): 125892. http://dx.doi.org/10.1016/j.physa.2021.125892.

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25

Giakoumakis, Vassilis, Daniel Krob, Leo Liberti, and Fabio Roda. "Technological architecture evolutions of information systems: Trade-off and optimization." Concurrent Engineering 20, no. 2 (May 24, 2012): 127–47. http://dx.doi.org/10.1177/1063293x12447715.

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26

Yang, J. B., C. Chen, and Z. J. Zhang. "The interactive step trade-off method (ISTM) for multiobjective optimization." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 3 (1990): 688–95. http://dx.doi.org/10.1109/21.57283.

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27

Geem, Zong Woo. "Multiobjective Optimization of Time-Cost Trade-Off Using Harmony Search." Journal of Construction Engineering and Management 136, no. 6 (June 2010): 711–16. http://dx.doi.org/10.1061/(asce)co.1943-7862.0000167.

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28

FURUKAWA, Ryo, Tadashi KUROIWA, Koichi OHTOMI, and Takuya KANZAWA. "Trade-off Design of Space Equipment using Multi-Objective Optimization." Proceedings of the JSME annual meeting 2004.7 (2004): 215–16. http://dx.doi.org/10.1299/jsmemecjo.2004.7.0_215.

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29

Kitayama, Satoshi, Koetsu Yamazaki, Masao Arakawa, and Hiroshi Yamakawa. "2311 Trade-Off Analysis on the Multi-Objective Design Optimization." Proceedings of Design & Systems Conference 2008.18 (2008): 335–37. http://dx.doi.org/10.1299/jsmedsd.2008.18.335.

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30

KOGISO, Nozomu, Ryo KODAMA, and Masahiro TOYODA. "Reliability-based multiobjective optimization using the satisficing trade-off method." Mechanical Engineering Journal 1, no. 6 (2014): DSM0063. http://dx.doi.org/10.1299/mej.2014dsm0063.

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31

Kitayama, Satoshi, and Koetsu Yamazaki. "Compromise point incorporating trade-off ratio in multi-objective optimization." Applied Soft Computing 12, no. 8 (August 2012): 1959–64. http://dx.doi.org/10.1016/j.asoc.2012.03.024.

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32

Doğankaya, Emre, Müge Kahya, and Hakkı Özgür Ünver. "Abrasive water jet machining of UHMWPE and trade-off optimization." Materials and Manufacturing Processes 35, no. 12 (June 10, 2020): 1339–51. http://dx.doi.org/10.1080/10426914.2020.1772486.

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33

Liu, Jinming, and Fred Rahbar. "Project Time-Cost Trade-Off Optimization by Maximal Flow Theory." Journal of Construction Engineering and Management 130, no. 4 (August 2004): 607–9. http://dx.doi.org/10.1061/(asce)0733-9364(2004)130:4(607).

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34

Miettinen, Kaisa, and Francisco Ruiz. "NAUTILUS framework: towards trade-off-free interaction in multiobjective optimization." Journal of Business Economics 86, no. 1-2 (January 2016): 5–21. http://dx.doi.org/10.1007/s11573-015-0786-0.

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35

Mokhtari, Hadi, Abdollah Aghaie, Javad Rahimi, and Ashkan Mozdgir. "Project time–cost trade-off scheduling: a hybrid optimization approach." International Journal of Advanced Manufacturing Technology 50, no. 5-8 (February 18, 2010): 811–22. http://dx.doi.org/10.1007/s00170-010-2543-4.

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36

Abbas, Adel T., Karim Hamza, Mohamed F. Aly, and Essam A. Al-Bahkali. "Multiobjective Optimization of Turning Cutting Parameters for J-Steel Material." Advances in Materials Science and Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/6429160.

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Анотація:

This paper presents a multiobjective optimization study of cutting parameters in turning operation for a heat-treated alloy steel material (J-Steel) with Vickers hardness in the range of HV 365–395 using uncoated, unlubricated Tungsten-Carbide tools. The primary aim is to identify proper settings of the cutting parameters (cutting speed, feed rate, and depth of cut) that lead to reasonable compromises between good surface quality and high material removal rate. Thorough exploration of the range of cutting parameters was conducted via a five-level full-factorial experimental matrix of samples and the Pareto trade-off frontier is identified. The trade-off among the objectives was observed to have a “knee” shape, in which certain settings for the cutting parameters can achieve both good surface quality and high material removal rate within certain limits. However, improving one of the objectives beyond these limits can only happen at the expense of a large compromise in the other objective. An alternative approach for identifying the trade-off frontier was also tested via multiobjective implementation of the Efficient Global Optimization (m-EGO) algorithm. The m-EGO algorithm was successful in identifying two points within the good range of the trade-off frontier with 36% fewer experimental samples.

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37

Won, Joong-Ho, and Seung-Jean Kim. "Robust trade-off portfolio selection." Optimization and Engineering 21, no. 3 (January 28, 2020): 867–904. http://dx.doi.org/10.1007/s11081-020-09485-z.

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38

Golpîra, Hêriş. "A Scenario Based Stochastic Time-Cost-Quality Trade-Off model for Project Scheduling Problem." International Journal of Management Science and Business Administration 2, no. 5 (2014): 7–12. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.25.1001.

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Анотація:

This paper formulates a new time–cost trade-off problem under some uncertainties for a multi-phase project. To do this, a new approach is proposed based on goal programming in compliance with scenario-based stochastic optimization formulation. To the best of our knowledge, this problem has not been extensively treated in the literature. Computational results show the applicability and usefulness of the proposed method.

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39

Park, U.-Yeol, and Sung-Hoon An. "A Study on Optimization Model of Time-Cost Trade-off Analysisusing Particle Swarm Optimization." Journal of the Korean Institute of Building Construction 8, no. 6 (December 20, 2008): 91–98. http://dx.doi.org/10.5345/jkic.2008.8.6.091.

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40

Hegazy, Tarek. "Optimization of construction time-cost trade-off analysis using genetic algorithms." Canadian Journal of Civil Engineering 26, no. 6 (December 1, 1999): 685–97. http://dx.doi.org/10.1139/l99-031.

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Анотація:

In the management of a construction project, the project duration can often be compressed by accelerating some of its activities at an additional expense. This is the so-called time-cost trade-off (TCT) problem, which has been studied extensively in the project management literature. TCT decisions, however, are complex and require planners to select appropriate resources for each project task, including crew size, equipment, methods, and technology. As combinatorial optimization problems, finding optimal decisions is difficult and time consuming considering the number of possible permutations involved. In this paper, a practical model for TCT optimization is developed using the principle of genetic algorithms (GAs). With its robust optimization search, the GAs model minimizes the total project cost as an objective function and accounts for project-specific constraints on time and cost. To maximize its benefits, the model has been implemented as a VBA macro program. This automates TCT analysis and combines it with standard resource-management procedures. Details of the proposed TCT model are described and several experiments conducted to demonstrate its benefits. The developments made in this paper provide guidelines for designing and implementing practical GA applications in the civil engineering domain.Key words: computer application, time-cost trade-off, construction management, genetic algorithms, and optimization.

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41

Alzahrani, Saad M., and Naruemon Wattanapongsakorn. "Comparative Study of Knee-Based Algorithms for Many-Objective Optimization Problems." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 12, no. 1 (April 10, 2018): 7–16. http://dx.doi.org/10.37936/ecti-cit.2018121.98549.

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Анотація:

Nowadays, most real-world optimization problems consist of many and often conflicting objectives to be optimized simultaneously. Although, many current Multi-Objective optimization algorithms can efficiently solve problems with 3 or less objectives, their performance deteriorates proportionally with the increasing of the objectives number. Furthermore, in many situations the decision maker (DM) is not interested in all trade-off solutions obtained but rather interested in a single optimum solution or a small set of those trade-offs. Therefore, determining an optimum solution or a small set of trade-off solutions is a difficult task. However, an interesting method for finding such solutions is identifying solutions in the Knee region. Solutions in the Knee region can be considered the best obtained solution in the obtained trade-off set especially if there is no preference or equally important objectives. In this paper, a pruning strategy was used to find solutions in the Knee region of Pareto optimal fronts for some benchmark problems obtained by NSGA-II, MOEA/D-DE and a promising new Multi-Objective optimization algorithm NSGA-III. Lastly, those knee solutions found were compared and evaluated using a generational distance performance metric, computation time and a statistical one-way ANOVA test.

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42

Shen, Wei, Zhenhuan Yang, Yiming Ying, and Xiaoming Yuan. "Stability and optimization error of stochastic gradient descent for pairwise learning." Analysis and Applications 18, no. 05 (August 22, 2019): 887–927. http://dx.doi.org/10.1142/s0219530519400062.

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Анотація:

In this paper, we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning setting. Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances among which notable examples are bipartite ranking, metric learning, area under ROC curve (AUC) maximization and minimum error entropy (MEE) principle. Our contribution is twofolded. Firstly, we establish the stability results for SGD for pairwise learning in the convex, strongly convex and non-convex settings, from which generalization errors can be naturally derived. Secondly, we establish the trade-off between stability and optimization error of SGD algorithms for pairwise learning. This is achieved by lower-bounding the sum of stability and optimization error by the minimax statistical error over a prescribed class of pairwise loss functions. From this fundamental trade-off, we obtain lower bounds for the optimization error of SGD algorithms and the excess expected risk over a class of pairwise losses. In addition, we illustrate our stability results by giving some specific examples of AUC maximization, metric learning and MEE.

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