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Journal articles on the topic 'Optimum global'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Pan, Xing-Chen, and Quan Zheng. "Global optimum shape design." Computers & Mathematics with Applications 37, no. 4-5 (1999): 151–62. http://dx.doi.org/10.1016/s0898-1221(99)00066-8.

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2

Yu, Bin, and Baozong Yuan. "A global optimum clustering algorithm." Engineering Applications of Artificial Intelligence 8, no. 2 (1995): 223–27. http://dx.doi.org/10.1016/0952-1976(94)00067-w.

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3

Yu, Yang, Shangce Gao, Yirui Wang, and Yuki Todo. "Global optimum-based search differential evolution." IEEE/CAA Journal of Automatica Sinica 6, no. 2 (2019): 379–94. http://dx.doi.org/10.1109/jas.2019.1911378.

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4

Liu, Yang, Liang Chen, Zhen Li, and Jianbin Du. "On the global optimum for heat conduction." International Journal of Heat and Mass Transfer 198 (December 2022): 123381. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2022.123381.

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5

Li, Shi, Jinhui Xu, and Minwei Ye. "Approximating Global Optimum for Probabilistic Truth Discovery." Algorithmica 82, no. 10 (2020): 3091–116. http://dx.doi.org/10.1007/s00453-020-00715-5.

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6

Arpino, F., P. Di Barba, P. Navarra, and A. Savini. "3D global optimum of an electromechanical device." IEEE Transactions on Magnetics 30, no. 5 (1994): 3427–30. http://dx.doi.org/10.1109/20.312675.

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Liu, Xingyu, Tao Wu, Wuxing Lai, Hu Yuan, Qilong Kou, and Jingping Yu. "Cuckoo Algorithm Based on Global Feedback." Computational Intelligence and Neuroscience 2023 (January 7, 2023): 1–20. http://dx.doi.org/10.1155/2023/2040866.

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This article proposes a cuckoo algorithm (GFCS) based on the global feedback strategy and innovatively introduces a “re-fly” mechanism. In GFCS, the process of the algorithm is adjusted and controlled by a dynamic global variable, and the dynamic global parameter also serves as an indicator of whether the algorithm has fallen into a local optimum. According to the change of the global optimum value of the algorithm in each round, the dynamic global variable value is adjusted to optimize the algorithm. In addition, we set new formulas for the other main parameters, which are also adjusted by th

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8

Štrupl, Miroslav, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, and Jürgen Schmidhuber. "Reward-Weighted Regression Converges to a Global Optimum." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8361–69. http://dx.doi.org/10.1609/aaai.v36i8.20811.

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Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework. In this family, learning at each iteration consists of sampling a batch of trajectories using the current policy and fitting a new policy to maximize a return-weighted log-likelihood of actions. Although RWR is known to yield monotonic improvement of the policy under certain circumstances, whether and under which conditions RWR converges to the optimal policy have remained open questions. In this paper, we provide for the first time a

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Krylatov, A. Yu. "Global Optimum Search in the Network Design Problem." Computational Mathematics and Mathematical Physics 64, no. 10 (2024): 2238–55. https://doi.org/10.1134/s0965542524701239.

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10

Lim, Sungmook, and Joe Zhu. "Integrated data envelopment analysis: Global vs. local optimum." European Journal of Operational Research 229, no. 1 (2013): 276–78. http://dx.doi.org/10.1016/j.ejor.2013.02.023.

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11

Kearfott, Ralph Baker. "On rigorous upper bounds to a global optimum." Journal of Global Optimization 59, no. 2-3 (2014): 459–76. http://dx.doi.org/10.1007/s10898-014-0173-3.

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12

Hu, Mingwei, Hongguang Wang, and Xinan Pan. "Multi-objective global optimum design of collaborative robots." Structural and Multidisciplinary Optimization 62, no. 3 (2020): 1547–61. http://dx.doi.org/10.1007/s00158-020-02563-x.

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13

DJALIL, BOUDJEHEM, BOUDJEHEM BADREDDINE, and BOUKAACHE ABDENOUR. "REDUCING DIMENSION IN GLOBAL OPTIMIZATION." International Journal of Computational Methods 08, no. 03 (2011): 535–44. http://dx.doi.org/10.1142/s0219876211002460.

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In this paper, we propose a very interesting idea in global optimization making it easer and a low-cost task. The main idea is to reduce the dimension of the optimization problem in hand to a mono-dimensional one using variables coding. At this level, the algorithm will look for the global optimum of a mono-dimensional cost function. The new algorithm has the ability to avoid local optima, reduces the number of evaluations, and improves the speed of the algorithm convergence. This method is suitable for functions that have many extremes. Our algorithm can determine a narrow space around the gl

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14

Li, Ling Fang, Zhe Ming He, and You Xin Luo. "Reliability Optimization Design of Mechanical Parts Based on Global Optimization." Advanced Materials Research 503-504 (April 2012): 40–43. http://dx.doi.org/10.4028/www.scientific.net/amr.503-504.40.

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Generally, action of constraints exists in reliability optimization design of mechanical parts. Usually, general optimum methods are adopted in reliability optimization design, so the local solution is obtained. Aimed at this situation, a global optimum method was introduced by Lingo13.0 software. The result shows the model is practical and effective and its solution is global solution better than the result with general optimum method. As the method is simple, it is worthy to spread in optimum design

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15

Dou, Runliang. "Optimum design of 3-RRR planar parallel manipulators." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 2 (2010): 411–18. http://dx.doi.org/10.1243/09544062jmes1658.

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This article deals with the optimum design of 3-RRR planar parallel manipulators. Based on the kinematic model and Jacobian matrix, the global conditioning index, global velocity index and global stiffness index of 3-RRR parallel manipulators are investigated. The corresponding atlases are represented graphically in the established design space, and the geometrical parameters without dimension are determined. An example is presented to achieve the optimum dimensional parameter based on the optimum non-dimensional result. The result of this article is not only useful for the development of 3-RR

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16

Demir, Gökçe, and Üzeyir Aydın. "Comparison Of High-Tech Industries In Newly Industrialized Countries." Optimum Ekonomi ve Yönetim Bilimleri Dergisi 12, no. 1 (2025): 85–99. https://doi.org/10.17541/optimum.1565197.

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The aim of this study is to measure the competitiveness of Newly Industrialized Countries (NICs) in high-technology manufacturing industries. For this purpose, Ballassa's Revealed Comparative Advantage (BRCA) index, a static comparison, was first calculated. Subsequently, the dynamic comparison of the change in the Normalized Revealed Comparative Advantage (NRCA) index cross-country variation was calculated. The analysis revealed that India in ''basic pharmaceutical products and pharmaceutical preparations''; the Philippines in ''computer, electronics, and optical products''; and Brazil in ''

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17

Kinnaman, Thomas, and Hide-Fumi Yokoo. "The Environmental Consequences of Global Reuse." American Economic Review 101, no. 3 (2011): 71–76. http://dx.doi.org/10.1257/aer.101.3.71.

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This paper summarizes a two-country model that solves for optimal tax rates to achieve efficiency in an economy with international trade in used consumer electronics. If only the developed nation can tax the disposal of e-waste, then the global Pareto Optimum can be obtained by either imposing an import tariff on used consumer electronics or subsidizing the return of e-waste for disposal in the developed country. The global Pareto Optimum can also be obtained by reducing the disposal tax in the developed country to a level below the external marginal cost of disposal should no other policy opt

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18

Zheltkov, Dmitry, and Eugene Tyrtyshnikov. "Global optimization based on TT-decomposition." Russian Journal of Numerical Analysis and Mathematical Modelling 35, no. 4 (2020): 247–61. http://dx.doi.org/10.1515/rnam-2020-0021.

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AbstractIn contrast to many other heuristic and stochastic methods, the global optimization based on TT-decomposition uses the structure of the optimized functional and hence allows one to obtain the global optimum in some problem faster and more reliable. The method is based on the TT-cross method of interpolation of tensors. In this case, the global optimum can be found in practice even in the case when the approximation of the tensor does not possess a high accuracy. We present a detailed description of the method and its justification for the matrix case and rank-1 approximation.

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19

Reynolds, Richard W., and Thomas M. Smith. "Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation." Journal of Climate 7, no. 6 (1994): 929–48. http://dx.doi.org/10.1175/1520-0442(1994)007<0929:igssta>2.0.co;2.

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20

SIMOES, L. M. C. "SEARCH FOR THE GLOBAL OPTIMUM OF LEAST VOLUME TRUSSES." Engineering Optimization 11, no. 1-2 (1987): 49–67. http://dx.doi.org/10.1080/03052158708941036.

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21

Castillo, Enrique, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas, and Amparo Alonso-Betanzos. "A Global Optimum Approach for One-Layer Neural Networks." Neural Computation 14, no. 6 (2002): 1429–49. http://dx.doi.org/10.1162/089976602753713007.

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The article presents a method for learning the weights in one-layer feed-forward neural networks minimizing either the sum of squared errors or the maximum absolute error, measured in the input scale. This leads to the existence of a global optimum that can be easily obtained solving linear systems of equations or linear programming problems, using much less computational power than the one associated with the standard methods. Another version of the method allows computing a large set of estimates for the weights, providing robust, mean or median, estimates for them, and the associated standa

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22

Kovács, Z., Z. Ercsey, F. Friedler, and L. T. Fan. "Separation-network synthesis: global optimum through rigorous super-structure." Computers & Chemical Engineering 24, no. 8 (2000): 1881–900. http://dx.doi.org/10.1016/s0098-1354(00)00568-8.

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23

Gadallah, Mohamed H. "Tolerance optimisation problem using a near-to-global optimum." International Journal of Experimental Design and Process Optimisation 2, no. 4 (2011): 318. http://dx.doi.org/10.1504/ijedpo.2011.043567.

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24

Lange, Andreas, and Ferit Küçükay. "Determine of the Global Energy Optimum of Hybrid Drives." ATZ worldwide 119, no. 9 (2017): 68–73. http://dx.doi.org/10.1007/s38311-017-0083-3.

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25

Floudas, C. A., A. Aggarwal, and A. R. Ciric. "Global optimum search for nonconvex NLP and MINLP problems." Computers & Chemical Engineering 13, no. 10 (1989): 1117–32. http://dx.doi.org/10.1016/0098-1354(89)87016-4.

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26

Susiarjo, Gundo, Sree N. Sreenath, and Ali M. Vali. "Optimum Supportable Global Population: Water Accounting and Dietary Considerations." Environment, Development and Sustainability 8, no. 3 (2006): 313–49. http://dx.doi.org/10.1007/s10668-005-6979-x.

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27

Çakır Gündoğdu, Burçin, and Hakan Kahyaoğlu. "Climate Change-Trade Openness- Financial Depth Index-Economic Growth Nexus: A Study of G-20." Optimum Ekonomi ve Yönetim Bilimleri Dergisi 11, no. 2 (2024): 325–48. http://dx.doi.org/10.17541/optimum.1416719.

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This study examines the impact of climate change, which is seen as an environmental problem but has the power to affect the economic and financial structure, of trade openness, economic growth, and depth of financial markets in G-20 countries between 1980 and 2020. It has been concluded that the variables can be used as a policy tool in this context, where the development of trade openness and financial depth in the G-20 countries, which account for 80% of global trade, has the effect of reducing climate change by creating environmentally friendly economic growth. Panel Var and Holtz-Eakin, Ne

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28

Metwalli, S. M. "Optimum Nonlinear Suspension Systems." Journal of Mechanisms, Transmissions, and Automation in Design 108, no. 2 (1986): 197–202. http://dx.doi.org/10.1115/1.3260802.

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Global optimal isolation is presented in this paper. Results indicate that to optimally isolate a system, it should be totally disconnected from the disturbance. A model is then selected to optimize nonlinear suspension systems which, in the limits, approach optimal isolation characteristics. Nondimensional design parameters that themselves are made to be dependent on the input are employed. A step disturbance is selected to equivalently represent real excitations. The objective function incorporates the tire-terrain normal force as an indicator of the vehicle controllability which is unconstr

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29

Jaddi, Najmeh Sadat, and Salwani Abdullah. "Global search in single-solution-based metaheuristics." Data Technologies and Applications 54, no. 3 (2020): 275–96. http://dx.doi.org/10.1108/dta-07-2019-0115.

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PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorith

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30

Jain, P., and A. M. Agogino. "Global Optimization Using the Multistart Method." Journal of Mechanical Design 115, no. 4 (1993): 770–75. http://dx.doi.org/10.1115/1.2919267.

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Multistart is a stochastic global optimization method for finding the global optimum of highly nonlinear mechanical problems. In this paper we introduce and develop a variant of the multistart method in which a fraction of the sample points in the feasible region with smallest function value are clustered using the Vector Quantization technique. The theories of lattices and sphere packing are used to define optimal lattices. These lattices are optimal with respect to quantization error and are used as code points for vector quantization. The implementation of these ideas has resulted in the VQ

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31

Derigs, Ulrich. "Using Confidence Limits for the Global Optimum in Combinatorial Optimization." Operations Research 33, no. 5 (1985): 1024–49. http://dx.doi.org/10.1287/opre.33.5.1024.

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32

Agrawal, O. P., and Y. Xu. "On the global optimum path planning for redundant space manipulators." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 9 (1994): 1306–16. http://dx.doi.org/10.1109/21.310507.

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33

Cunefare, Kenneth A., and Gary H. Koopmann. "Global optimum active noise control: Surface and far‐field effects." Journal of the Acoustical Society of America 90, no. 1 (1991): 365–73. http://dx.doi.org/10.1121/1.401259.

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34

Dayou, J., and M. J. Brennan. "Optimum tuning of a vibration neutralizer for global vibration control." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 215, no. 8 (2001): 933–42. http://dx.doi.org/10.1177/095440620121500806.

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This paper is concerned with optimization of the natural frequency of a vibration neutralizer to minimize the global vibration of a structure at a single frequency. The optimization is carried out using a quadratic minimization technique to determine the dynamic stiffness of the control device that is required, and then the optimum resonance frequency of the neutralizer is determined. It is shown that, with the exception of very low frequencies, an optimally adjusted vibration neutralizer can be as effective as an active device at a single frequency. Simulations are presented with a single neu

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35

Karim, Asim, and Hojjat Adeli. "Global optimum design of cold-formed steel hat-shape beams." Thin-Walled Structures 35, no. 4 (1999): 275–88. http://dx.doi.org/10.1016/s0263-8231(99)00033-6.

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36

Duran Toksarı, M. "A heuristic approach to find the global optimum of function." Journal of Computational and Applied Mathematics 209, no. 2 (2007): 160–66. http://dx.doi.org/10.1016/j.cam.2006.10.074.

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37

Deissenberg, Christophe. "Interactive search for a global optimum in macroeconometric policy-making." Computers & Mathematics with Applications 21, no. 6-7 (1991): 203–13. http://dx.doi.org/10.1016/0898-1221(91)90174-3.

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38

Karim, Asim, and Hojjat Adeli. "Global Optimum Design of Cold-Formed Steel Z-Shape Beams." Practice Periodical on Structural Design and Construction 4, no. 1 (1999): 17–20. http://dx.doi.org/10.1061/(asce)1084-0680(1999)4:1(17).

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39

Karim, Asim, and Hojjat Adeli. "Global Optimum Design of Cold-Formed Steel I-Shape Beams." Practice Periodical on Structural Design and Construction 5, no. 2 (2000): 78–81. http://dx.doi.org/10.1061/(asce)1084-0680(2000)5:2(78).

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40

Wilde, Douglass J. "Convexity analysis in detecting a steel plant hidden global optimum." Journal of Global Optimization 3, no. 2 (1993): 117–31. http://dx.doi.org/10.1007/bf01096733.

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41

Öztopcu, Aslı. "An ARDL Study on Wastewater, Carbon Dioxide, and Blue Economy Impacts in Türkiye." Optimum Ekonomi ve Yönetim Bilimleri Dergisi 12, no. 1 (2025): 101–19. https://doi.org/10.17541/optimum.1581491.

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Pollution of water resources results in the degradation of aquatic ecosystems and poses a threat to the global Blue Economy. Water pollutants, especially in water source like oceans, seas, rivers, and lakes where aquaculture is prevalent, can lead to species alterations, reduced food quality, decreased tourism opportunities in coastal areas, reduced employment in the sector, and declining incomes. This study aims to capture the impact of municipal wastewater and carbon dioxide on income in Türkiye between 1994 and 2022 using the ARDL approach. It examines the relationship between municipal was

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42

Shao, Wei, and Guangbao Guo. "Multiple-Try Simulated Annealing Algorithm for Global Optimization." Mathematical Problems in Engineering 2018 (July 17, 2018): 1–11. http://dx.doi.org/10.1155/2018/9248318.

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Simulated annealing is a widely used algorithm for the computation of global optimization problems in computational chemistry and industrial engineering. However, global optimum values cannot always be reached by simulated annealing without a logarithmic cooling schedule. In this study, we propose a new stochastic optimization algorithm, i.e., simulated annealing based on the multiple-try Metropolis method, which combines simulated annealing and the multiple-try Metropolis algorithm. The proposed algorithm functions with a rapidly decreasing schedule, while guaranteeing global optimum values.

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43

Aqib, Amjad Islam, Aftab Ahmad Anjum, Md Atiqul Islam, Asad Murtaza, and Aziz ur Rehman. "Recent Global Trends in Vaccinology, Advances and Challenges." Vaccines 11, no. 3 (2023): 520. http://dx.doi.org/10.3390/vaccines11030520.

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44

Dardani, Ian, and Gerard F. Jones. "Algorithms for optimization of branching gravity-driven water networks." Drinking Water Engineering and Science 11, no. 1 (2018): 67–85. http://dx.doi.org/10.5194/dwes-11-67-2018.

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Abstract. The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a co

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45

Huang, Yao-Huei, Yu-Chien Ko, and Hao-Chun Lu. "An Optimal Classification Method for Biological and Medical Data." Mathematical Problems in Engineering 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/398232.

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This paper proposes a union of hyperspheres by the mixed-integer nonlinear program to classify biological and medical datasets. A classifying program with nonlinear terms uses piecewise linearization technique to obtain a global optimum. The numerical examples illustrate that the proposed method can obtain the global optimum more effectively than current methods.

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46

Nakajima, Y., H. Kadowaki, T. Kamegawa, and K. Ueno. "Application of a Neural Network for the Optimization of Tire Design." Tire Science and Technology 27, no. 2 (1999): 62–83. http://dx.doi.org/10.2346/1.2135978.

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Abstract When there are multiple peaks in the design space, it is difficult to obtain the global optimum by mathematical programming. The procedure to obtain the pseudo-global optimum in the multiple-peak problem is to select the best solution from many local optimums starting from different initial values. However, this approach requires large computer resources and has difficulty in obtaining the pseudo-optimum in a problem with many peaks, due to the complicated design space. We propose more efficient and robust optimization procedures for the multiple-peak problem. In this approach, a neur

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47

Baselga, Sergio, Ivandro Klein, Stefano Sampaio Suraci, Leonardo Castro de Oliveira, Marcelo Tomio Matsuoka, and Vinicius Francisco Rofatto. "Global Optimization of Redescending Robust Estimators." Mathematical Problems in Engineering 2021 (July 23, 2021): 1–13. http://dx.doi.org/10.1155/2021/9929892.

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Robust estimation has proved to be a valuable alternative to the least squares estimator for the cases where the dataset is contaminated with outliers. Many robust estimators have been designed to be minimally affected by the outlying observations and produce a good fit for the majority of the data. Among them, the redescending estimators have demonstrated the best estimation capabilities. It is little known, however, that the success of a robust estimation method depends not only on the robust estimator used but also on the way the estimator is computed. In the present paper, we show that for

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48

Gao, Zhi Qiang, Li Xia Liu, Wei Wei Kong, and Xiao Hong Wang. "A Composite Framework of Cuckoo Search and PSO Algorithm." Applied Mechanics and Materials 713-715 (January 2015): 1491–94. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1491.

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A novel composite framework of Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithm called CS-PSO is proposed in this paper. In CS-PSO, initialization is substituted by chaotic system, and then Cuckoo shares optimums in the global best solutions pool with particles in PSO to improve parallel cooperation and social interaction. Furthermore, Cloud Model, famous for its outstanding characteristics of the process of transforming qualitative concepts to a set of quantitative numerical values, is adopted to exploit the surrounding of the local solutions obtained from the global best so

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49

Yeng, Foo Fong, Soo Kum Yoke, and Azrina Suhaimi. "The saturation of population fitness as a stopping criterion in genetic algorithm." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4130. http://dx.doi.org/10.11591/ijece.v9i5.pp4130-4137.

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Genetic Algorithm is an algorithm imitating the natural evolution process in solving optimization problems. All feasible (candidate) solutions would be encoded into chromosomes and undergo the execution of genetic operators in evolution. The evolution itself is a process searching for optimum solution. The searching would stop when a stopping criterion is met. Then, the fittest chromosome of last generation is declared as the optimum solution. However, this optimum solution might be a local optimum or a global optimum solution. Hence, an appropriate stopping criterion is important such that th

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Foo, Fong Yeng, Kum Yoke Soo, and Suhaimi Azrina. "The saturation of population fitness as a stopping criterion in genetic algorithm." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4130–37. https://doi.org/10.11591/ijece.v9i5.pp4130-4137.

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Genetic Algorithm is an algorithm imitating the natural evolution process in solving optimization problems. All feasible (candidate) solutions would be encoded into chromosomes and undergo the execution of genetic operators in evolution. The evolution itself is a process searching for optimum solution. The searching would stop when a stopping criterion is met. Then, the fittest chromosome of last generation is declared as the optimum solution. However, this optimum solution might be a local optimum or a global optimum solution. Hence, an appropriate stopping criterion is important such that th

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