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Journal articles on the topic 'Loss optimization'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Fuchs, Marco, Cagatay Necati Dagli, and Stephan Kabelac. "Shape Optimization of Heat Exchanger Fin Structures Using the Adjoint Method and Their Experimental Validation." Energies 17, no. 5 (2024): 1246. http://dx.doi.org/10.3390/en17051246.

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The freedom of additive manufacturing allows for the production of heat-transferring structures that are optimized in terms of heat transfer and pressure loss using various optimization methods. One question is whether the structural optimizations made can be reproduced by additive manufacturing and whether the adaptations can also be verified experimentally. In this article, adjoint optimization is used to optimize a reference structure and then examine the optimization results experimentally. For this purpose, optimizations are carried out on a 2D model as well as a 3D model. The material ch

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2

Fulga, Cristinca. "Portfolio optimization under loss aversion." European Journal of Operational Research 251, no. 1 (2016): 310–22. http://dx.doi.org/10.1016/j.ejor.2015.11.038.

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3

Park, Kyungchul, and Kyungsik Lee. "Distribution-robust loss-averse optimization." Optimization Letters 11, no. 1 (2016): 153–63. http://dx.doi.org/10.1007/s11590-016-1002-z.

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4

Tsouvalas, Nikolaos, Ioannis Xydis, Ioannis Tsakirakis, and Z. Papazacharopoulos. "Asynchronous motor drive loss optimization." Journal of Materials Processing Technology 181, no. 1-3 (2007): 301–6. http://dx.doi.org/10.1016/j.jmatprotec.2006.03.062.

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5

Ahmad, Faris, Musirin Ismail, Jelani Shahrizal, Amri Ismail Saiful, Helmi Mansor Mohd, and V. Senthil Kumar A. "Tap changer optimisation using embedded differential evolutionary programming technique for loss control in power system." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2253–60. https://doi.org/10.11591/eei.v9i6.2505.

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Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and syst

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6

Dr.K., Lenin *1. "DIMENSIONED PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER OPTIMIZATION PROBLEM." International Journal of Research - Granthaalayah 6, no. 4 (2018): 281–90. https://doi.org/10.5281/zenodo.1248180.

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This paper present’s Dimensioned Particle Swarm Optimization (DPSO) algorithm for solving Reactive power optimization (RPO) problem. Dimensioned extension is introduced to particles in the particle swarm optimization (PSO) model in order to overcome premature convergence in interactive optimization. In the performance of basic PSO often flattens out with a loss of diversity in the search space as resulting in local optimal solution. Proposed algorithm has been tested in standard IEEE 57 test bus system and compared to other standard algorithms. Simulation results reveal about the best pe

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7

Kim, Minyoung, and Timothy Hospedales. "A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17913–20. https://doi.org/10.1609/aaai.v39i17.33970.

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We tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learning and more. These problems are often formalized as Bi-Level Optimizations (BLO). We introduce a novel perspective by turning a given BLO problem into a stochastic optimization, where the inner loss function becomes a smooth probability distribution, and the outer loss becomes an expected loss over the inner distribution. To solve this stochastic optimization, we adopt Stochastic Gradient Langevin Dynamics (SGLD) MC

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Faris, Ahmad, Ismail Musirin, Shahrizal Jelani, Saiful Amri Ismail, Mohd Helmi Mansor, and A. V. Senthil Kumar. "Tap changer optimisation using embedded differential evolutionary programming technique for loss control in power system." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2253–60. http://dx.doi.org/10.11591/eei.v9i6.2505.

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Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and syst

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9

Yang, Zhenhuan, Wei Shen, Yiming Ying, and Xiaoming Yuan. "Stochastic AUC optimization with general loss." Communications on Pure & Applied Analysis 19, no. 8 (2020): 4191–212. http://dx.doi.org/10.3934/cpaa.2020188.

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10

Jourjon, Guillaume, Emmanuel Lochin, and Laurent Dairaine. "Optimization of TFRC Loss History Initialization." IEEE Communications Letters 11, no. 3 (2007): 276–78. http://dx.doi.org/10.1109/lcomm.2007.061707.

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11

Sauvet, Fabien, and Mounir Chennaoui. "Sleep optimization to prevent sleep loss." Journal of Science and Medicine in Sport 20 (November 2017): S17. http://dx.doi.org/10.1016/j.jsams.2017.09.042.

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12

Dixit, Rituraj, Sarabjeet Singh Bedi, Ibrahim Aljubayri, and Mohammad Zubair Khan. "Implementation of Single Candidate Loss Optimization Algorithm for Loss Optimization of Bhojpuri-English Machine Translation Model." Journal of Computer Science 21, no. 5 (2025): 1059–70. https://doi.org/10.3844/jcssp.2025.1059.1070.

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13

Xie, Jihua, Chang Chen, and Huan Long. "A Loss Reduction Optimization Method for Distribution Network Based on Combined Power Loss Reduction Strategy." Complexity 2021 (July 23, 2021): 1–13. http://dx.doi.org/10.1155/2021/9475754.

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Power loss reflects the effective utilization rate of energy and the management level of power grids. In this paper, we propose a combined power loss reduction strategy optimization framework to improve the power loss reduction effect in a distribution network. The weak points of the distribution network are analyzed based on power flow calculation. The corresponding power loss reduction strategies are generated considering the following three aspects: replacing distribution lines, distribution transformers, and reactive power compensation. A combined power loss reduction strategy optimization

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14

Qi, Bin, Su Jun Dong, and Jun Wang. "Optimization of Loss Models for Centrifugal Compressor Performance Prediction Based on Numerical Analysis Results." Applied Mechanics and Materials 300-301 (February 2013): 225–31. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.225.

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An optimization of loss models for performance prediction of centrifugal compressors has been conducted in this paper. A centrifugal compressor with vaneless diffuser (VLD) and vaned diffuser (VD) is selected for the present study. The study begins with a numerical analysis, using commercial software ANSYS CFX. The numerical result is used to modify mean streamline analysis loss models. Matlab optimization toolbox is used for this purpose. Also, a matlab program is compiled to calculate the off-design performance of the centrifugal compressor. The results show that after optimizating the loss

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15

Schnieper, René. "Portfolio Optimization." ASTIN Bulletin 30, no. 1 (2000): 195–248. http://dx.doi.org/10.2143/ast.30.1.504632.

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AbstractBased on the profit and loss account of an insurance company we derive a probabilistic model for the financial result of the company, thereby both assets and liabilities are marked to market. We thus focus on the economic value of the company.We first analyse the underwriting risk of the company. The maximization of the risk return ratio of the company is derived as optimality criterion. It is shown how the risk return ratio of heterogeneous portfolios or of catastrophe exposed portfolios can be dramatically improved through reinsurance. The improvement of the risk return ratio through

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16

Lenin, K. "REDUCTION OF TRUE POWER LOSS BY IMPROVED ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 8 (2018): 105–13. http://dx.doi.org/10.29121/granthaalayah.v6.i8.2018.1404.

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This paper proposes Improved Brain Storm Optimization (IBSO) algorithm is used for solving reactive power problem. predictably, optimization algorithm stimulated by human being inspired problem-solving procedure should be highly developed than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, a new Improved brain storm optimization algorithm defined, which was stimulated by the human brainstorming course of action. In the projected Improved Brain Storm Optimization (IBSO) algorithm, the vibrant clustering strategy is used to perk up the k-means clusteri

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Dr., K. Lenin. "REDUCTION OF TRUE POWER LOSS BY IMPROVED ALGORITHM." International Journal of Research - Granthaalayah 6, no. 8 (2018): 105–13. https://doi.org/10.5281/zenodo.1403827.

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This paper proposes Improved Brain Storm Optimization (IBSO) algorithm is used for solving reactive power problem. predictably, optimization algorithm stimulated by human being inspired problem-solving procedure should be highly developed than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, a new Improved brain storm optimization algorithm defined, which was stimulated by the human brainstorming course of action. In the projected Improved Brain Storm Optimization (IBSO) algorithm, the vibrant clustering strategy is used to perk up the k-means clusteri

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18

Tao, Ziyou. "Stress Analysis and Size Optimization of Suspension Beam Structure of Robot Manipulator." Theoretical and Natural Science 2, no. 1 (2023): 92–96. http://dx.doi.org/10.54254/2753-8818/2/20220174.

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In this paper, the stress and deformation of a manipulator structure are analyzed, and the structural optimization design is carried out. The initial configuration is a cantilever beam structure with rectangular section, which is fixed at one end and bears a load of 1 ton at the other end. After stress and deformation analysis with ABAQUS software and SolidWorks software, three optimizations were carried out. Geometric configuration optimization, topology optimization and material optimization. After optimization, the overall quality of the structure is reduced by 80%, and there is no great lo

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19

Lenin, K. "TRUE POWER LOSS REDUCTION BY WOLF OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 11 (2018): 323–29. http://dx.doi.org/10.29121/granthaalayah.v6.i11.2018.1134.

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In this paper wolf optimization algorithm (WOA) has been applied for solving reactive power problem. In order to enhance the search procedure the basic qualities of particle swarm optimization has been intermingled to improve the capability of the search to reach a global solution. Efficiency of the projected wolf optimization algorithm (WOA) is tested in standard IEEE 30 bus test system. Simulation study indicates wolf optimization algorithm (WOA) performs well in tumbling the actual power losses& particularly voltage stability has been enriched.

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20

Dr., K. Lenin. "TRUE POWER LOSS REDUCTION BY WOLF OPTIMIZATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 11 (2018): 323–29. https://doi.org/10.5281/zenodo.2173816.

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In this paper wolf optimization algorithm (WOA) has been applied for solving reactive power problem. In order to enhance the search procedure the basic qualities of particle swarm optimization has been intermingled to improve the capability of the search to reach a global solution. Efficiency of the projected wolf optimization algorithm (WOA) is tested in standard IEEE 30 bus test system. Simulation study indicates wolf optimization algorithm (WOA) performs well in tumbling the actual power losses& particularly voltage stability has been enriched.

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21

Meseret, Yenesew. "ENHANCED PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHM FOR REACTIVE POWER OPTIMIZATION IN THE DISTRIBUTION SYSTEM." International Journal of Engineering Research and Modern Education 2, no. 2 (2017): 56–64. https://doi.org/10.5281/zenodo.1130820.

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The problem of optimal capacitor allocation in electric distribution systems involves maximizing energy utilization, feeder loss reduction, and voltage profile improvement. The feeder loss can be separated into two parts based on the active and reactive power loss components. This paper presents an optimization method for minimizing the loss associated with the reactive component of branch currents by allocating optimal reactive power in the distribution system. In this paper, particle swarm optimization (PSO) algorithm is used for reactive power optimization problem in the distribution system

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22

Srinivasan, Sriram, Behrouz Babaki, Golnoosh Farnadi, and Lise Getoor. "Lifted Hinge-Loss Markov Random Fields." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7975–83. http://dx.doi.org/10.1609/aaai.v33i01.33017975.

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Statistical relational learning models are powerful tools that combine ideas from first-order logic with probabilistic graphical models to represent complex dependencies. Despite their success in encoding large problems with a compact set of weighted rules, performing inference over these models is often challenging. In this paper, we show how to effectively combine two powerful ideas for scaling inference for large graphical models. The first idea, lifted inference, is a wellstudied approach to speeding up inference in graphical models by exploiting symmetries in the underlying problem. The s

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23

Lenin, K. "ENHANCED ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS." International Journal of Research -GRANTHAALAYAH 6, no. 7 (2018): 45–51. http://dx.doi.org/10.29121/granthaalayah.v6.i7.2018.1282.

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In this paper, Enhanced Aggressive Weed Optimization (EWO) algorithm is applied to solve the optimal reactive power Problem. Aggressive Weed Optimization is a stochastic search algorithm that imitate natural deeds of weeds in colonize and detection of appropriate place for growth and reproduction. Enhanced Aggressive Weed Optimization (EWO) algorithm is based on hybridization of genetic algorithm with weed optimization algorithm which refers combination of crossover and mutation of genetic algorithm, and by the use of the cross factor new species are arisen. Proposed Enhanced Aggressive Weed O

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24

Dr., K. Lenin. "ENHANCED ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS." International Journal of Research - Granthaalayah 6, no. 7 (2018): 45–51. https://doi.org/10.5281/zenodo.1322997.

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In this paper, Enhanced Aggressive Weed Optimization (EWO) algorithm is applied to solve the optimal reactive power Problem. Aggressive Weed Optimization is a stochastic search algorithm that imitate natural deeds of weeds in colonize and detection of appropriate place for growth and reproduction. Enhanced Aggressive Weed Optimization (EWO) algorithm is based on hybridization of genetic algorithm with weed optimization algorithm which refers combination of crossover and mutation of genetic algorithm, and by the use of the cross factor new species are arisen. Proposed Enhanced Aggressive Weed O

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25

Lin, Jun-Lin, Yiqing Zhang, Kunhuang Zhu, Binbin Chen, and Feng Zhang. "Asymmetric Loss Functions for Contract Capacity Optimization." Energies 13, no. 12 (2020): 3123. http://dx.doi.org/10.3390/en13123123.

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For high-voltage and extra-high-voltage consumers, the electricity cost depends not only on the power consumed but also on the contract capacity. For the same amount of power consumed, the smaller the difference between the contract capacity and the power consumed, the smaller the electricity cost. Thus, predicting the future power demand for setting the contract capacity is of great economic interest. In the literature, most works predict the future power demand based on a symmetric loss function, such as mean squared error. However, the electricity pricing structure is asymmetric to the unde

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26

MOCHIZUKI, Tomoya, Takaaki NAKAMURA, Masahiko KIMURA, and Masaru HOSHIYA. "Optimization of earthquake insurance considering subjective loss." Doboku Gakkai Ronbunshu, no. 703 (2002): 203–10. http://dx.doi.org/10.2208/jscej.2002.703_203.

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27

Lenin, Kanagasabai. "Power loss reduction by gryllidae optimization algorithm." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 3 (2020): 179. http://dx.doi.org/10.11591/ijict.v9i3.pp179-184.

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<p><span lang="EN-US">This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds.

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28

Stevens, David P., and R. C. Baker. "A Generalized Loss Function for Process Optimization." Decision Sciences 25, no. 1 (1994): 41–56. http://dx.doi.org/10.1111/j.1540-5915.1994.tb00515.x.

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29

Makris, Spilios E., Clive L. Dym, and J. MacGregor Smith. "Transmission loss optimization in acoustic sandwich panels." Journal of the Acoustical Society of America 79, no. 6 (1986): 1833–43. http://dx.doi.org/10.1121/1.393746.

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30

Choi, John M., Wei Liang, Yong Xu, and Amnon Yariv. "Loss optimization of transverse Bragg resonance waveguides." Journal of the Optical Society of America A 21, no. 3 (2004): 426. http://dx.doi.org/10.1364/josaa.21.000426.

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31

Schäfer, R., M. Rührig, and A. Hubert. "Loss optimization for iron-rich metallic glasses." Physica Scripta 40, no. 4 (1989): 552–57. http://dx.doi.org/10.1088/0031-8949/40/4/025.

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32

Witting, Lars, and Volker Loeschcke. "Biodiversity conservation: Reserve optimization or loss minimization?" Trends in Ecology & Evolution 8, no. 11 (1993): 417. http://dx.doi.org/10.1016/0169-5347(93)90045-q.

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33

Chavan, Sudarshan L. "Distribution Feeder Loss Optimization: A Case Study." International Journal of Automation and Smart Technology 13, no. 1 (2023): 2451. http://dx.doi.org/10.5875/ausmt.v13i1.2451.

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34

Kanagasabai, Lenin. "Power loss reduction by gryllidae optimization algorithm." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 3 (2020): 179–84. https://doi.org/10.11591/ijict.v9i3.pp179-184.

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This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization A

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35

Maistrenko, O. S. "Derivative-free optimization for custom loss functions." Journal of Numerical and Applied Mathematics, no. 1 (2025): 77–89. https://doi.org/10.17721/2706-9699.2025.1.07.

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Derivative-free optimization (DFO) has emerged as a powerful technique for solving optimization problems where the gradient of the objective function is either unavailable, expensive to compute, or non-smooth. This article explores the application of DFO methods to optimize custom loss functions in machine learning and other fields. The paper also highlights the challenges and potential improvements in the current DFO approaches, offering insights for further research and practical applications.

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36

Liu, Mengxin, Wenyuan Tao, Xiao Zhang, Yi Chen, Jie Li, and Chung-Ming Own. "GO Loss: A Gaussian Distribution-Based Orthogonal Decomposition Loss for Classification." Complexity 2019 (December 12, 2019): 1–10. http://dx.doi.org/10.1155/2019/9206053.

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We present a novel loss function, namely, GO loss, for classification. Most of the existing methods, such as center loss and contrastive loss, dynamically determine the convergence direction of the sample features during the training process. By contrast, GO loss decomposes the convergence direction into two mutually orthogonal components, namely, tangential and radial directions, and conducts optimization on them separately. The two components theoretically affect the interclass separation and the intraclass compactness of the distribution of the sample features, respectively. Thus, separatel

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Jiang, Yajie, Siyuan Cheng, and Haoze Wang. "Distributed Integral Convex Optimization-Based Current Control for Power Loss Optimization in Direct Current Microgrids." Energies 16, no. 24 (2023): 8106. http://dx.doi.org/10.3390/en16248106.

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Due to the advantages of fewer energy conversion stages and a simple structure, direct current (DC) microgrids are being increasingly studied and applied. To minimize distribution loss in DC microgrids, a systematic optimal control framework is proposed in this paper. By considering conduction loss, switching loss, reverse recovery loss, and ohmic loss, the general loss model of a DC microgrid is formulated as a multi-variable convex function. To solve the objective function, a top-layer distributed integral convex optimization algorithm (DICOA) is designed to optimize the current-sharing coef

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38

Lenin, K. "DIMENSIONED PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER OPTIMIZATION PROBLEM." International Journal of Research -GRANTHAALAYAH 6, no. 4 (2018): 281–90. http://dx.doi.org/10.29121/granthaalayah.v6.i4.2018.1663.

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This paper present’s Dimensioned Particle Swarm Optimization (DPSO) algorithm for solving Reactive power optimization (RPO) problem. Dimensioned extension is introduced to particles in the particle swarm optimization (PSO) model in order to overcome premature convergence in interactive optimization. In the performance of basic PSO often flattens out with a loss of diversity in the search space as resulting in local optimal solution. Proposed algorithm has been tested in standard IEEE 57 test bus system and compared to other standard algorithms. Simulation results reveal about the best performa

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39

Dr., K. Lenin. "LESSENING OF ACTUAL POWER LOSS BY MODIFIED ALGORITHM." International Journal of Research - Granthaalayah 6, no. 8 (2018): 159–67. https://doi.org/10.5281/zenodo.1403848.

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This paper presents a Modified Teaching-Learning-Based Optimization (MTLBO) algorithm for solving reactive power flow problem. Basic Teaching-Learning-Based Optimization (TLBO) is reliable, accurate and vigorous for solving the optimization problems. Also, it has been found that TLBO algorithm slow in convergence due to its high concentration in the accuracy. This paper presents an, Modified version of TLBO algorithm, called as Modified Teaching-Learning-Based Optimization (MTLBO). A parameter called as “weight” has been included in the fundamental TLBO equations & subsequently

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Dr.K.Lenin. "ENHANCED SEEKER OPTIMIZATION ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS." International Journal of Research - Granthaalayah 5, no. 10 (2017): 336–47. https://doi.org/10.5281/zenodo.1045996.

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This paper projects Enhanced Seeker Optimization (ESO) algorithm for solving optimal reactive power problem. Seeker optimization algorithm (SOA) models the deeds of human search population based on their memory, experience, uncertainty reasoning and communication with each other. In Artificial Bee Colony (ABC) algorithm the colony consists of three groups of bees: employed bees, onlookers and scouts. All bees that are presently exploiting a food source are known as employed bees. The number of the employed bees is equal to the number of food sources and an employed bee is allocated to one of t

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Rayner, D. Chris, Michael Bowling, and Nathan Sturtevant. "Euclidean Heuristic Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 81–86. http://dx.doi.org/10.1609/aaai.v25i1.7815.

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We pose the problem of constructing good search heuristics as an optimization problem: minimizing the loss between the true distances and the heuristic estimates subject to admissibility and consistency constraints. For a well-motivated choice of loss function, we show performing this optimization is tractable. In fact, it corresponds to a recently proposed method for dimensionality reduction. We prove this optimization is guaranteed to produce admissible and consistent heuristics, generalizes and gives insight into differential heuristics, and show experimentally that it produces strong heuri

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Dr., K. Lenin. "ACTIVE POWER LOSS DIMINUTION & VOLTAGE STABILITY ENHANCEMENT BY RED WOLF OPTIMIZATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 11 (2018): 355–65. https://doi.org/10.5281/zenodo.2175738.

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In this paper optimal reactive power dispatch problem (ORPD), has been solved by Enriched Red Wolf Optimization (ERWO) algorithm. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with swarm based algorithm called as particle swarm optimization (PSO) algorithm. In the approach each Red wolf has a flag vector, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Exploration capability of the projected Red wolf optimization algorithm has been enriched by hybridization of both WO with PSO. Efficiency of the projec

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Dr, K. Lenin. "HYBRIDIZATION OF ANT COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research - Granthaalayah 6, no. 12 (2018): 121–27. https://doi.org/10.5281/zenodo.2532382.

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In this work Ant colony optimization algorithm (ACO) & particle swarm optimization (PSO) algorithm has been hybridized (called as APA) to solve the optimal reactive power problem. In this algorithm, initial optimization is achieved by particle swarm optimization algorithm and then the optimization process is carry out by ACO around the best solution found by PSO to finely explore the design space. In order to evaluate the proposed APA, it has been tested on IEEE 300 bus system and compared to other standard algorithms. Simulations results show that proposed APA algorithm performs well in r

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Negi, Parimarjan, Ryan Marcus, Andreas Kipf, et al. "Flow-loss." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2019–32. http://dx.doi.org/10.14778/3476249.3476259.

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Recently there has been significant interest in using machine learning to improve the accuracy of cardinality estimation. This work has focused on improving average estimation error, but not all estimates matter equally for downstream tasks like query optimization. Since learned models inevitably make mistakes, the goal should be to improve the estimates that make the biggest difference to an optimizer. We introduce a new loss function, Flow-Loss, for learning cardinality estimation models. Flow-Loss approximates the optimizer's cost model and search algorithm with analytical functions, which

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Dr., K. Lenin. "BANKS CAPACITOR COMPENSATION FOR CRITICAL NODAL DETECTION BY AUGMENTED RED WOLF OPTIMIZATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 10 (2018): 169–75. https://doi.org/10.5281/zenodo.1476675.

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In this paper Banks Capacitor Compensation for Critical Nodal Detections by Augmented Red Wolf Optimization Algorithm has been worked out. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with swarm based algorithm called as particle swarm optimization (PSO) algorithm. In the approach each Red wolf has a flag vector, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Exploration capability of the projected Red wolf optimization algorithm has been enriched by hybridization of both WO with PSO. Efficiency of t

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Lenin, Kanagasabai. "Enhanced whale optimization algorithm for active power loss diminution." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 1 (2020): 19. http://dx.doi.org/10.11591/ijict.v9i1.pp19-23.

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In this paper Enhanced whale Optimization Algorithm (EWO) proposed to solve the optimal reactive power problem. Whale optimization algorithm is modeled by Bubble-net hunting tactic. In the projected optimization algorithm an inertia weight ω ∈ [1, 0] has been introduced to perk up the search ability. Whales are commonly moving 10-16 meters down then through the bubbles which are created artificially then they encircle the prey and move upward towards the surface of sea. Proposed Enhanced whale optimization algorithm (EWO) is tested in standard IEEE 57 bus systems and power loss reduced conside

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DIRECTOR, STEPHEN W., PETER FELDMANN, and KANNAN KRISHNA. "OPTIMIZATION OF PARAMETRIC YIELD: A TUTORIAL." International Journal of High Speed Electronics and Systems 03, no. 01 (1992): 95–136. http://dx.doi.org/10.1142/s0129156492000059.

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Yield loss can be characterized as either catastrophic or parametric. Catastrophic yield loss is primarily due to local disturbances, such as spot defects, that occur in a manufacturing process. On the other hand, parametric yield loss is due to global disturbances, such as mask misalignment. In this paper we briefly explore these two different types of yield loss and then review some methods that have been developed to maximize parametric yield.

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Dr.K.Lenin. "REDUCTION OF REAL POWER LOSS BY ADVANCED PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 2 (2018): 166–81. https://doi.org/10.5281/zenodo.1189235.

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This paper presents Advanced Particle Swarm Optimization (APSO) algorithm for solving optimal reactive power problem. In this work Biological Particle swarm Optimization algorithm utilized to solve the problem by eliminating inferior population & keeping superior population, to make full use of population resources and speed up the algorithm convergence. Projected Advanced Particle Swarm Optimization (APSO) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed Advanced Particle Swarm Optimization (A

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Kowsalya, M., K. K. Ray, and D. P. Kothari. "Loss Optimization for Voltage Stability Enhancement Incorporating UPFC Using Particle Swarm Optimization." Journal of Electrical Engineering and Technology 4, no. 4 (2009): 492–98. http://dx.doi.org/10.5370/jeet.2009.4.4.492.

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Lenin, K. "ACTUAL POWER LOSS REDUCTION BY AUGMENTED PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 9 (2018): 212–19. http://dx.doi.org/10.29121/granthaalayah.v6.i9.2018.1222.

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This paper presents an advanced particle swarm optimization Algorithm for solving the reactive power problem in power system. Bacterial Foraging Optimization Algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposed a new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO) called advanced bacterial foraging-oriented particle swarm optimization (ABFPSO) algorithm for solving reactive power problem. The simul

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