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Journal articles on the topic 'Algorithm stability'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Maibed, Zena H., and Noor N. Salem. "On the Stability and Acceleration of Projection Algorithms." Ibn AL-Haitham Journal For Pure and Applied Sciences 36, no. 1 (January 20, 2023): 292–99. http://dx.doi.org/10.30526/36.1.2923.

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The focus of this paper is the presentation of a new type of mapping called projection Jungck zn- Suzuki generalized and also defining new algorithms of various types (one-step and two-step algorithms) (projection Jungck-normal N algorithm, projection Jungck-Picard algorithm, projection Jungck-Krasnoselskii algorithm, and projection Jungck-Thianwan algorithm). The convergence of these algorithms has been studied, and it was discovered that they all converge to a fixed point. Furthermore, using the previous three conditions for the lemma, we demonstrated that the difference between any two sequences is zero. These algorithms' stability was demonstrated using projection Jungck Suzuki generalized mapping. In contrast, the rate of convergence of these algorithms was demonstrated by contrasting the rates of convergence of the various algorithms, leading us to conclude that the projection Jungck-normal algorithm is the fastest of all the algorithms mentioned above.

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2

Banakar, Ahmad. "Lyapunov Stability Analysis of Gradient Descent-Learning Algorithm in Network Training." ISRN Applied Mathematics 2011 (July 5, 2011): 1–12. http://dx.doi.org/10.5402/2011/145801.

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The Lyapunov stability theorem is applied to guarantee the convergence and stability of the learning algorithm for several networks. Gradient descent learning algorithm and its developed algorithms are one of the most useful learning algorithms in developing the networks. To guarantee the stability and convergence of the learning process, the upper bound of the learning rates should be investigated. Here, the Lyapunov stability theorem was developed and applied to several networks in order to guaranty the stability of the learning algorithm.

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3

Dzhafarov, V., and T. Büyükköroğlu. "An Algorithm for Segment Stability." Mathematical and Computational Applications 8, no. 1 (April 1, 2003): 111–18. http://dx.doi.org/10.3390/mca8010111.

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4

Fan, Jiarui, Cheng Ai, Aofei Guo, Xiaojun Yan, and Jingang Wang. "Evaluation of Electric Field Integral Voltage Measurement Method of Transmission Line Based on Error Transmission and Uncertainty Analysis." Sensors 21, no. 13 (June 25, 2021): 4340. http://dx.doi.org/10.3390/s21134340.

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Electric field numerical integration algorithms can realize the non-contact measurement of transmission line voltage effectively. Although there are many electric field numerical integration algorithms, lack of a comprehensive comparison of accuracy and stability among various algorithms results in difficulties in evaluating the measurement results of various algorithms. Therefore, this paper presents the G-L (Gauss–Legendre) algorithm, the I-G-L (improved Gauss–Legendre) algorithm, and the I-G-C (improved Gauss–Chebyshev) algorithm and proposes a unified error propagation model of the derived algorithms to assess the accuracy of each integration method by considering multiple error sources. Moreover, evaluation criteria for the uncertainty of transmission line voltage measurement are proposed to analyze the stability and reliability of these algorithms. A simulation model and experiment platform were then constructed to conduct error propagation and uncertainty analyses. The results show that the G-L algorithm had the highest accuracy and stability in the scheme with five integral nodes, for which the simulation error was 0.603% and the relative uncertainty was 2.130%. The I-G-L algorithm was more applicable due to the smaller number of integral nodes required, yet the algorithm was less stable in achieving the same accuracy as the G-L algorithm. In addition, the I-G-C algorithm was relatively less accurate and stable in voltage measurement.

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5

Mohsin Al-Wazni, Hazim Sadeq, and Shatha Suhbat Abdulla Al-Kubragyi. "A hybrid algorithm for voltage stability enhancement of distribution systems." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 50. http://dx.doi.org/10.11591/ijece.v12i1.pp50-61.

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This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.

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Hou, Guo Zhao, Jin Biao Wang, and Jing Wu. "MANET-Based Stable Clustering Algorithm and its Performance Analysis." Applied Mechanics and Materials 571-572 (June 2014): 100–104. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.100.

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In MANET, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a MANET-based stable clustering algorithm (MSCA) was proposed. Firstly, MSCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that MSCA can improve cluster stability and reduce clustering overheads.

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7

Ghadban, Ahmed Majeed, Ghassan Abdullah Salman, and Husham Idan Hussein. "Assessment of voltage stability based on power transfer stability index using computational intelligence models." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 2790. http://dx.doi.org/10.11591/ijece.v11i4.pp2790-2797.

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<span>In this paper, the importance of voltage stability is explained, which is a great problem in the EPS. The estimation of VS is made a priority so as to make the power system stable and prevent it from reaching voltage collapse. The power transfer stability index (PTSI) is used as a predictor utilized in a PSN to detect the instability of voltages on weakened buses. A PSI is used to obtain a voltage assessment of the PSNs. Two hybrid algorithms are developed. The (CA-NN) and the (PSO-NN). After developing algorithms, they are compared with the actual values of PTSI NR method. The algorithms installed on the 24 bus Iraqi PS. The actual values of PTSI are the targets needed. They are obtained from the NR algorithm when the input data is V<sub>i</sub>, δ<sub>i</sub>, P<sub>d</sub>, Q<sub>d</sub> for the algorithm. The results indicate that a weak bus that approaches voltage collapse and all results were approximately the same. There is a slight difference with the actual results and demonstrated classical methods are slower and less accurate than the hybrid algorithms. It also demonstrates the validation and effectiveness of algorithms (CA-NN, and PSO-NN) for assessing voltage-prioritizing </span><span>algorithms</span><span> (CA-NN). The MATLAB utilized to obtain most of the results.</span>

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8

RAKHLIN, ALEXANDER, SAYAN MUKHERJEE, and TOMASO POGGIO. "STABILITY RESULTS IN LEARNING THEORY." Analysis and Applications 03, no. 04 (October 2005): 397–417. http://dx.doi.org/10.1142/s0219530505000650.

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The problem of proving generalization bounds for the performance of learning algorithms can be formulated as a problem of bounding the bias and variance of estimators of the expected error. We show how various stability assumptions can be employed for this purpose. We provide a necessary and sufficient stability condition for bounding the bias and variance for the Empirical Risk Minimization algorithm, and various sufficient conditions for bounding bias and variance of estimators for general algorithms. We discuss settings in which it is possible to obtain exponential bounds, and we prove an extension of the bounded-difference inequality for "almost always" stable algorithms.

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9

Yi, Jian. "Research on enterprise financial economics early warning based on machine learning method." Journal of Computational Methods in Sciences and Engineering 22, no. 2 (March 28, 2022): 529–39. http://dx.doi.org/10.3233/jcm-215783.

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The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model.

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10

Gao, Wei, Yun Gang Zhang, and Li Liang. "Stability Analysis for Local Transductive Regression Algorithms." Advanced Materials Research 267 (June 2011): 438–43. http://dx.doi.org/10.4028/www.scientific.net/amr.267.438.

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In this paper, the stability of local transductive regression algorithms is studied by adopting a strategy which adjusts the sample set by removing one or two elements from it. A sufficient condition for uniform stability is given. The result of our work shows that if a local transductive regression algorithm uses square loss, and if for any x, a kernel function K(x, x) has a limited upper bound, then the local transductive regression algorithm which minimizes the standard form will have good uniform stability.

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11

Liu, Chang Hua, and Cao Yuan. "Stability of Primal-Dual Algorithm with Communication Delay for Congestion Control." Advanced Materials Research 219-220 (March 2011): 513–17. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.513.

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Stability is a crucial issue for transport protocols. A lot of works study how to tune the parameters of congestion control algorithms to keep the stability of networks. This paper uses the idea of re-engineering to design an adaptive-stability congestion control algorithms base on the optimization theories. Furthermore, we present one congestion control algorithm according to different stable conditions. The effectiveness of these stability criteria is validated by simulation results．

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12

Wu, Jing, and Guang Xue Meng. "A New Clustering Algorithm and Relevant Theoretical Analysis for Ad-Hoc Networks." Applied Mechanics and Materials 556-562 (May 2014): 4001–4. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4001.

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In ad-hoc networks, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a new clustering algorithm (NCA) was proposed. Firstly, NCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that NCA can improve cluster stability and reduce clustering overheads.

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13

Arifovic, Jasmina. "STABILITY OF EQUILIBRIA UNDER GENETIC-ALGORITHM ADAPTATION: AN ANALYSIS." Macroeconomic Dynamics 2, no. 1 (March 1998): 1–21. http://dx.doi.org/10.1017/s1365100598006014.

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The stability of stationary equilibria under genetic-algorithm adaptation is studied in an overlapping-generations economy with a unique, monetary steady-state and two-period cyclic equilibria. Agents use the genetic algorithm to update their labor-supply decisions. The results of simulations and the analysis of the dynamics show that the two-period cycle is stable and that the steady-state equilibrium is unstable under genetic-algorithm adaptation. The dynamics are driven primarily by the wealth and substitution effects that lead to the algorithm's convergence to the two-period cycle.

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14

CHANG, XIANGYU, ZONGBEN XU, BIN ZOU, and HAI ZHANG. "GENERALIZATION BOUNDS OF REGULARIZATION ALGORITHMS DERIVED SIMULTANEOUSLY THROUGH HYPOTHESIS SPACE COMPLEXITY, ALGORITHMIC STABILITY AND DATA QUALITY." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 04 (July 2011): 549–70. http://dx.doi.org/10.1142/s0219691311004213.

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A main issue in machine learning research is to analyze the generalization performance of a learning machine. Most classical results on the generalization performance of regularization algorithms are derived merely with the complexity of hypothesis space or the stability property of a learning algorithm. However, in practical applications, the performance of a learning algorithm is not actually affected only by an unitary factor just like the complexity of hypothesis space, stability of the algorithm and data quality. Therefore, in this paper, we develop a framework of evaluating the generalization performance of regularization algorithms combinatively in terms of hypothesis space complexity, algorithmic stability and data quality. We establish new bounds on the learning rate of regularization algorithms based on the measure of uniform stability and empirical covering number for general type of loss functions. As applications of the generic results, we evaluate the learning rates of support vector machines and regularization networks, and propose a new strategy for regularization parameter setting.

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15

Chen, K., J. Lu, X. Qiu, and B. Xu. "Stability improvement of relaxed FAP algorithm." Electronics Letters 43, no. 20 (2007): 1119. http://dx.doi.org/10.1049/el:20071938.

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16

Chang, Shuenn-Yih. "Explicit Pseudodynamic Algorithm with Unconditional Stability." Journal of Engineering Mechanics 128, no. 9 (September 2002): 935–47. http://dx.doi.org/10.1061/(asce)0733-9399(2002)128:9(935).

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17

Bose, Tamal, and David A. Trautman. "Stability of the quantized LMS algorithm." Circuits, Systems, and Signal Processing 14, no. 5 (September 1995): 587–602. http://dx.doi.org/10.1007/bf01213956.

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18

Dyah Ratna Kusuma, M. S., Ardyono Priyadi, and B. P. Vita Lystianingrum. "Transient Stability Prediction Using algorithm Backpropagation." Journal of Physics: Conference Series 1201 (May 2019): 012051. http://dx.doi.org/10.1088/1742-6596/1201/1/012051.

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19

Balasubramanian, M. "The Isomap Algorithm and Topological Stability." Science 295, no. 5552 (January 4, 2002): 7a—7. http://dx.doi.org/10.1126/science.295.5552.7a.

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20

Buslaeva, L. T. "Stability of a routing optimization algorithm." Cybernetics and Systems Analysis 33, no. 5 (September 1997): 754–56. http://dx.doi.org/10.1007/bf02667202.

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21

FORTUNE, STEVEN. "NUMERICAL STABILITY OF ALGORITHMS FOR 2D DELAUNAY TRIANGULATIONS." International Journal of Computational Geometry & Applications 05, no. 01n02 (March 1995): 193–213. http://dx.doi.org/10.1142/s0218195995000118.

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We consider the correctness of 2-d Delaunay triangulation algorithms implemented using floating-point arithmetic. The α-pseudocircle through points a, b, c consists of three circular arcs connecting ab, bc, and ac, each arc inside the circumcircle of a, b, c and forming angle α with the circumcircle; a triangulation is α-empty if the α-pseudocircle through the vertices of each triangle is empty. We show that a simple Delaunay triangulation algorithm—the flipping algorithm—can be implemented to produce O(n∈)-empty triangulations, where n is the number of point sites and ∈ is the relative error of floating-point arithmetic; its worst-case running time is O(n2). We also discuss floating-point implementation of other 2-d Delaunay triangulation algorithms.

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22

Sun, Mao Sheng, and Jun Wu. "A Stability Analysis of Randomized Scheduling Algorithm for Input-Queued Switches." Applied Mechanics and Materials 20-23 (January 2010): 1439–45. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.1439.

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Input-Queued switching fabrics are widely used in high speed network switches for their scalability. However, a scheduling algorithm must be elaborative designed for input-queued switches to get high throughput. The randomized algorithms are attractive in kinds of scheduling algorithms for their low complexity and well performance. In this paper, a fluid model is introduced to analyze the rate stability of switching systems with randomized schedulers. By this method, we proved that the randomized scheduling algorithm can achieve 100% throughput under any admissible traffics which satisfy the strong law of large numbers (SLLN).

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23

Ofem, Austine Efut, Unwana Effiong Udofia, and Donatus Ikechi Igbokwe. "A ROBUST ITERATIVE APPROACH FOR SOLVING NONLINEAR VOLTERRA DELAY INTEGRO–DIFFERENTIAL EQUATIONS." Ural Mathematical Journal 7, no. 2 (December 30, 2021): 59. http://dx.doi.org/10.15826/umj.2021.2.005.

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This paper presents a new iterative algorithm for approximating the fixed points of multivalued generalized \(\alpha\)–nonexpansive mappings. We study the stability result of our new iterative algorithm for a larger concept of stability known as weak \(w^2\)–stability. Weak and strong convergence results of the proposed iterative algorithm are also established. Furthermore, we show numerically that our new iterative algorithm outperforms several known iterative algorithms for multivalued generalized \(\alpha\)–nonexpansive mappings. Again, as an application, we use our proposed iterative algorithm to find the solution of nonlinear Volterra delay integro-differential equations. Finally, we provide an illustrative example to validate the mild conditions used in the result of the application part of this study. Our results improve, generalize and unify several results in the existing literature.

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Zhao, Haining, Hongbin Jing, Zhengbao Fang, and Hongwei Yu. "Flash Calculation Using Successive Substitution Accelerated by the General Dominant Eigenvalue Method in Reduced-Variable Space: Comparison and New Insights." SPE Journal 25, no. 06 (July 20, 2020): 3332–48. http://dx.doi.org/10.2118/202472-pa.

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Summary On the basis of a previously published reduced-variables method, we demonstrate that using these reduced variables can substantially accelerate the conventional successive-substitution iterations in solving two-phase flash (TPF) problems. By applying the general dominant eigenvalue method (GDEM) to the successive-substitution iterations in terms of the reduced variables, we obtained a highly efficient solution for the TPF problem. We refer to this solution as Reduced-GDEM. The Reduced-GDEM algorithm is then extensively compared with more than 10 linear-acceleration and Newton-Raphson (NR)-type algorithms. The initial equilibrium ratio for flash calculation is generated from reliable phase-stability analysis (PSA). We propose a series of indicators to interpret the PSA results. Two new insights were obtained from the speed comparison among various algorithms and the PSA. First, the speed and robustness of the Reduced-GDEM algorithm are of the same level as that of the reduced-variables NR flash algorithm, which has previously been proved to be the fastest flash algorithm. Second, two-side phase-stability-analysis results indicate that the conventional successive-substitution phase-stability algorithm is time consuming (but robust) at pressures and temperatures near the stability-test limit locus in the single-phase region and near the spinodal in the two-phase region.

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25

Zhang, Ya, Tong Li, Zhen Li, Yu-Mei Wu, and Hong Miao. "Software Defects Prediction Based on Hybrid Beetle Antennae Search Algorithm and Artificial Bee Colony Algorithm with Comparison." Axioms 11, no. 7 (June 24, 2022): 305. http://dx.doi.org/10.3390/axioms11070305.

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Software defects are problems in software that destroy normal operation ability and reflect the quality of the software. Software fault can be predicted by the software reliability model. In this paper, the hybrid algorithm is applied to parameter estimation in software defect prediction. As a biological heuristic algorithm, BAS (Beetle Antennae Search Algorithm) has fast convergence speed and is easy to implement. ABC (Artificial Bee Colony Algorithm) is better in optimization and has strong robustness. In this paper, the BAS-ABC hybrid algorithm is proposed by mixing the two algorithms and the goal of the proposed method is to improve the convergence and stability of the algorithm. Five datasets were used to carry out the experiments, and the data results showed that the hybrid algorithm was more accurate than the single algorithm, with stronger convergence and stability, so it was more suitable for parameter estimation of the software reliability model. Meanwhile, this paper implemented the comparison between hybrid BAS + ABC and hybrid PSO + SSA, and the result shows that BAS + ABC has better performance both in convergence and stability. The comparison result shows the strong ability in estimation and prediction of software defects hybrid BAS and ABC.

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Zhang, Jiahui, Xiao Wang, Mingchi Ju, Tailin Han, and Yingzhi Wang. "An Improved Sparsity Adaptive Matching Pursuit Algorithm and Its Application in Shock Wave Testing." Mathematical Problems in Engineering 2021 (July 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/6615584.

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In the compressed sensing (CS) reconstruction algorithms, the problems of overestimation and large redundancy of candidate atoms will affect the reconstruction accuracy and probability of the algorithm when using Sparsity Adaptive Matching Pursuit (SAMP) algorithm. In this paper, we propose an improved SAMP algorithm based on a double threshold, candidate set reduction, and adaptive backtracking methods. The algorithm uses the double threshold variable step-size method to improve the accuracy of sparsity judgment and reduces the undetermined atomic candidate set in the small step stage to enhance the stability. At the same time, the sparsity estimation accuracy can be improved by combining with the backtracking method. We use a Gaussian sparse signal and a measured shock wave signal of the 15psi range sensor to verify the algorithm performance. The experimental results show that, compared with other iterative greedy algorithms, the overall stability of the DBCSAMP algorithm is the strongest. Compared with the SAMP algorithm, the estimated sparsity of the DBCSAMP algorithm is more accurate, and the reconstruction accuracy and operational efficiency of the DBCSAMP algorithm are greatly improved.

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27

Yang, Zhanshan, and Xilan Liu. "Roundoff Error Analysis of an Algorithm Based on Householder Bidiagonalization for Total Least Squares Problems." Mathematics 9, no. 20 (October 12, 2021): 2550. http://dx.doi.org/10.3390/math9202550.

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For large-scale problems, how to establish an algorithm with high accuracy and stability is particularly important. In this paper, the Householder bidiagonalization total least squares (HBITLS) algorithm and nonlinear iterative partial least squares for total least squares (NIPALS-TLS) algorithm were established, by which the same approximate TLS solutions was obtained. In addition, the propagation of the roundoff error for the process of the HBITLS algorithm was analyzed, and the mixed forward-backward stability of these two algorithms was proved. Furthermore, an upper bound of roundoff error was derived, which presents a more detailed and clearer approximation of the computed solution.

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Li, Weina, and Jiadong Ren. "Distributed Frequent Interactive Pattern-Based Complex Software Group Network Stability Measurement." International Journal of Software Engineering and Knowledge Engineering 28, no. 05 (May 2018): 619–41. http://dx.doi.org/10.1142/s0218194018500171.

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Interactive software can run not only independently but also often collaboratively to perform tasks thus forming a larger group of software networks. Hence the analysis of interactions is essential as a way to measure the stability of the entire software group network, i.e. the interactive patterns and frequency. However, current studies rarely investigate the performance of software as groups but as individuals thus omitting their interactions. Especially, the performance of some traditional measurement algorithms which execute in nondistributed runtime environments is poor. In this paper, we proposed a new software group stability model concentrating on software network level behaviors as a group. An algorithm is proposed to extract key nodes and critical interactive items based on frequent interaction pattern, then the stability of software group can be assessed based on the loss of connectivity caused by removing key nodes and key edges from the network, using the algorithm SG-StaMea. Furthermore, our algorithms can quantify the stability. To validate the efficacy of our model, the Spark and Hadoop platforms have been selected as targets systems. Both experiments and experimental data showed that our algorithms have significantly improved the accuracy of software stability measurement compared to classical algorithm such as Apriori of frequent pattern.

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Haghshenas, Sina Shaffiee, Sami Shaffiee Haghshenas, Zong Woo Geem, Tae-Hyung Kim, Reza Mikaeil, Luigi Pugliese, and Antonello Troncone. "Application of Harmony Search Algorithm to Slope Stability Analysis." Land 10, no. 11 (November 15, 2021): 1250. http://dx.doi.org/10.3390/land10111250.

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Slope stability analysis is undoubtedly one of the most complex problems in geotechnical engineering and its study plays a paramount role in mitigating the risk associated with the occurrence of a landslide. This problem is commonly tackled by using limit equilibrium methods or advanced numerical techniques to assess the slope safety factor or, sometimes, even the displacement field of the slope. In this study, as an alternative approach, an attempt to assess the stability condition of homogeneous slopes was made using a machine learning (ML) technique. Specifically, a meta-heuristic algorithm (Harmony Search (HS) algorithm) and K-means algorithm were employed to perform a clustering analysis by considering two different classes, depending on whether a slope was unstable or stable. To achieve the purpose of this study, a database made up of 19 case studies with 6 model inputs including unit weight, intercept cohesion, angle of shearing resistance, slope angle, slope height and pore pressure ratio and one output (i.e., the slope safety factor) was established. Referring to this database, 17 out of 19 slopes were categorized correctly. Moreover, the obtained results showed that, referring to the considered database, the intercept cohesion was the most significant parameter in defining the class of each slope, whereas the unit weight had the smallest influence. Finally, the obtained results showed that the Harmony Search algorithm is an efficient approach for training K-means algorithms.

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Bellaire, Sascha, Christine Pielmeier, Martin Schneebeli, and Jürg Schweizer. "Stability algorithm for snow micro-penetrometer measurements." Journal of Glaciology 55, no. 193 (2009): 805–13. http://dx.doi.org/10.3189/002214309790152582.

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AbstractInformation on snow-cover stability is important for predicting avalanche danger. Traditionally, stability evaluation is based on manual observations of snow stratigraphy and stability tests, which are time-consuming. The SnowMicroPen (SMP) is a high-resolution, constant-speed penetrometer to measure penetration resistance. We have analysed the resistance signal to derive snow stability. The proposed stability algorithm was developed by comparing 68 SMP force–distance profiles with the corresponding manual profiles, including stability tests. The algorithm identifies a set of four potentially weak layers by taking into account changes in structure and rupture strength of microstructural elements that make up snow layers as derived from the SMP signal. In 90% of the cases, one of the four potentially weak layers proposed by the algorithm coincided with the failure layer observed in the stability test. To select the critical layer from the four potential weaknesses was more difficult. With fully automatic picking of the critical layer, agreement with the failure layer observed in the stability test was reached in 60% of the cases. To derive a stability classification, we analysed weak-layer as well as slab properties. These predictor variables allow the SMP signal to be classified into two stability classes, poor and fair-to-good, with an accuracy of ∼75% when compared with observed stability. The SMP, in combination with the proposed algorithm, shows high potential for providing snow-cover stability information at high resolution in time and space.

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31

Lenin, K. "AMENDED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REAL POWER LOSS REDUCTION AND STATIC VOLTAGE STABILITY MARGIN INDEX ENHANCEMENT." International Journal of Research -GRANTHAALAYAH 6, no. 2 (February 28, 2018): 146–56. http://dx.doi.org/10.29121/granthaalayah.v6.i2.2018.1555.

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In this paper, Amended Particle Swarm Optimization Algorithm (APSOA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) for solving the optimal reactive power dispatch Problem. PSO is one of the most widely used evolutionary algorithms in hybrid methods due to its simplicity, convergence speed, an ability of searching Global optimum. GSA has many advantages such as, adaptive learning rate, memory-less algorithm and, good and fast convergence. Proposed hybridized algorithm is aimed at reduce the probability of trapping in local optimum. In order to assess the efficiency of proposed algorithm, it has been tested on Standard IEEE 30 system and compared to other standard algorithms. The simulation results demonstrate worthy performance of the Amended Particle Swarm Optimization Algorithm (APSOA) in solving optimal reactive power dispatch problem.

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32

Tang, Liang, Shuhua Ma, Xianchun Ma, and Hairong You. "Research on Image Matching of Improved SIFT Algorithm Based on Stability Factor and Feature Descriptor Simplification." Applied Sciences 12, no. 17 (August 24, 2022): 8448. http://dx.doi.org/10.3390/app12178448.

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In view of the problems of long matching time and the high-dimension and high-matching rate errors of traditional scale-invariant feature transformation (SIFT) feature descriptors, this paper proposes an improved SIFT algorithm with an added stability factor for image feature matching. First of all, the stability factor was increased during construction of the scale space to eliminate matching points of unstable points, speed up image processing and reduce the dimension and the amount of calculation. Finally, the algorithm was experimentally verified and showed excellent results in experiments on two data sets. Compared to other algorithms, the results showed that the algorithm proposed in this paper improved SIFT algorithm efficiency, shortened image-processing time, and reduced algorithm error.

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Karimi, Abbas, Abbas Afsharfarnia, Faraneh Zarafshan, and S. A. R. Al-Haddad. "A Novel Clustering Algorithm for Mobile Ad Hoc Networks Based on Determination of Virtual Links’ Weight to Increase Network Stability." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/432952.

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The stability of clusters is a serious issue in mobile ad hoc networks. Low stability of clusters may lead to rapid failure of clusters, high energy consumption for reclustering, and decrease in the overall network stability in mobile ad hoc network. In order to improve the stability of clusters, weight-based clustering algorithms are utilized. However, these algorithms only use limited features of the nodes. Thus, they decrease the weight accuracy in determining node’s competency and lead to incorrect selection of cluster heads. A new weight-based algorithm presented in this paper not only determines node’s weight using its own features, but also considers the direct effect of feature of adjacent nodes. It determines the weight of virtual links between nodes and the effect of the weights on determining node’s final weight. By using this strategy, the highest weight is assigned to the best choices for being the cluster heads and the accuracy of nodes selection increases. The performance of new algorithm is analyzed by using computer simulation. The results show that produced clusters have longer lifetime and higher stability. Mathematical simulation shows that this algorithm has high availability in case of failure.

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Quach, Hong-Nam, Hyeonjun Jo, Sungwoong Yeom, and Kyungbaek Kim. "Link Stability aware Reinforcement Learning based Network Path Planning." Korean Institute of Smart Media 11, no. 5 (June 30, 2022): 82–90. http://dx.doi.org/10.30693/smj.2022.11.5.82.

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Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

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Ma, Yan, Yan-ling Hao, and Feng-min Tian. "A Novel Model of Conforming Delaunay Triangulation for Sensor Network Configuration." Abstract and Applied Analysis 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/720249.

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Delaunay refinement is a technique for generating unstructured meshes of triangles for sensor network configuration engineering practice. A new method for solving Delaunay triangulation problem is proposed in this paper, which is called endpoint triangle’s circumcircle model (ETCM). As compared with the original fractional node refinement algorithms, the proposed algorithm can get well refinement stability with least time cost. Simulations are performed under five aspects including refinement stability, the number of additional nodes, time cost, mesh quality after intruding additional nodes, and the aspect ratio improved by single additional node. All experimental results show the advantages of the proposed algorithm as compared with the existing algorithms and confirm the algorithm analysis sufficiently.

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Peibo, Duan, Zhang Changsheng, and Zhang Bin. "A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/734975.

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This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time.

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Nguyen, Au Ngoc, Anh Huy Nguyen, and Binh Thi Thanh Phan. "Feature subset selection in dynamic stability assessment power system using artificial neural networks." Science and Technology Development Journal 18, no. 2 (June 30, 2015): 15–24. http://dx.doi.org/10.32508/stdj.v18i2.1054.

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This paper presents method of feature subset selection in dynamic stability assessment (DSA) power system using artificial neural networks (ANN). In the application of ANN on DSA power system, feature subset selection aims to reduce the number of training features, cost and memory computer. However, the major challenge is to reduce the number of features but classification rate gets a high accuracy. This paper proposes applying Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS) and Feature Ranking (FR) algorithm to feature subset selection. The effectiveness of the algorithms was tested on the GSO-37bus power system. With the same number of features, the calculation results show that SFS algorithm yielded higher classification rate than FR, SBS algorithm. SFS algorithm yielded the same classification rate as SFFS algorithm.

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Gao, Hongxing, Guoxi Liang, and Huiling Chen. "Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction." Electronics 11, no. 2 (January 10, 2022): 209. http://dx.doi.org/10.3390/electronics11020209.

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In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

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ElDahshan, Kamal, Afaf Abd, and Nermeen Ghazy. "Achieving Stability in the Round Robin Algorithm." International Journal of Computer Applications 172, no. 6 (August 17, 2017): 15–20. http://dx.doi.org/10.5120/ijca2017915161.

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Usluogullari, Omer Faruk. "A novel algorithm for slope stability analysis." Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 169, no. 1 (February 2016): 3–14. http://dx.doi.org/10.1680/jgeen.15.00071.

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QURESHI, S., and M. S. CHANDIO. "Absolute Stability for a Fractional Numerical Algorithm." SINDH UNIVERSITY RESEARCH JOURNAL -SCIENCE SERIES 49, no. 003 (September 22, 2017): 655——658. http://dx.doi.org/10.26692/surj/2017.09.34.

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42

刘, 阳. "Calculus Algorithm for Stability of Irregular Slopes." Mine Engineering 09, no. 03 (2021): 180–85. http://dx.doi.org/10.12677/me.2021.93027.

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Xue, Tian Liang, Fang Zong Wang, and Jing Ye. "Parallel Algorithm for Transient Stability Online Simulation." Advanced Materials Research 433-440 (January 2012): 4446–51. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4446.

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Parallel computation is an effective approach to real-time simulation and transient stability online assessment of large-scale power systems. In this paper, the s-stage 2s-order symplectic Runge-Kutta-Nyström method is adopted for transient stability simulation of power system using classic model. Using Butcher transformation, a new parallel algorithm has been derived. The proposed algorithm has the convergence characteristic of a Newton type method and is of fully parallel-in-time. Through numerical simulation where the IEEE 145-bus power system is used, the proposed algorithm has been tested and compared with the conventional parallel-in-time Newton approach using implicit trapezoidal rule.

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Utkin, Vadim. "Stability and Convergence Rate of Twisting Algorithm." IFAC Proceedings Volumes 43, no. 14 (September 2010): 1267–71. http://dx.doi.org/10.3182/20100901-3-it-2016.00198.

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MOTSCHA, MATTHIAS. "Algorithm to compute the complex stability radius." International Journal of Control 48, no. 6 (December 1988): 2417–28. http://dx.doi.org/10.1080/00207178808906337.

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Morris, Nicholas F. "Use of Lanczos Algorithm in Elastic Stability." Journal of Structural Engineering 116, no. 7 (July 1990): 2049–54. http://dx.doi.org/10.1061/(asce)0733-9445(1990)116:7(2049).

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Chang, Shuenn-Yih. "Explicit Pseudodynamic Algorithm with Improved Stability Properties." Journal of Engineering Mechanics 136, no. 5 (May 2010): 599–612. http://dx.doi.org/10.1061/(asce)em.1943-7889.99.

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Ke, Xi-zheng, Xiao-hui Li, Zhi-ying Liu, and Fang-lin Deng. "Stability analysis of a time scale algorithm." Chinese Astronomy and Astrophysics 26, no. 2 (April 2002): 245–53. http://dx.doi.org/10.1016/s0275-1062(02)00063-2.

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Dabney, John, and Brian C. Dean. "An Efficient Algorithm for Batch Stability Testing." Algorithmica 58, no. 1 (May 8, 2009): 52–58. http://dx.doi.org/10.1007/s00453-009-9320-5.

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Bigelow, Stephen, and David Penneys. "Principal graph stability and the jellyfish algorithm." Mathematische Annalen 358, no. 1-2 (June 6, 2013): 1–24. http://dx.doi.org/10.1007/s00208-013-0941-2.

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