scholarly journals On the Stability of a Solution of a Guarantee Optimization Problem under a Functional Constraint on the Disturbance

2018 ◽  
Vol 51 (32) ◽  
pp. 228-233
Author(s):  
M.I. Gomoyunov ◽  
V.O. Karandina ◽  
I.P. Mezentsev ◽  
D.A. Serkov
Keyword(s):  
Optimization Problem ◽  
Functional Constraint ◽  
Stability Of A Solution ◽  
The Stability

scholarly journals Gas transportation system development in Mongolia under gas prices uncertainty

2021 ◽  
Vol 289 ◽  
pp. 04006
Author(s):  
Darya Maksakova
Keyword(s):  
Optimization Problem ◽  
System Development ◽  
Stable Solution ◽  
Transportation System ◽  
Decision Making Process ◽  
Import Prices ◽  
Stability Of A Solution ◽  
Stable Solutions ◽  
The Stability ◽  
Support Decision Making

The paper analyses the stability of a solution to a problem of gas transportation system development in terms of gas import prices. The object of the study is a future gas transportation system in Mongolia. The employed tools are based on an original optimization problem, which is aimed to support decision-making process when choosing capacity, location, and time for investments in gas infrastructure. Different scenarios of gas import prices are considered for Mongolia. A stable solution is defined as the solution that is included in the optimal plans for every scenario. A multi-criteria approach is proposed to expanding the area of stable solutions. In conclusion, the priority areas of gas transportation system development in Mongolia are highlighted.

scholarly journals Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2347
Author(s):  
Yanyan Wang ◽  
Lin Wang ◽  
Ruijuan Zheng ◽  
Xuhui Zhao ◽  
Muhua Liu
Keyword(s):  
Queue Length ◽  
Optimization Problem ◽  
Time Slot ◽  
Smart Homes ◽  
Back Pressure ◽  
Smart Devices ◽  
Smart Device ◽  
Simulation Results ◽  
Computational Offloading ◽  
The Stability

In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.

scholarly journals Optimal Location and Design of TCSC controller For Improvement of Stability

2011 ◽  
pp. 210-215
Author(s):  
Swathi Kommamuri ◽  
P. Sureshbabu
Keyword(s):  
Power System ◽  
Optimization Problem ◽  
Low Frequency ◽  
System Stability ◽  
Simulation Environment ◽  
Swarm Optimization ◽  
Stability Performance ◽  
Low Frequency Oscillations ◽  
Simulation Results ◽  
The Stability

Power system stability improvement by a coordinate Design ofThyristor Controlled Series Compensator (TCSC) controller is addressed in this paper.Particle Swarm Optimization (PSO) technique is employed for optimization of the parameterconstrained nonlinear optimization problem implemented in a simulation environment. The proposed controllers are tested on a weakly connected power system. The non-linear simulation results are presented. The eigenvalue analysis and simulation results show the effectiveness and robustness of proposed controllers to improve the stability performance of power system by efficient damping of low frequency oscillations under various disturbances.

scholarly journals Parameter Optimization of Droop Controllers for Microgrids in Islanded Mode by the SQP Method with Gradient Sampling

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Peijie Li ◽  
Ziyi Yang ◽  
Shuchen Huang ◽  
Jun Zhang
Keyword(s):  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Parameters Optimization ◽  
Stationary Points ◽  
Small Signal Stability ◽  
Signal Stability ◽  
Gradient Sampling ◽  
Nonsmooth Problem ◽  
Islanded Mode ◽  
The Stability

For enhancing the stability of the microgrid operation, this paper proposes an optimization model considering the small-signal stability constraint. Due to the nonsmooth property of the spectral abscissa function, the droop controller parameters’ optimization is a nonsmooth optimization problem. The Sequential Quadratic Programming with Gradient Sampling (SQP-GS) is implemented to optimize the droop controller parameters for solving the nonsmooth problem. The SQP-GS method can guarantee the solution of the optimization problem globally and efficiently converges to stationary points with probability of one. In the current iteration, the gradient of the nonsmooth function can be evaluated on a set of randomly generated nearby points by computing closed-form sensitivities. A test on the microgrid system shows that the optimality and the efficiency of the SQP-GS are better than those of the heuristic algorithms.

scholarly journals SELECTION OF THE PARAMETERS OF THE GENERALIZED DISPERSION ALGORITHM FOR MULTIVARIATE STATISTICAL CONTROL OF THE PROCESS SCATTERING

2021 ◽  
Vol 23 (1) ◽  
pp. 79-83
Author(s):  
A.V. Alekseeva ◽  
◽  
V.N. Klyachkin ◽  
Keyword(s):  
Performance Indicators ◽  
Optimization Problem ◽  
Unstable State ◽  
Statistical Control ◽  
Multivariate Statistical ◽  
Generalized Variance ◽  
Process Indicators ◽  
The Stability ◽  
Process Disturbances ◽  
Selection Of

To control the stability of the functioning of aviation equipment units based on the results of monitoring a group of indicators, methods of statistical processes control can be used. In the presence of significant correlations between performance indicators, multivariate methods are used. In this case, the control of the average level of the process is carried out on the basis of the Hotelling algorithm, the control of multivariate scattering is carried out using the generalized variance algorithm. If, according to the conditions of the process, it is necessary to ensure the fastest detection of a violation, then the optimization problem of finding such values of the sample size, sampling frequency and position of the control boundaries is solved that minimizes the average time of the unstable state of the process. The initial data are the number of process indicators monitored, the target value of the generalized variance (estimated from experimental data), the characteristic of the permissible increase in scattering, the intensity of process disturbances (parameter of the Poisson distribution); time to search for a violation after its detection and time to calculate the sample element.

On Solving the Multi-Objective Software Package Upgradability Problem

2018 ◽  
Vol 9 (2) ◽  
pp. 18-38
Author(s):  
Noureddine Aribi ◽  
Yahia Lebbah
Keyword(s):  
Open Source Software ◽  
Software Package ◽  
Optimization Problem ◽  
Mixed Integer ◽  
Software Systems ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Before And After ◽  
The Stability

Free and open source software (FOSS) distributions are increasingly based on the abstraction of packages to manage and accommodate new features before and after the deployment stage. However, due to inter-package dependencies, package upgrade entails challenging shortcomings of deployment and management of complex software systems, inhibiting their ability to cope with frequent upgrade failures. Moreover, the upgrade process may be achieved according to some criteria (maximize the stability, minimize outdated packages, etc.). This problem is actually a multi-objective optimization problem. Throughout the article, the authors propose a Leximax approach based on mixed integer linear programming (MILP) to tackle the upgradability problem, while ensuring efficiency and fairness requirements between the objective functions. Experiments performed on real-world instances, from the MANCOOSI project, show that the authors' approach efficiently finds solutions of consistently high quality.

MAXIMUM ENTROPY ALGORITHMS FOR UNCERTAINTY MEASURES

1993 ◽  
Vol 01 (01) ◽  
pp. 69-93 ◽  
Author(s):  
Y. MAEDA ◽  
H. T. NGUYEN ◽  
H. ICHIHASHI
Keyword(s):  
Decision Making ◽  
Maximum Entropy ◽  
Optimization Problem ◽  
Functional Constraint ◽  
Computational Algorithms ◽  
Probabilistic Information ◽  
Uncertainty Measures ◽  
Theory Of Evidence ◽  
Probabilistic Entropy

In decision-making problems when probabilistic information is incomplete (e.g. Nguyen and Walker [1]) as well as in measuring the non-specificity and conflict in the theory of evidence (e.g. Maeda and Ichihashi [2, 3]), one is led to consider the problem of maximizing probabilistic entropy under a functional constraint induced by the available evidence. This paper is devoted specifically to developing computational algorithms for this optimization problem.

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