• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.41
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• pp.177-188
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• 1997

This study describes the important information for establishing Human Computer Interface System for solving the large scale Traveling Saleman Problem in Printed Circuit Board production. Appropriate types and sizes of partitioning of large scale problems are discussed. Optimal tours for the special patterns appeared in PCB's are given. The comparision of optimal solutions of non-Euclidean problems and Euclidean problems shows the possibilities of using human interface in solving the Chebyshev TSP. Algorithm for the large scale problem using described information and coputational result of the practical problem are given.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.601-605
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• 1991

In this paper an approximate solution method for the large-scale Traveling Salesman Problem(TSP) is presented. The method start with the subdivision of the problem domain into a number of clusters by considering their geometries. The clusters have limited number of nodes so as to get local solutions. They are linked to give the least path which covers the whole domain and become TSPs with start- and end-node. The approximate local solutions in each cluster are obtained by using geometrical property of the cluster, and combined to give an overall-approximate solution for the large-scale TSP.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• Advances in Computational Design
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• v.8 no.2
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• pp.133-145
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• 2023

Scaling and similitude large scale structural member to small scale model is considered the most important matter for the experimental tests because of the difficulty in controlling, lack of capacities and expenses, furthermore that most of MSc and PhD students suffering from choosing the suitable specimen before starting their experimental study. The current study adopts to take large scale slab with opening as a case study of structural member where the slab is squared with central squared opening, the boundary condition is fixed from all sides, the load represents by four concentrated force in four corners of opening, as well as, the study adopts Buckingham theorem which has been used for scaling, all the parameters of the problem have been formed in dimensionless groups, the main groups have been connected by a relations, those relations are represented by force, maximum stress and maximum displacement. Finite element method by ANSYS R18.1 has been used for analyzing and forming relations for the large scale member. Prediction analysis has been computed for three small scale models by depending on the formed relations of the large scale member. It is found that Buckingham theorem is considered suitable way for creating relations among the parameters for any structural problem then making similitude and scaling the large scale members to small scale members. Finally, verification between the prediction and theoretical results has been done, it is observed that the maximum deviation between them is not more than 2.4%.

• Journal of Communications and Networks
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• v.11 no.2
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• pp.157-165
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• 2009

In this paper, we investigate a dynamic spectrum sharing problem for the centralized uplink cognitive radio networks using orthogonal frequency division multiple access. We formulate a large-scale joint rate and power allocation as an optimization problem under quality of service constraint for secondary users and interference constraint for primary users. We also suggest admission control to nd a feasible solution to the optimization problem. To implement the resource allocation on a large-scale, we introduce a notion of using the conservative factors $\alpha$ and $\beta$ depending on the outage and violation probabilities. Since estimating instantaneous channel gains is costly and requires high complexity, the proposed algorithm pursues a practical and implementation-friendly resource allocation. Simulation results demonstrate that the large-scale joint rate and power allocation incurs a slight loss in system throughput over the instantaneous one, but it achieves lower complexity with less sensitivity to variations in shadowing statistics.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.403-410
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• 1999

In this study, a parallel algorithm has been developed that can quickly solve the optiaml control problem of large-scale dynamic systems. The algorithm adopts the sequential quadratic programming methods and achieves domain decomposition-type parallelism in computing sensitivities for search direction computation. A silicon wafer thermal process problem has been solved using the algorithm, and a parallel efficiency of 45% has been achieved with 16 processors. Practical methods have also been investigated in this study as a way to further speed up the computation time.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.139-144
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• 1999

The present paper is concerned with the robust decentralized stabilization problem of large-scale systems with time delays in the interconnections using sliding mode control. Based on Lyapunov stability theorem and H$_{\infty}$ theory, an existence condition of the sliding mode and a robust decentralized sliding mode controller are newly derived for large-scale systems under mismatched uncertainties. Finally, a numerical example is given to verify the validity of the results developed in this paper.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.11
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• pp.503-509
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• 2002

In this paper, the robust non-fragile guaranteed cost control problem is studied for a class of linear large-scale systems with uncertainties and a given quadratic cost functions. The uncertainty in the system is assumed to be norm-bounded and time-varying. Also, the state-feedback gains for subsystems of the large-scale system are assumed to have norm-bounded controller gain variations. The problem is to design a state feedback control laws such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound for all admissible uncertainties and controller gain variations. Sufficient conditions for the existence of such controllers are derived based on the linear matrix inequality (LMI) approach combined with the Lyapunov method. A parameterized characterization of the robust non-fragile guaranteed cost controllers is given in terms of the feasible solutions to a certain LMI. A numerical example is given to illustrate the proposed method.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.61-76
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• 2002

The robust non-fragile guaranteed cost control problem is studied in this paper for class of uncertain linear large-scale systems with time-varying delays in subsystem interconnections and given quadratic cost functions. The uncertainty in the system is assumed to be norm-hounded arid time-varying. Also, the state-feedback gains for subsystems of the large-scale system are assumed to have norm-bounded controller gain variations. The problem is to design state feedback control laws such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound far all admissible uncertainties. Sufficient conditions for the existence of such controllers are derived based on the linear matrix inequality (LMI) approach combined with the Lyapunov method. A parameterized characterization of the robust non-fragile guaranteed cost contrellers is 7iven in terms of the feasible solution to a certain LMI. Finally, in order to show the application of the proposed method, a numerical example is included.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.155-160
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.