Solving Very Large Traveling Salesman Problems by SOM Parallelization on Cluster Architectures

Schabauer, Hannes; Schikuta, Erich; Weishäupl, Thomas

doi:10.1109/pdcat.2005.223

Cited by 19 publications

(6 citation statements)

References 3 publications

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“…This work also includes a brief overview of other partitioning methods used in combinatorial optimization (e.g. geometric, nearest neighbour, clustering) and also proposes a decomposition based on Kohonen's self organizing maps (SOMs) to solve the same problem using neural networks (a similar method has been used in Schabauer et al 2005 to solve the TSP). Here the similarities with our approach are more relevant, since once the problem has been partitioned into subproblems (represented by clusters of the graph representing it) these are not solved independently in parallel but according to a given ordering.…”

Section: Decomposition Techniques In Combinatorial Optimizationmentioning

confidence: 99%

A comparison of problem decomposition techniques for the FAP

Colombo

Allen

2009

J Heuristics

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This paper proposes a problem decomposition approach to solve hard Frequency Assignment Problem instances with standard meta-heuristics. The proposed technique aims to divide the initial problem into a number of easier subproblems, solve them and then recompose the partial solutions into one of the original problem. We consider the COST-259 MI-FAP instances and other Cardiff University test problems in order to simulate larger and more realistic networks. For both benchmarks the standard implementations of meta-heuristics do not generally produce a satisfactory performance within reasonable times of execution. However, the decomposed assignment approach can improve their results, both in terms of solution quality and runtime.

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Section: Decomposition Techniques In Combinatorial Optimizationmentioning

confidence: 99%

A comparison of problem decomposition techniques for the FAP

Colombo

Allen

2009

J Heuristics

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“…The primary advantage of this method is its transparency to architectural constraints; however, poor implementations may diminish speedups. The parallelism of neural networks using clusters and message-passing interface(MPI) also suffers from communication overheads and contention delays [15]. With the introduction of programmability, graphics processing units (GPU) have gained enough flexibility for use in non-graphic applications [16].…”

Section: Introductionmentioning

confidence: 99%

Use of High-performance Graphics Processing Units for Power System Demand Forecasting

He¹,

Meng²,

Dong³

et al. 2010

Journal of Electrical Engineering and Technology

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-Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

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“…However, poor implementation can diminish the speedup achieved. Parallelism of neural networks using clusters and MPI [10] also suffers from communication overheads and contention delay. With the introduction of programmability, the Graphics Processing Units (GPU) has gained enough flexibility to find use in non-graphics applications [11].…”

Section: Introductionmentioning

confidence: 99%

Accelerating Multi-layer Perceptron based short term demand forecasting using Graphics Processing Units

Dong

Meng

et al. 2009

2009 Transmission &Amp; Distribution Conference &Amp; Exposition: Asia and Pacific

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Load forecasting plays a vitally important role in the operation and planning of the power system in a deregulated electricity market. A large variety of methods have been proposed for load forecasting. In this paper, we introduce the Graphics Processing Units (GPU) based computing to accelerate the short term load forecasting with Multi-layer Perceptron (MLP). The proposed method is tested with the Queensland electricity market demand series. The result shows that the GPU based computing largely reduce the computational cost.Index Terms-GPU, Multi-Layer Perceptron, Neural Network, Load Forecasting.T. He is pursuing her PhD at the University of Queensland, Australia. Her research interest is in power system stability and computational methods application in power system analysis. Z.Y. Dong is with the Department of Electrical Engineering, Hong Kong Polytechnic University, Hong Kong. He held academic positions with the University of Queensland, Australia. He also worked with Powerlink Queensland and Transend Networks Tasmania, Australia. His research interest includes power system planning and stability, power market analysis and power system computation K. Meng is with the EE Dept, Hong Kong Polytechnic University. His research interests include power system stability and control, evolutionary computing, and power market analysis. H. Wang is a research fellow with the University of Queensland. Y.T. Oh is a Professor of Electrical Engineering with the KoreaUniversity of Technology and Education, Korea. His research interests include power system analysis and power quality research.

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Solving Very Large Traveling Salesman Problems by SOM Parallelization on Cluster Architectures

Cited by 19 publications

References 3 publications

A comparison of problem decomposition techniques for the FAP

A comparison of problem decomposition techniques for the FAP

Use of High-performance Graphics Processing Units for Power System Demand Forecasting

Accelerating Multi-layer Perceptron based short term demand forecasting using Graphics Processing Units

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