The Travelling Salesman Problem (TSP) has been extensively studied in the literature and various solvers are available. However, none of the state-of-the-art solvers for TSP outperforms the others in all problem instances within a given time limit. Therefore, the prediction of the best performing algorithm can save computational resources and optimise the results. In this paper, the TSP is studied in context of automated algorithm selection. Our aim is to identify the relevant features of problem instances and tackle this scenario as a machine learning task. We extend the set of existing features in the literature and propose several novel features to better characterise the problem. The contribution of the new features is statistically analysed and experiments show that adding our new features improves the prediction accuracy. We identified that our features based on kNN graph transformation are especially helpful.To create the training datasets, two state-of-the-art (meta-)heuristic algorithms are systematically evaluated on more than 2000 problems. Overall, we show that our prediction can be substantially more accurate than simple preference of an algorithm with the best performance for a majority of problem instances.
A condition of oil-paper insulating system is principal diagnostic indicator for failure-free operation of power transformers. This insulating system is thought to be one of the key components of the whole machine. During the operation of the transformer ageing of the insulating system appears, which may cause changes of its important electrical and mechanical properties. Many factors work simultaneously on the insulation during the operation and the particular effects influence each other as well. The material ageing is also affected by changes of operating properties. Ageing rate of organic components, which compose the insulating system, is mainly accelerated by temperature, humidity and oxygen presence. The diagnostic system, which is mainly applied during the shutdown of the transformer, contributes to the detection of the ageing level and to the judgement of residual working life. Also on-line diagnostic systems are currently more applied, especially for strategically significant devices or in cases, when appeared failure could lead to considerable economic waste.This contribution focuses on analysis of factors causing degradation of insulating system of power transformer as well as on sequent elaboration of programme application for on-line diagnostics of these transformers. Above-mentioned application has been programmed in LabVIEW graphics environment and provides actions connected with computer analysis, visualization and saving of measured data. The programme also starts alarm in case of overrunning of pre-set limit values and provides approximate estimation of residual working life of device. The main advantage of computing solution of devices consists in the possibility of flexible conformation of sensors and digital indications in accordance to current requirements of particular users.
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