Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

Flexible design of urban water distribution systems

Urban water distribution systems (UWDS) are highly inter-connected and under many uncertainties from water demand, pipe roughness, and component failure. Accurate projections of these uncertainties are almost impossible, and thus it may not be a proper method to design the system to meet its performance criteria for the forecasted scenario. The system is designed for the deterministic not for the uncertainties, as a result it may not be efficient or effective to be operated under different future scenarios. Flexible design is shown as a useful strategy to cost-effectively respond to uncertainties because of its consideration of uncertainties in advance, and has been successfully applied in many engineering systems. The objective of flexible design is to identify flexibility sources in UWDS and embed them into the system design to respond to uncertainties. The thesis discussed different terms to define the property of the system to respond to uncertainties and proposed a definition of flexibility for UWDS. It then proposed different measures to indicate flexibility value and introduced an efficient method to handle numerous uncertain parameters in the model. It also develops an efficient method to identify high value flexibility sources based on the Flexibility Index. Finally the thesis presents a flexibility-based optimisation model that enable water engineers to compare different flexible design alternatives and generate optimal solutions. A definition of flexibility in UWDS is proposed to illustrate broadly its property to respond to uncertainties, since it is not so useful, or at least in this thesis to distinguish similar terms to define the property of the system to respond to uncertainties. Identified flexibility sources by the proposed method is not useful for the flexibility-based optimization model to design a system, but it might be a powerful tool to locate the weak points in the system or provide better update options during rehabilitation of the system. The computational efficiency of the proposed flexibility-based optimisation model was demonstrated by dramatic decreasing on the number of the required hydraulic simulation in the case study. Flexible designs in the case study are more expensive than inflexible design, but have better hydraulic performance under uncertainties

An adaptive minimum spanning tree multi-element method for uncertainty quantification of smooth and discontinuous responses

A novel approach for non-intrusive uncertainty propagation is proposed. Our approach overcomes the limitation of many traditional methods, such as generalised polynomial chaos methods, which may lack sufficient accuracy when the quantity of interest depends discontinuously on the input parameters. As a remedy we propose an adaptive sampling algorithm based on minimum spanning trees combined with a domain decomposition method based on support vector machines. The minimum spanning tree determines new sample locations based on both the probability density of the input parameters and the gradient in the quantity of interest. The support vector machine efficiently decomposes the random space in multiple elements, avoiding the appearance of Gibbs phenomena near discontinuities. On each element, local approximations are constructed by means of least orthogonal interpolation, in order to produce stable interpolation on the unstructured sample set. The resulting minimum spanning tree multi-element method does not require initial knowledge of the behaviour of the quantity of interest and automatically detects whether discontinuities are present. We present several numerical examples that demonstrate accuracy, efficiency and generality of the method.Comment: 20 pages, 18 figure

Exhaustive Search-based Model for Hybrid Sensor Network

A new model for a cluster of hybrid sensors network with multi sub-clusters is proposed. The model is in particular relevant to the early warning system in a large scale monitoring system in, for example, a nuclear power plant. It mainly addresses to a safety critical system which requires real-time processes with high accuracy. The mathematical model is based on the extended conventional search algorithm with certain interactions among the nearest neighborhood of sensors. It is argued that the model could realize a highly accurate decision support system with less number of parameters. A case of one dimensional interaction function is discussed, and a simple algorithm for the model is also given.Comment: 6 pages, Proceeding of the International Conference on Intelligent & Advanced Systems 2012 pp. 557-56

A Multilabel Approach for Fault Detection and Classification of Transmission Lines using Binary Relevance

In Contemporary automation systems, Fault detection and classification of electrical transmission lines in grid systems are given top priority. The broad application of Machine Learning (ML) methods has enabled the substitute of conventional methods of fault identification and classification. These methods are more effective ones that can identify faults early on using a significant quantity of sensory data. So detecting simultaneous failures is difficult in the context of distracting the noise and several faults in the transmission lines. This study contributes by offering a unique way for concurrently detecting and classifying several faults using a multilabel classification approach based on binary relevance classifiers. The proposed binary relevance multilabel detection and classification models’ performances are examined. Under both ideal and problematic circumstances, faults in the dataset are collected. A variety of multilabel fault types detection and classification determines the suggested method’s effectiveness