1,513 research outputs found

    A Tutorial on Clique Problems in Communications and Signal Processing

    Full text link
    Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and kk-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

    A Distribution Network Reconfiguration and Islanding Strategy

    Get PDF
    With the development of Smart Grid, the reliability and stability of the power system are significantly improved. However, a large-scale outage still possibly occurs when the power system is exposed to extreme conditions. Power system blackstart, the restoration after a complete or partial outage is a key issue needed to be studied for the safety of power system. Network reconfiguration is one of the most important steps when crews try to rapidly restore the network. Therefore, planning an optimal network reconfiguration scheme with the most efficient restoration target at the primary stage of system restoration is necessary and it also builds the foundation to the following restoration process. Besides, the utilization of distributed generators (DGs) has risen sharply in the power system and it plays a critical role in the future Smart Grid to modernize the power grid. The emerging Smart Grid technology, which enables self-sufficient power systems with DGs, provides further opportunities to enhance self-healing capability. The introduction of DGs makes a quick and efficient restoration of power system possible. In this thesis, based on the topological characteristics of scale-free networks and the Discrete Particle Swarm Optimization (DPSO) algorithm, a network reconfiguration scheme is proposed. A power system structure can be converted into a system consisting of nodes and edges. Indices that reflect the nodes’ and edges’ topological characteristics in Graph Theory can be utilized to describe the importance of loads and transmission lines in the power system. Therefore, indices like node importance degree, line betweenness centrality and clustering coefficient are introduced to weigh the importance of loads and transmission lines. Based on these indices, an objective function which aims to restore as many important loads and transmission lines as possible and also subjected to constraints is formulated. The effectiveness of potential reconfiguration scheme is verified by Depth First Search (DFS) algorithm. Finally, DPSO algorithm is employed to obtain the optimal reconfiguration scheme. The comprehensive reconfiguration scheme proposed by my thesis can be the theoretical basis for the power grid dispatchers. Besides, DGs are introduced in this thesis to enhance the restoration efficiency and success rate at the primary stage of network restoration. Firstly, the selection and classification principle of DGs are introduced in my thesis. In addition, the start sequence principle of DGs is presented as a foundation for the following stability analysis of network restoration with DGs. Then, the objective function subjected to constraints that aims to restore as many important loads as possible is formulated. Based on the restoration objective, islands that include part of important and restorable loads are formed because the DGs’ capacity cannot ensure an entire restoration of the outage areas. Finally, DPSO is used to obtain the optimal solution of islanding strategy and the state sequence matrix is utilized to represent the solution space. It is believed that this work will provide some useful insight into improving the power system resiliency in the face of extreme events such as natural or man-made disasters

    Modelling human network behaviour using simulation and optimization tools: the need for hybridization

    Get PDF
    The inclusion of stakeholder behaviour in Operations Research / Industrial Engineering (OR/IE) models has gained much attention in recent years. Behavioural and cognitive traits of people and groups have been integrated in simulation models (mainly through agent-based approaches) as well as in optimization algorithms. However, especially the influence of relations between different actors in human networks is a broad and interdisciplinary topic that has not yet been fully investigated. This paper analyses, from an OR/IE point of view, the existing literature on behaviour-related factors in human networks. This review covers different application fields, including: supply chain management, public policies in emergency situations, and Internet-based human networks. The review reveals that the methodological approach of choice (either simulation or optimization) is highly dependent on the application area. However, an integrated approach combining simulation and optimization is rarely used. Thus, the paper proposes the hybridization of simulation with optimization as one of the best strategies to incorporate human behaviour in human networks and the resulting uncertainty, randomness, and dynamism in related OR/IE models.Peer Reviewe

    Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

    Get PDF
    Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, “Bat-Cluster (BC).” This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values

    The development and application of metaheuristics for problems in graph theory: A computational study

    Get PDF
    This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.It is known that graph theoretic models have extensive application to real-life discrete optimization problems. Many of these models are NP-hard and, as a result, exact methods may be impractical for large scale problem instances. Consequently, there is a great interest in developing e±cient approximate methods that yield near-optimal solutions in acceptable computational times. A class of such methods, known as metaheuristics, have been proposed with success. This thesis considers some recently proposed NP-hard combinatorial optimization problems formulated on graphs. In particular, the min- imum labelling spanning tree problem, the minimum labelling Steiner tree problem, and the minimum quartet tree cost problem, are inves- tigated. Several metaheuristics are proposed for each problem, from classical approximation algorithms to novel approaches. A compre- hensive computational investigation in which the proposed methods are compared with other algorithms recommended in the literature is reported. The results show that the proposed metaheuristics outper- form the algorithms recommended in the literature, obtaining optimal or near-optimal solutions in short computational running times. In addition, a thorough analysis of the implementation of these methods provide insights for the implementation of metaheuristic strategies for other graph theoretic problems
    • …
    corecore