Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO

Variable neighbourhood search for the minimum labelling Steiner tree problem

We present a study on heuristic solution approaches to the minimum labelling Steiner tree problem, an NP-hard graph problem related to the minimum labelling spanning tree problem. Given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes of the graph, whose edges have the smallest number of distinct labels. Such a model may be used to represent many real world problems in telecommunications and multimodal transportation networks. Several metaheuristics are proposed and evaluated. The approaches are compared to the widely adopted Pilot Method and it is shown that the Variable Neighbourhood Search metaheuristic is the most effective approach to the problem, obtaining high quality solutions in short computational running times

Técnicas heurísticas para instâncias de grande porte do problema cabo-trincheira

Orientadores: Flávio Keidi Miyazawa, Eduardo Candido XavierDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O problema cabo trincheira foi apresentado em 2002 para modelar redes cabeadas. Esse problema pode ser visto como a união do problema de caminhos mínimos com o problema da árvore geradora mínima. Como entrada do problema temos um grafo $G=(V,E)$ com pesos nas arestas que indicam a distância entre os vértices incidentes na mesma. Há um vértice especial que representa uma instalação e demais vértices representam clientes. Uma solução para o problema é uma árvore geradora enraizada na instalação. O custo da solução é o custo da árvore geradora multiplicado por um fator de custo de trincheira mais os custos de cabos. Para cada cliente, o seu custo de cabo é dado pelo custo do caminho do cliente até a instalação multiplicado por um fator de custo de cabo. Esse problema modela cenários onde cada cliente deve ser conectado a uma instalação central através de um cabo dedicado. Cada cabo deve estar acomodado em uma trincheira e cada trincheira pode conter um número ilimitado de cabos. Sabendo que o custo dos cabos e trincheiras é proporcional a seu comprimento multiplicado por um fator de custo, o problema é encontrar uma rede com custo mínimo. Trabalhos anteriores  utilizaram o problema cabo trincheira para modelar problemas em telecomunicações, distribuição de energia, redes ferroviárias e até para reconstrução de vasos sanguíneos em exames de tomografia computadorizada. O trabalho foca na resolução do problema em instâncias de grande porte (superiores a 10 mil vértices). Foram desenvolvidas várias heurísticas para o problema. Na busca por simplificações de instâncias, foram demonstradas regras seguras, ou seja, que não comprometem nenhuma solução ótima, e heurísticas para a remoção de arestas eliminando aquelas que dificilmente estariam em ''boas soluções" de uma instância. Foi apresentado um algoritmo rápido para busca local capaz de ser executado mesmo em instâncias de grande porte. Foram desenvolvidos também algoritmos baseados em Greedy Randomized Adaptive Search Procedure (GRASP) e formulada uma heurística que contrai vértices. Com a contração de vértices, foram criadas instâncias do problema Cabo Trincheira com Demandas nos Vértices (CTDV). Essa versão com demandas tem um número menor de vértices que o problema original, o que viabiliza o uso de algoritmos baseados em programação linear para resolvê-lo. Foi demonstrado como é possível, ao resolver essa versão reduzida com demandas, remontar uma solução viável para o problema cabo trincheira original. Foram obtidos, com essas heurísticas, resultados melhores do que trabalhos anteriores encontrados na literatura do problema. Para além disso, foi demonstrado como essa técnica de contração de vértices tem o potencial para resolver instâncias de tamanhos ainda maior para o problema cabo trincheiraAbstract: The Cable Trench Problem (CTP) was presented in 2002 to model wired networks. This problem can be seen as the combination of the shortest path problem with the minimum spanning tree problem. An instance of the problem is composed by a graph $G=(V, E)$ with weigths, representing the distance between a pair of vertices. A special vertex represents a facility, and all others are clients. A solution to the problem is a spanning tree rooted in the facility. The solution's cost is given by the spanning tree cost multiplied by a trench cost factor, added by the cables cost reaching the root from each vertex in the graph. For each client, its cable cost is given by the path in the spanning tree, from the client to the root, multiplied by a cable cost factor. The CTP models a scenario where each client must be connected through a dedicated cable to a central facility. Each cable must be laying on a trench and a trench may hold an unlimited number of cables. Knowing that the cost of cables and trenches are proportional to its lengths multiplied by a cost factor, the problem is to find a network of minimum cost. Previous works in the literature used the CTP to model telecommunication problems, power distribution, rail networks, and even a blood vessel networks for computed tomography exams. In this research, we focused on large-scale instances of the problem (above 10 thousand vertices), achieving better results than previous works found in the literature. We developed a series of heuristics for the problem. Searching for a simplification for those instances, we present safe reductions, that do not affect any optimal solution, and heuristic reduction rules that are capable of removing edges unlikely to be part of ''good'' solutions in an instance. We present a fast local search algorithm, capable of improving even solutions for large-scale instances. We developed an algorithm based on a Greedy Randomized Adaptive Search Procedure (GRASP) and formulated a heuristic to cluster vertices. By clustering vertices, we represent a CTP instance as an instance of the Cable Trench Problem with Demands (CTPD). We represent the large-scale CTP instance into a vertex-wise smaller one adding demands to its vertices. Dealing with smaller instances, we enable a new range of techniques such as linear programming based algorithms to solve it. We demonstrate how this instances with demands can be used to build a viable solution for the original CTP instance. We also demonstrate how this vertex clustering technique has the potential to solve even larger scale instances for the CTPMestradoCiência da ComputaçãoMestre em Ciência da Computação133323/2018-8, 131175/2017-3CNP

Cross-layer modeling and optimization of next-generation internet networks

Scaling traditional telecommunication networks so that they are able to cope with the volume of future traffic demands and the stringent European Commission (EC) regulations on emissions would entail unaffordable investments. For this very reason, the design of an innovative ultra-high bandwidth power-efficient network architecture is nowadays a bold topic within the research community. So far, the independent evolution of network layers has resulted in isolated, and hence, far-from-optimal contributions, which have eventually led to the issues today's networks are facing such as inefficient energy strategy, limited network scalability and flexibility, reduced network manageability and increased overall network and customer services costs. Consequently, there is currently large consensus among network operators and the research community that cross-layer interaction and coordination is fundamental for the proper architectural design of next-generation Internet networks. This thesis actively contributes to the this goal by addressing the modeling, optimization and performance analysis of a set of potential technologies to be deployed in future cross-layer network architectures. By applying a transversal design approach (i.e., joint consideration of several network layers), we aim for achieving the maximization of the integration of the different network layers involved in each specific problem. To this end, Part I provides a comprehensive evaluation of optical transport networks (OTNs) based on layer 2 (L2) sub-wavelength switching (SWS) technologies, also taking into consideration the impact of physical layer impairments (PLIs) (L0 phenomena). Indeed, the recent and relevant advances in optical technologies have dramatically increased the impact that PLIs have on the optical signal quality, particularly in the context of SWS networks. Then, in Part II of the thesis, we present a set of case studies where it is shown that the application of operations research (OR) methodologies in the desing/planning stage of future cross-layer Internet network architectures leads to the successful joint optimization of key network performance indicators (KPIs) such as cost (i.e., CAPEX/OPEX), resources usage and energy consumption. OR can definitely play an important role by allowing network designers/architects to obtain good near-optimal solutions to real-sized problems within practical running times

Satellite downlink scheduling problem: A case study

The synthetic aperture radar (SAR) technology enables satellites to efficiently acquire high quality images of the Earth surface. This generates significant communication traffic from the satellite to the ground stations, and, thus, image downlinking often becomes the bottleneck in the efficiency of the whole system. In this paper we address the downlink scheduling problem for Canada's Earth observing SAR satellite, RADARSAT-2. Being an applied problem, downlink scheduling is characterised with a number of constraints that make it difficult not only to optimise the schedule but even to produce a feasible solution. We propose a fast schedule generation procedure that abstracts the problem specific constraints and provides a simple interface to optimisation algorithms. By comparing empirically several standard meta-heuristics applied to the problem, we select the most suitable one and show that it is clearly superior to the approach currently in use.Comment: 23 page