An improved particle swarm algorithm for multi-objectives based optimization in MPLS/GMPLS networks

Particle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same time, Multi-Protocol Label Switched (MPLS) and its extended version Generalized MPLS, has become an emerging network technology for modern and diverse applications. Therefore, as per MPLS and Generalized MPLS MCOP needs, it is important to find the Pareto based optimal solutions that guarantee the optimal resource utilization without compromising the quality of services (QoS) within the networks. The paper proposes a novel version of PSO, which includes a modified version of the Elitist Learning Strategy (ELS) in PSO that not only solves the existing exploration problem in PSO, but also produces optimal solutions with efficient convergence rates for different MPLS/ GMPLS network scales. The proposed approach has also been applied with two objective functions; the resource provisioning and the traffic load balancing costs. Our simulations and comparative study showed improved results of the proposed algorithm over the well-known optimization algorithms such as the “standard” PSO, Adaptive PSO, BAT, and Dolphin algorithm

Multiple solutions based particle swarm optimization for cluster-head-selection in wireless-sensor-network

Wireless sensor network (WSN) has a significant role in wide range of scientific and industrial applications. In WSN, within the operation area of sensor nodes the nodes are randomly deployed. The constraint related to energy is considered as one of the major challenges for WSN, which may not only affect the sensor nodes efficiency but also influences the operational capabilities of the network. Therefore, numerous attempts of researches have been proposed to counter this energy problem in WSN. Hierarchical clustering approaches are popular techniques that offered the efficient consumption of the energy in WSN. In addition to this, it is understood that the optimum choice of sensor as cluster head can critically help to reduce the energy consumption of the sensor node. In recent years, metaheuristic optimization is used as a proposed technique for the optimal selection of cluster heads. Furthermore, it is noteworthy here that proposed techniques should be efficient enough to provide the optimal solution for the given problem. Therefore, in this regard, various attempts are made in the form of modified versions or new metaheuristic algorithms for optimization problems. The research in the paper offered a modified version of particle-swarm-optimization (PSO) for the optimal selection of sensor nodes as cluster heads. The performance of the suggested algorithm is experimented and compared with the renowned optimization techniques. The proposed approach produced better results in the form of residual energy, number of live nodes, sum of dead nodes, and convergence rate

A modified whale optimization algorithm for enhancing the features selection process in machine learning

In recent years, when there is an abundance of large datasets in various fields, the importance of feature selection problem has become critical for researchers. The real world applications rely on large datasets, which implies that datasets have hundreds of instances and attributes. Finding a better way of optimum feature selection could significantly improve the machine learning predictions. Recently, metaheuristics have gained momentous popularity for solving feature selection problem. Whale Optimization Algorithm has gained significant attention by the researcher community searching to solve the feature selection problem. However, the exploration problem in whale optimization algorithm still exists and remains to be researched as various parameters within the whale algorithm have been ignored and not introduced into machine learning models. This paper proposes a new and improved version of the whale algorithm entitled Modified Whale Optimization Algorithm (MWOA) that hybrid with the machine learning models such as logistic regression, decision tree, random forest, K-nearest neighbour, support vector machine, naïve Bayes model. To test this new approach and the performance, the breast cancer datasets were used for MWOA evaluation. The test results revealed the superiority of this model when compared to the results obtained by machine learning models

Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System

This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria; EspañaFil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: González, Begoña. Universidad de Las Palmas de Gran Canaria; EspañaFil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin

Particle swarm optimization for routing and wavelength assignment in next generation WDM networks.

PhDAll-optical Wave Division Multiplexed (WDM) networking is a promising technology for long-haul backbone and large metropolitan optical networks in order to meet the non-diminishing bandwidth demands of future applications and services. Examples could include archival and recovery of data to/from Storage Area Networks (i.e. for banks), High bandwidth medical imaging (for remote operations), High Definition (HD) digital broadcast and streaming over the Internet, distributed orchestrated computing, and peak-demand short-term connectivity for Access Network providers and wireless network operators for backhaul surges. One desirable feature is fast and automatic provisioning. Connection (lightpath) provisioning in optically switched networks requires both route computation and a single wavelength to be assigned for the lightpath. This is called Routing and Wavelength Assignment (RWA). RWA can be classified as static RWA and dynamic RWA. Static RWA is an NP-hard (non-polynomial time hard) optimisation task. Dynamic RWA is even more challenging as connection requests arrive dynamically, on-the-fly and have random connection holding times. Traditionally, global-optimum mathematical search schemes like integer linear programming and graph colouring are used to find an optimal solution for NP-hard problems. However such schemes become unusable for connection provisioning in a dynamic environment, due to the computational complexity and time required to undertake the search. To perform dynamic provisioning, different heuristic and stochastic techniques are used. Particle Swarm Optimisation (PSO) is a population-based global optimisation scheme that belongs to the class of evolutionary search algorithms and has successfully been used to solve many NP-hard optimisation problems in both static and dynamic environments. In this thesis, a novel PSO based scheme is proposed to solve the static RWA case, which can achieve optimal/near-optimal solution. In order to reduce the risk of premature convergence of the swarm and to avoid selecting local optima, a search scheme is proposed to solve the static RWA, based on the position of swarm‘s global best particle and personal best position of each particle. To solve dynamic RWA problem, a PSO based scheme is proposed which can provision a connection within a fraction of a second. This feature is crucial to provisioning services like bandwidth on demand connectivity. To improve the convergence speed of the swarm towards an optimal/near-optimal solution, a novel chaotic factor is introduced into the PSO algorithm, i.e. CPSO, which helps the swarm reach a relatively good solution in fewer iterations. Experimental results for PSO/CPSO based dynamic RWA algorithms show that the proposed schemes perform better compared to other evolutionary techniques like genetic algorithms, ant colony optimization. This is both in terms of quality of solution and computation time. The proposed schemes also show significant improvements in blocking probability performance compared to traditional dynamic RWA schemes like SP-FF and SP-MU algorithms

Optimización de tráfico en redes multiservicios aplicando técnicas heurísticas

El abrupto crecimiento del tráfico presente en las redes convergentes actuales, trae como consecuencia la implementación de nuevas tecnologías que permiten ofrecer a los usuarios mayores anchos de banda para lo cual es necesario realizar una distribución óptima del tráfico, tomando algún criterio de desempeño y teniendo en cuenta la elasticidad del flujo que involucra atender tráficos tan disímiles como voz, video, sonido, datos, entre otros. Optimizar la distribución de distintos requerimientos considerando estos aspectos en redes multiservicios permite garantizar la disponibilidad de la red para los requerimientos de tráfico, cuando las demandas modernas ponen en riesgo de congestión a las redes que utilizan las técnicas tradicionales de conmutación. MPLS (conmutación de etiquetas multiprotocolo) se ha convertido en una tecnología eficaz en la solución a estos inconvenientes, aunque el problema de la selección de la mejor ruta y de la distribución de tráfico no solo sigue existiendo, sino que exige nuevas propuestas de optimización del enrutamiento. En muchos casos, la planificación óptima de distribución de tráfico en redes MPLS, conlleva la necesidad de resolver un problema de optimización combinatorio de características tales que, para instancias medias o grandes del problema, los métodos determinísticos no son adecuados desde el punto de vista del tiempo de ejecución necesario para obtener el óptimo. En este punto las heurísticas, constituyen una alternativa válida para proporcionar buenas soluciones en tiempos aceptables. En esta tesis se presenta una taxonomía de estrategias heurísticas y metaheurísticas con el objetivo de distribuir los requerimientos en los enlaces disponibles de una red minimizando el costo de enrutamiento, al tiempo que se satisfacen restricciones en cuanto a demanda y capacidad de cada enlace. Se presenta el desarrollo, descripción y modelado del problema, se diseñan diferentes algoritmos bio-inspirados en el comportamiento de enjambres que brindan una solución de configuración fuera de línea, a este problema tradicional de la ingeniería de tráfico en redes con alta interconectividad. Se implementan cinco algoritmos inspirados en bandadas de pájaros, colonias de hormigas y el comportamiento de quirópteros, que permiten determinar una solución óptima explorando el espacio de búsqueda desde diferentes estrategias. Se ejecutan los algoritmos sobre cuatro redes de ensayo de diferentes tamaños, con lo que se determina la aplicabilidad de los algoritmos, y los parámetros óptimos de funcionamiento en cada caso, se presenta el análisis comparativo de los resultados obtenidos y se dejan planteadas distintas opciones de trabajos e investigaciones a futuro.Facultad de Informátic

Journal of Telecommunications and Information Technology, 2011, nr 3

kwartalni

Dynamic routing optimization using traffic prediction

In this dissertation, a new efficient routing maintenance algorithm, called Predicting of Future Load-based Routing (PFLR), is introduced for optimizing the routing performance in IP-based networks. The main idea of PFLR algorithm is combing the predicted link load with the current link load with an effective method to optimize the link weights and so reduce the network congestions. Another research objective is introducing a new efficient Traffic Engineering (TE) algorithm, called Prediction-based Decentralized Routing (PDR) algorithm, which is fully decentralized and self-organized approach

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Finding and Mitigating Geographic Vulnerabilities in Mission Critical Multi-Layer Networks

Title from PDF of title page, viewed on June 20, 2016Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 232-257)Thesis(Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2016In Air Traffic Control (ATC), communications outages may lead to immediate loss of communications or radar contact with aircraft. In the short term, there may be safety related issues as important services including power systems, ATC, or communications for first responders during a disaster may be out of service. Significant financial damage from airline delays and cancellations may occur in the long term. This highlights the different types of impact that may occur after a disaster or other geographic event. The question is How do we evaluate and improve the ability of a mission-critical network to perform its mission during geographically correlated failures? To answer this question, we consider several large and small networks, including a multi-layer ATC Service Oriented Architecture (SOA) network known as SWIM. This research presents a number of tools to analyze and mitigate both long and short term geographic vulnerabilities in mission critical networks. To provide context for the tools, a disaster planning approach is presented that focuses on Resiliency Evaluation, Provisioning Demands, Topology Design, and Mitigation of Vulnerabilities. In the Resilience Evaluation, we propose a novel metric known as the Network Impact Resilience (NIR) metric and a reduced state based algorithm to compute the NIR known as the Self-Pruning Network State Generation (SP-NSG) algorithm. These tools not only evaluate the resiliency of a network with a variety of possible network tests, but they also identify geographic vulnerabilities. Related to the Demand Provisioning and Mitigation of Vulnerabilities, we present methods that focus on provisioning in preparation for rerouting of demands immediately following an event based on Service Level Agreements (SLA) and fast rerouting of demands around geographic vulnerabilities using Multi-Topology Routing (MTR). The Topology Design area focuses on adding nodes to improve topologies to be more resistant to geographic vulnerabilities. Additionally, a set of network performance tools are proposed for use with mission critical networks that can model at least up to 2nd order network delay statistics. The first is an extension of the Queueing Network Analyzer (QNA) to model multi-layer networks (and specifically SOA networks). The second is a network decomposition tool based on Linear Algebraic Queueing Theory (LAQT). This is one of the first extensive uses of LAQT for network modeling. Benefits, results, and limitations of both methods are described.Introduction -- SWIM Network - Air traffic Control example -- Performance analysis of mission critical multi-layer networks -- Evaluation of geographically correlated failures in multi-layer networks -- Provisioning and restoral of mission critical services for disaster resilience -- Topology improvements to avoid high impact geographic events -- Routing of mission critical services during disasters -- Conclusions and future research -- Appendix A. Pub/Sub simulation model description -- Appendix B. ME Random Number Generatio