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

Analysis of artificial intelligence-based metaheuristic algorithm for MPLS network optimization

Multiprotocol label switched (MPLS) networks were introduced to enhance the network`s service provisioning and optimize its performance using multiple protocols along with label switched based networking technique. With the addition of traffic engineering entity in MPLS domain, there is a massive increase in the networks resource management capability with better quality of services (QoS) provisioning for end users. Routing protocols play an important role in MPLS networks for network traffic management, which uses exact and approximate algorithms. There are number of artificial intelligence-based optimization algorithms which can be used for the optimization of traffic engineering in MPLS networks. The paper presents an optimization model for MPLS networks and proposed dolphin-echolocation algorithm (DEA) for optimal path computation. For Network with different nodes, both algorithms performance has been investigated to study their convergence towards the production of optimal solutions. Furthermore, the DEA algorithm will be compared with the bat algorithm to examine their performance in MPLS network optimization. Various parameters such as mean, minimum /optimal fitness function values and standard deviation. Document type: Conference objec

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

Analysis of artificial intelligence-based metaheuristic algorithm for MPLS network optimization

Multiprotocol label switched (MPLS) networks were introduced to enhance the network`s service provisioning and optimize its performance using multiple protocols along with label switched based networking technique. With the addition of traffic engineering entity in MPLS domain, there is a massive increase in the networks resource management capability with better quality of services (QoS) provisioning for end users. Routing protocols play an important role in MPLS networks for network traffic management, which uses exact and approximate algorithms. There are number of artificial intelligence-based optimization algorithms which can be used for the optimization of traffic engineering in MPLS networks. The paper presents an optimization model for MPLS networks and proposed dolphin-echolocation algorithm (DEA) for optimal path computation. For Network with different nodes, both algorithms performance has been investigated to study their convergence towards the production of optimal solutions. Furthermore, the DEA algorithm will be compared with the bat algorithm to examine their performance in MPLS network optimization. Various parameters such as mean, minimum /optimal fitness function values and standard deviation

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

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

A Pareto based approach with elitist learning strategy for MPLS/GMPS networks

Abstract—Modern telecommunication networks are based on diverse applications that highlighted the status of efficient use of network resources and performance optimization. Various methodologies are developed to address multi-objectives optimization within the traffic engineering of MPLS/ GMPLS networks. However, Pareto based approach can be used to achieve the optimization of multiple conflicting objective functions concurrently. The paper considered two objective functions such as routing and load balancing costs functions. The paper introduces a heuristics algorithm for solving multi-objective multiple constrained optimization (MCOP) in MPLS/ GMPLS networks. The paper proposes the application of a Pareto based particle swarm optimization (PPSO) for such network’s type and through a comparative analysis tests its efficiency against another modified version; Pareto based particle swarm optimization with elitist learning strategy (PPSO ELS). The simulation results showed that the former proposed approach not only solved the MCOP problem but also provide effective solution for exploration problem attached with PPSO algorithm