Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

This paper presents the first investigation on applying a particle swarm optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics community. The literature on the application of meta-heuristics to multicast routing problems is less extensive but includes several promising approaches. Many interesting research issues still remain to be investigated, for example, the inclusion of different constraints, such as delay bounds, when finding multicast trees with minimum cost. In this paper, we develop a novel PSO algorithm based on the jumping PSO (JPSO) algorithm recently developed by Moreno-Perez et al. (Proc. of the 7th Metaheuristics International Conference, 2007), and also propose two novel local search heuristics within our JPSO framework. A path replacement operator has been used in particle moves to improve the positions of the particle with regard to the structure of the tree. We test the performance of our JPSO algorithm, and the effect of the integrated local search heuristics by an extensive set of experiments on multicast routing benchmark problems and Steiner tree problems from the OR library. The experimental results show the superior performance of the proposed JPSO algorithm over a number of other state-of-the-art approaches

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.publishedVersio

Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network

In Wireless Sensor Network (WSN), high transmission time occurs when search agent focuses on the same sensor nodes, while local optima problem happens when agent gets trapped in a blind alley during searching. Swarm intelligence algorithms have been applied in solving these problems including the Ant Colony System (ACS) which is one of the ant colony optimization variants. However, ACS suffers from local optima and stagnation problems in medium and large sized environments due to an ineffective exploration mechanism. This research proposes a hybridization of Enhanced ACS and Tabu Search (EACS(TS)) algorithm for packet routing in WSN. The EACS(TS) selects sensor nodes with high pheromone values which are calculated based on the residual energy and current pheromone value of each sensor node. Local optima is prevented by marking the node that has no potential neighbour node as a Tabu node and storing it in the Tabu list. Local pheromone update is performed to encourage exploration to other potential sensor nodes while global pheromone update is applied to encourage the exploitation of optimal sensor nodes. Experiments were performed in a simulated WSN environment supported by a Routing Modelling Application Simulation Environment (RMASE) framework to evaluate the performance of EACS(TS). A total of 6 datasets were deployed to evaluate the effectiveness of the proposed algorithm. Results showed that EACS(TS) outperformed in terms of success rate, packet loss, latency, and energy efficiency when compared with single swarm intelligence routing algorithms which are Energy-Efficient Ant-Based Routing (EEABR), BeeSensor and Termite-hill. Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. The outcome of this research contributes an optimized routing algorithm for WSN. This will lead to a better quality of service and minimum energy utilization

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

ANTMANET: a novel routing protocol for mobile ad-hoc networks based on ant colony optimisation

The core aim of this research is to present “ANTMANET” a novel routing protocol for Mobile Ad-Hoc networks. The proposed protocol aims to reduce the network overhead and delay introduced by node mobility in MANETs. There are two techniques embedded in this protocol, the “Local Zone” technique and the “North Neighbour” Table. They take an advantage of the fact that the nodes can obtain their location information by any means to reduce the network overhead during the route discovery phase and reduced the size of the routing table to guarantee faster convergence. ANTMANET is a hybrid Ant Colony Optimisation-based (ACO) routing protocol. ACO is a Swarm Intelligence (SI) routing algorithm that is well known for its high-quality performance compared to other distributed routing algorithms such as Link State and Distance Vector. ANTMANET has been benchmarked in various scenarios against the ACO routing protocol ANTHOCNET and several standard routing protocols including the Ad-Hoc On-Demand Distance Vector (AODV), Landmark Ad-Hoc Routing (LANMAR), and Dynamic MANET on Demand (DYMO). Performance metrics such as overhead, end-to-end delay, throughputs and jitter were used to evaluate ANTMANET performance. Experiments were performed using the QualNet simulator. A benchmark test was conducted to evaluate the performance of an ANTMANET network against an ANTHOCNET network, with both protocols benchmarked against AODV as an established MANET protocol. ANTMANET has demonstrated a notable performance edge when the core algorithm has been optimised using the novel adaptation method that is proposed in this thesis. Based on the simulation results, the proposed protocol has shown 5% less End-to-End delay than ANTHOCNET. In regard to network overhead, the proposed protocol has shown 20% less overhead than ANTHOCNET. In terms of comparative throughputs ANTMANET in its finest performance has delivered 25% more packets than ANTHOCNET. The overall validation results indicate that the proposed protocol was successful in reducing the network overhead and delay in high and low mobility speeds when compared with the AODV, DMO and LANMAR protocols. ANTMANET achieved at least a 45% less delay than AODV, 60% less delay than DYMO and 55% less delay than LANMAR. In terms of throughputs; ANTMANET in its best performance has delivered 35% more packets than AODV, 40% more than DYMO and 45% more than LANMAR. With respect to the network overhead results, ANTMANET has illustrated 65% less overhead than AODV, 70% less than DYMO and 60 % less than LANMAR. Regarding the Jitter, ANTMANET at its best has shown 60% less jitter than AODV, 55% jitter less than DYMO and 50% less jitter than LANMAR

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

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

Multi-objective tools for the vehicle routing problem with time windows

Most real-life problems involve the simultaneous optimisation of two or more, usually conflicting, objectives. Researchers have put a continuous effort into solving these problems in many different areas, such as engineering, finance and computer science. Over time, thanks to the increase in processing power, researchers have created methods which have become increasingly sophisticated. Most of these methods have been based on the notion of Pareto dominance, which assumes, sometimes erroneously, that the objectives have no known ranking of importance. The Vehicle Routing Problem with Time Windows (VRPTW) is a logistics problem which in real-life applications appears to be multi-objective. This problem consists of designing the optimal set of routes to serve a number of customers within certain time slots. Despite this problem’s high applicability to real-life domains (e.g. waste collection, fast-food delivery), most research in this area has been conducted with hand-made datasets. These datasets sometimes have a number of unrealistic features (e.g. the assumption that one unit of travel time corresponds to one unit of travel distance) and are therefore not adequate for the assessment of optimisers. Furthermore, very few studies have focused on the multi-objective nature of the VRPTW. That is, very few have studied how the optimisation of one objective affects the others. This thesis proposes a number of novel tools (methods + dataset) to address the above- mentioned challenges: 1) an agent-based framework for cooperative search, 2) a novel multi-objective ranking approach, 3) a new dataset for the VRPTW, 4) a study of the pair-wise relationships between five common objectives in VRPTW, and 5) a simplified Multi-objective Discrete Particle Swarm Optimisation for the VRPTW

Intelligent Advancements in Location Management and C-RAN Power-Aware Resource Allocation

The evolving of cellular networks within the last decade continues to focus on delivering a robust and reliable means to cope with the increasing number of users and demanded capacity. Recent advancements of cellular networks such as Long-Term Evolution (LTE) and LTE-advanced offer a remarkable high bandwidth connectivity delivered to the users. Signalling overhead is one of the vital issues that impact the cellular behavior. Causing a significant load in the core network hence effecting the cellular network reliability. Moreover, the signaling overhead decreases the Quality of Experience (QoE) of users. The first topic of the thesis attempts to reduce the signaling overhead by developing intelligent location management techniques that minimize paging and Tracking Area Update (TAU) signals. Consequently, the corresponding optimization problems are formulated. Furthermore, several techniques and heuristic algorithms are implemented to solve the formulated problems. Additionally, network scalability has become a challenging aspect that has been hindered by the current network architecture. As a result, Cloud Radio Access Networks (C-RANs) have been introduced as a new trend in wireless technologies to address this challenge. C-RAN architecture consists of: Remote Radio Head (RRH), Baseband Unit (BBU), and the optical network connecting them. However, RRH-to-BBU resource allocation can cause a significant downgrade in efficiency, particularly the allocation of the computational resources in the BBU pool to densely deployed small cells. This causes a vast increase in the power consumption and wasteful resources. Therefore, the second topic of the thesis discusses C-RAN infrastructure, particularly where a pool of BBUs are gathered to process the computational resources. We argue that there is a need of optimizing the processing capacity in order to minimize the power consumption and increase the overall system efficiency. Consequently, the optimal allocation of computational resources between the RRHs and BBUs is modeled. Furthermore, in order to get an optimal RRH-to-BBU allocation, it is essential to have an optimal physical resource allocation for users to determine the required computational resources. For this purpose, an optimization problem that models the assignment of resources at these two levels (from physical resources to users and from RRHs to BBUs) is formulated