491 research outputs found

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

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    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

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

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    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

    A hybrid ACO/PSO based algorithm for QoS multicast routing problem

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    AbstractMany Internet multicast applications such as videoconferencing, distance education, and online simulation require to send information from a source to some selected destinations. These applications have stringent Quality-of-Service (QoS) requirements that include delay, loss rate, bandwidth, and delay jitter. This leads to the problem of routing multicast traffic satisfying QoS requirements. The above mentioned problem is known as the QoS constrained multicast routing problem and is NP Complete. In this paper, we present a swarming agent based intelligent algorithm using a hybrid Ant Colony Optimization (ACO)/Particle Swarm Optimization (PSO) technique to optimize the multicast tree. The algorithm starts with generating a large amount of mobile agents in the search space. The ACO algorithm guides the agents’ movement by pheromones in the shared environment locally, and the global maximum of the attribute values are obtained through the random interaction between the agents using PSO algorithm. The performance of the proposed algorithm is evaluated through simulation. The simulation results reveal that our algorithm performs better than the existing algorithms

    Multicast routing and wavelength assignment in optical networks with particle swarm optimization

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    Large bandwidth on hand in WDM networks is the best choice for increasing tra c demand; although, routing and wavelength assignment (RWA) problems still remain a challenge. This work proposes a novel method to solve multicast-RWA problems, using multi-objective Particle Swarm Optimization (PSO), implementing four competitive ap- proaches of state-of-the-art. Such algorithms minimize simultaneously the hop count, the number of splitting power light, the number of split- ter node and the balancing of multicast tree for a given set of multicast demands. This way, a set of optimal solutions (known as Pareto set) is obtained in one run of the algorithms, without a priori restrictions. Sim- ulation results prove the viability of the PSO proposal and the advantage compared on classical approaches as Multicast Open Shortest Path First routing algorithm and Least Used wavelength assignment algorithm.IV Workshop Arquitectura, Redes y Sistemas Operativos (WARSO)Red de Universidades con Carreras en Informática (RedUNCI

    Quality of Service (QoS) routing algorithm for Software Defined Network (SDN)

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    Due to the use of various technologies like mobile, cloud, big data. The network traffic has increased this has resulted in the  re examination of the working of  traditional network architectures as these are built as static architectures and cannot handle the rapid growing traffic on the internet. A dynamic architecture which can be programmed according to the traffic behaviour was the need. Software Defined Networking (SDN) was emerged to address the growing needs of the dynamic traffic which has been in the moonlight since 2010. SDN increase and makes the network as flexible to program according to the programmers needs by keeping the traffic in line. It gives the user flexibility of adjusting the network resources by separating the control plane and data plane. By using SDN networks can be managed dynamically. The capacity of a network to offer good services to the selected network traffic over various technologies is termed as Quality of Service (QoS). To transfer high-bandwidth video and multimedia information continuously QoS is of particular objective.Â

    FANET optimization: a destination path flow model

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    Closed-loop routing in flying ad hoc networks (FANET) arises as a result of the quick changes of communication links and topology. As such, causing link breakage during information dissemination. This paper proposed a destination path flow model to improve the communication link in FANET. The models utilized Smell Agent Optimization and Particle Swarm Optimization algorithms in managing link establishment between communicating nodes. The modeled scenario depicts the practical application of FANET in media and sports coverage where only one vendor is given the license for live coverage and must relay to other vendors. Three different scenarios using both optimization Algorithms were presented. From the result obtained, the SAO optimizes the bandwidth costs much better than PSO with a percentage improvement of 10.46%, 4.04% and 3.66% with respect to the 1st, 2nd and 3rd scenarios respectively. In the case of communication delay between the FANET nodes, the PSO has a much better communication delay over SAO with percentage improvement of 40.89%, 50.26% and 68.85% in the first, second and third scenarios respectively

    Intelligent MANET optimisation system

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    This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the literature, various Mobile Ad hoc NETwork (MANET) routing protocols proposed. Each performs the best under specific context conditions, for example under high mobility or less volatile topologies. In existing MANET, the degradation in the routing protocol performance is always associated with changes in the network context. To date, no MANET routing protocol is able to produce optimal performance under all possible conditions. The core aim of this thesis is to solve the routing problem in mobile Ad hoc networks by introducing an optimum system that is in charge of the selection of the running routing protocol at all times, the system proposed in this thesis aims to address the degradation mentioned above. This optimisation system is a novel approach that can cope with the network performance’s degradation problem by switching to other routing protocol. The optimisation system proposed for MANET in this thesis adaptively selects the best routing protocol using an Artificial Intelligence mechanism according to the network context. In this thesis, MANET modelling helps in understanding the network performance through different contexts, as well as the models’ support to the optimisation system. Therefore, one of the main contributions of this thesis is the utilisation and comparison of various modelling techniques to create representative MANET performance models. Moreover, the proposed system uses an optimisation method to select the optimal communication routing protocol for the network context. Therefore, to build the proposed system, different optimisation techniques were utilised and compared to identify the best optimisation technique for the MANET intelligent system, which is also an important contribution of this thesis. The parameters selected to describe the network context were the network size and average mobility. The proposed system then functions by varying the routing mechanism with the time to keep the network performance at the best level. The selected protocol has been shown to produce a combination of: higher throughput, lower delay, fewer retransmission attempts, less data drop, and lower load, and was thus chosen on this basis. Validation test results indicate that the identified protocol can achieve both a better network performance quality than other routing protocols and a minimum cost function of 4.4%. The Ad hoc On Demand Distance Vector (AODV) protocol comes in second with a cost minimisation function of 27.5%, and the Optimised Link State Routing (OLSR) algorithm comes in third with a cost minimisation function of 29.8%. Finally, The Dynamic Source Routing (DSR) algorithm comes in last with a cost minimisation function of 38.3%

    Energy Efficient Multicast Routing in Mobile Ad Hoc Networks: Contemporary Affirmation of Benchmarking Models in Recent Literature

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    The Mobile Ad hoc Networks playing critical role in network aided communication requirements The features such as ad hoc and open architecture based connectivity and node mobility are elevating the mobile ad hoc networks as much as feasible to deploy and use The direct communication between any of two nodes in this network is possible if target node is in the range of source node If not the indirect communication took place which is usually referred as multi hop routing The multi hop routing occurs as either a unicast model one source node to one destination node multicast model one source node to multiple destination nodes or multiple casting manifold unicast routing In these routing strategies provision of service quality in multi hop routing is a challenging task The optimal quality of service in routing magnifies the delivery ratio transmission rate network life span and other expected characteristics of the ad hoc routing Among the quality service provision factors minimal energy conservation is prime factor which is since the nodes involved in routing are self-energized and if discharged early then the route will be destructed that causes discontinued routing The energy consumption is more specific in multicast routing hence it is grabbing the more attention of the current research contribution

    Optimisation of Mobile Communication Networks - OMCO NET

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    The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
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