International Journal of Computational Intelligence and Applications

Multiprotocol label switching (MPLS) is a hybrid solution that combines the advantages of easy forwarding with the ability of guaranteeing quality-of-service (QoS). To deliver reliable service, MPLS requires traffic protection and recovery. Rerouting is one such recovery mechanism. In this paper, we propose a novel rerouting model called DDRAAS that is inspired by the spider and its web in nature. We try to establish an artificial logical spider web in the MPLS network to reorganise it into a structure that is more regular and simple. Based on this, we give the definition of the reroute area. Artificial spiders are then used to explore recovery paths dynamically in the reroute area. DDRAAS can be used to calculate the recovery paths in advance in order to protect the work path, while the improved DDRAAS can be a fast rerouting algorithm to calculate and establish recovery paths when faults occur.

We have simulated our mechanism using the MPLS network simulator (MNS) and the performance metrics were compared to those of other proposals. The simulation results show that our mechanism is better in reducing packet loss, disorder and has faster rerouting speed. These improvements help to minimise the effects of link failure and/or congestion.

This paper introduces a new generic routing algorithm called QColony for packet-switched communications networks that support real-time flows. In this context, we introduce both a novel path-selection scheme, namely the best-fit scheme, and a novel routing technique, namely the multi-pheromone technique, inspired by observations of biological ant colonies. Simulation was carried out for our algorithm and two other routing algorithms under various traffic scenarios and different irregular network topologies. Simulation results show that we can achieve good performance for the QColony algorithm. According to our experiments, QColony is able to provide smooth performance for all operating conditions, especially with large networks and under traffic scenarios with failure conditions and improperly functioning nodes. We also demonstrate through simulation that the best-fit scheme is able to deliver higher routing performance than what the shortest-path scheme can achieve.

This paper describes AntHocNet, an algorithm for routing in mobile ad-hoc networks based on ideas from the ant colony optimisation framework. In AntHocNet a source node reactively sets up a path to a destination node at the start of each communication session. During the course of the session, the source node uses ant agents to proactively search for alternatives and improvements of the original path. This allows to adapt to changes in the network, and to construct a mesh of alternative paths between source and destination. The proactive behaviour is supported by a lightweight information bootstrapping process. Paths are represented in the form of distance-vector routing tables called pheromone tables. An entry of a pheromone table contains the estimated goodness of going over a certain neighbour to reach a certain destination. Data are routed stochastically over the different paths of the mesh according to these goodness estimates. In an extensive set of simulation tests, we compare AntHocNet to AODV, a reactive algorithm which is an important reference in this research area. We show that AntHocNet can outperform AODV for different evaluation criteria in a wide range of different scenarios. AntHocNet is also shown to scale well with respect to the number of nodes.

Multicasting protocols deliver data packets from a source node to multiple receivers, and serve a very important function in mobile ad-hoc networks (MANETs). In this paper, a novel receiver-initiated soft-state probabilistic multicasting protocol (RISP) for MANETs is proposed. RISP is inspired by the ant colony's route-seeking mechanism, in which an individual ant chooses the optimal path to its destination through cooperation with others in a totally distributed manner. Imitating the behaviour of ants in nature, RISP introduces probabilistic forwarding and soft-state for making relay decisions that are automatically adaptive to node mobility in MANETs. Compared with other protocols, we show by computer simulations that RISP has lower delivery redundancy, while achieving higher delivery ratio at all mobility scenarios. Furthermore, RISP has lower control overhead.

In this paper, we present a novel multiuser detection (MUD) technique based on ant colony optimisation (ACO), for synchronous direct sequence code division multiple access systems. ACO algorithms are based on the cooperative foraging strategy of real ants. While an optimal MUD design using an exhaustive search method is prohibitively complex, we show that the ACO-based MUD converges to the optimal bit-error-rate performance in relatively few iterations providing 95% savings in computational complexity. This reduction in complexity is retained even when considering users with unequal received powers.

We present an application of multiple-objectives evolutionary optimisation to the problem of engineering the distribution of the interdomain traffic in the internet. We show that this practical problem requires such a heuristic due to the potentially conflicting nature of traffic-engineering objectives. Furthermore, having to work on the parameter's space (BGP routing) of the real problem makes such techniques as evolutionary optimisation very easy to use. We show the successful application of our algorithm to two practically relevant problems in interdomain traffic engineering.

The widespread deployment of WDM optical networks posts lots of new challenges for network designers. Traffic grooming is one of the most common problems. Efficient grooming of traffic can effectively reduce the overall cost of the network. But unfortunately, traffic grooming problems have been shown to be NP-hard. Therefore, new heuristics must be devised to tackle them. Among these approaches, metaheuristics are among the most promising ones. In this paper, we present a thorough and comprehensive survey on various metaheuristic approaches to the grooming of traffic in both static and dynamic patterns in WDM optical networks. Some future challenges and research directions are also discussed in this paper.

For the reliable communication network design (RCND) problem unreliable links are available, each bearing several options which have different levels of reliability and varying costs. The goal is to find the most cost-effective communication network design that satisfies a predefined overall reliability constraint. This paper presents two new evolutionary algorithm (EA) approaches to solving the RCND problem: LaBORNet and BaBORNet. LaBORNet uses an encoding that represents the network topology as well as the used link options while repairing infeasible solutions using an additional repair heuristic (CURE). BaBORNet encodes only the network topology and determines the link options by using the repair heuristic CURE as a local search method. The experimental results show that the new EA approaches using repair heuristics outperform existing EA approaches from the literature using penalties for infeasible solutions. They also find better solutions for existing problems from the literature, as well as for new and larger test problems.

This paper presents a genetic algorithm for designing minimum-cost two-connected networks such that the shortest cycle to which each edge belongs to does not exceed a given length. We provide numerical results based on randomly generated graphs found in the literature and compare the solution quality with that of tabu search and branch and bound. The results demonstrate the effectiveness of our algorithm and show promise for tackling ring-based network design problems. This paper is among the first to document the implementation of a genetic algorithm for the design of two-connected networks with the added constraint of bounded rings.