Multiple Feasible Paths in Ant Colony Algorithm for mobile Ad-hoc Networks with Minimum Overhead

Mobile ad-hoc networks are infrastructure-less networks consisting of wireless, possibly mobile nodes which are organized in peer-to-peer and autonomous fashion. The highly dynamic topology, limited bandwidth availability and energy constraints make the routing problem a challenging one. Ant colony optimization (ACO) is a population based meta-heuristic for combinatorial optimization problems such as communication network routing problem. In real life, ants drop some kind of chemical substances to mark the path that they used. Then on their way, back they choose the path with the highest pheromones which becomes the shortest path. But Ant net Algorithms may cause the network congestion and stagnation. Here, multiple optimal paths are proposed with negligible overhead in spite of single optimal path in Ant net routing algorithm, so that the problem of stagnation can be rectified. This paper proposes an improved Multiple Feasible Paths in Ant Colony Algorithm for mobile Ad-hoc Networks with Minimum Overhead

A Potts Neuron Approach to Communication Routing

A feedback neural network approach to communication routing problems is developed with emphasis on Multiple Shortest Path problems, with several requests for transmissions between distinct start- and endnodes. The basic ingredients are a set of Potts neurons for each request, with interactions designed to minimize path lengths and to prevent overloading of network arcs. The topological nature of the problem is conveniently handled using a propagator matrix approach. Although the constraints are global, the algorithmic steps are based entirely on local information, facilitating distributed implementations. In the polynomially solvable single-request case the approach reduces to a fuzzy version of the Bellman-Ford algorithm. The approach is evaluated for synthetic problems of varying sizes and load levels, by comparing with exact solutions from a branch-and-bound method. With very few exceptions, the Potts approach gives legal solutions of very high quality. The computational demand scales merely as the product of the numbers of requests, nodes, and arcs.Comment: 10 pages LaTe

Constrained shortest paths for QoS routing and path protection in communication networks.

The CSDP (k) problem requires the selection of a set of k > 1 link-disjoint paths with minimum total cost and with total delay bounded by a given upper bound. This problem arises in the context of provisioning paths in a network that could be used to provide resilience to link failures. Again we studied the LP relaxation of the ILP formulation of the problem from the primal perspective and proposed an approximation algorithm.We have studied certain combinatorial optimization problems that arise in the context of two important problems in computer communication networks: end-to-end Quality of Service (QoS) and fault tolerance. These problems can be modeled as constrained shortest path(s) selection problems on networks with each of their links associated with additive weights representing the cost, delay etc.The problems considered above assume that the network status is known and accurate. However, in real networks, this assumption is not realistic. So we considered the QoS route selection problem under inaccurate state information. Here the goal is to find a path with the highest probability that satisfies a given delay upper bound. We proposed a pseudo-polynomial time approximation algorithm, a fully polynomial time approximation scheme, and a strongly polynomial time heuristic for this problem.Finally we studied the constrained shortest path problem with multiple additive constraints. Using the LARAC algorithm as a building block and combining ideas from mathematical programming, we proposed a new approximation algorithm.First we studied the QoS single route selection problem, i.e., the constrained shortest path (CSP) problem. The goal of the CSP problem is to identify a minimum cost route which incurs a delay less than a specified bound. It can be formulated as an integer linear programming (ILP) problem which is computationally intractable. The LARAC algorithm reported in the literature is based on the dual of the linear programming relaxation of the ILP formulation and gives an approximate solution. We proposed two new approximation algorithms solving the dual problem. Next, we studied the CSP problem using the primal simplex method and exploiting certain structural properties of networks. This led to a novel approximation algorithm

Efficient negative cycle-canceling algorithm for finding the optimal traffic routing for network evacuation with nonuniform threats

A new network flow solution method is designed to determine optimal traffic routing efficiently for the evacuation of networks with several threat zones and with nonuniform threat levels across zones. The objective is to minimize total exposure (as duration and severity) to the threat for all evacuees during the evacuation. The problem is formulated as a minimum cost dynamic flow problem coupled with traffic dynamic constraints. The traffic flow dynamic constraints are enforced by the well-known point queue and spatial queue models in a time-expanded network presentation. The key to the efficiency of the proposed method is that, for any feasible solution, the algorithm can find and can cancel multiple negative cycles (including the cycle with the largest negative cost) with a single shortest path calculation made possible by applying a proposed transformation to the original problem. A cost transformation function and a multisource shortest path algorithm are proposed to facilitate the efficient detection and cancelation of negative cycles. Zone by zone, negative cycles are canceled at the border links of the zones. The solution method is proved to be optimal. The algorithm is implemented, tested, and verified to be optimal for a midsized example problem

Multi-objective stochastic path planning

The present research formulates the path planning as an optimization problem with multiple objectives and stochastic edge parameters. The first section introduces different variants of the PP problem and discusses existing solutions to the problem. The next section introduces and solves various versions of the PP model within the scope of this research. The first three versions describe a single entity traveling from a single source to a single destination node. In the first version, the entity has a single objective and abides by multiple constraints. The second version deals with an entity traveling with multiple objectives and multiple constraints. The third version is a modification of the second version where the actual probability distributions of travel times along edges are known. The fourth and final version deals with multiple heterogeneous entities routed from multiple sources (supply nodes) to multiple destinations (demand nodes) along capacitated edges. Each of these formulations is solved by using either exact algorithms or heuristics developed in this research. The performance of each algorithm/heuristic is discussed in the final section. The main contributions of this research are: 1. Provide a framework for analyzing PP in presence of multiple objectives and stochastic edge parameters. 2. Identify candidate constraints where clustering based multi-level programming can be applied to eliminate infeasible edges. 3. Provide an exact O (V.E) algorithm for building redundant shortest paths. 4. Provide an O (V.E+C2) heuristic for generating Pareto optimal shortest paths in presence of multiple objectives where C is the upper bound for path length. The complexity can be further reduced to O (V.E) by using graphical read-out of the Pareto frontier. 5. Provide a cost structure which can capture multiple key probability distribution parameters of edge variables. This is in contrast with usual techniques which just capture single parameters like the mean or the variance of distributions. 6. Provide a MIP formulation to a multi-commodity transportation problem with multiple decision variables, stochastic demands and uncertain edge/route capacities. 7. Provide an alternate formulation to the classic binary facility selection problem

A new multiple objective dynamic routing method using implied costs, Journal of Telecommunications and Information Technology, 2003, nr 3

There are advantages in considering the routing problem in integrated communication networks as a multiobjective shortest path problem, having in mind to grasp eventual conflicts and trade-offs among distinct objectives and quality of services (QoS) constraints. On the other hand the utilisation of dynamic routing methods in various types of networks is well known to have significant impact on network performance and cost, namely in overload and failure conditions. This paper presents the detailed formulation of a proposal of a multiple objective dynamic routing method (MODR) of periodic state dependent routing type, enabling to represent distinct QoS related metrics and requirements in a consistent manner. The MODR method present formulation is based on a multiple objective shortest path model with constraints and is prepared to use implied costs as one of the metrics. Alternative paths for each traffic flow are changed as a function of periodic updates of certain QoS related parameters estimated from real time measurements on the routes and trunks of the network. Such paths are computed by a specialised and efficient variant of a bi-objective shortest path constrained algorithm, developed for the MODR, enabling to incorporate flexible requirements on the QoS metrics. The architecture of the routing system is discussed together with the features of its main modules. An illustrative example of application of theMODR path calculation module to a circuit-switched type network using blocking probability and implied cost as metrics, is also presented, considering different overload conditions