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

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

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