Abstract
Multicast routing (MR) is a technology for delivering network data from some source node(s) to a group of destination nodes. The objective of the minimum cost MR (MCMR) problem is to find an optimal multicast tree with the minimum cost for MR. This problem is NP complete. In order to tackle the problem, this paper proposes a novel algorithm termed the minimum cost multicast routing ant colony optimization (MCMRACO). Based on the ant colony optimization (ACO) framework, the artificial ants in the proposed algorithm use a probabilistic greedy realization of Prim’s algorithm to construct multicast trees. Moving in a cost complete graph (CCG) of the network topology, the ants build solutions according to the heuristic and pheromone information. The heuristic information represents problem-specific knowledge for the ants to construct solutions. The pheromone update mechanisms coordinate the ants’ activities by modulating the pheromones. The algorithm can quickly respond to the changes of multicast nodes in a dynamic MR environment. The performance of the proposed algorithm has been compared with published results available in the literature. Results show that the proposed algorithm performs well in both static and dynamic MCMR problems.
Highlights
Multicast routing (MR) is one of the most important communication network routing technologies
This paper proposes a minimum cost multicast routing ant colony optimization (MCMRACO) algorithm for solving the minimum cost multicast routing (MCMR) problems
Different from the traditional algorithms for minimum cost MR (MCMR), the proposed MCMRACO utilizes the ant colony optimization (ACO) technique to search for an optimal multicast tree in the network graph
Summary
Multicast routing (MR) is one of the most important communication network routing technologies. The authors had tested three different sequences for moving the ants from one node to another and found that the random approach was the best Their algorithm still could not always find the optimal solutions of some of their test cases. In order to make better use of ACO, this paper proposes a novel minimum cost multicast routing ant colony optimization (MCMRACO) algorithm for solving MCMR problems. (2) Moving in a cost complete graph (CCG) of the network topology, the ants build solutions according to the heuristic and pheromone information. (3) The heuristic information is designed to represent problem-specific knowledge for the ants to construct solutions and to bias the selection of nodes in the multicast group. Utilizing heuristic and pheromone information effectively, the proposed MCMRACO is more suitable for solving MCMR problems than the other heuristic and CI algorithms.
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