International Journal of Scientific & Engineering Research Volume 4, Issue 1, January-2013 1 ISSN 2229-5518 IJSER © 2013 http://www.ijser.org Application of ant colony optimization for Multicasting in MANET Deepender Dhull, Swati Kamra Abstract- The advent of ubiquitous computing and the proliferation of portable computing devices have raised the importance of mobile and wireless networking. at the same time ,the popularity of group-oriented computing has grown tremendously .However, little has been accomplished to–date in bringing together the technologies for group-oriented communication and mobile networking. In particular, most modern wireless/mobile and ad hoc networks do not provide support for multicast communication. A major challenge lies in adapting multicast communication to environments where mobility is unlimited and outages/failures are frequent. Ant colony optimization (ACO) algorithm is a novel population-based meta-heuristic search Algorithm for solving difficult discrete optimization problems, inspired by the foraging behavior of real ant colonies .it has been applied to TSP, QAP, Scheduling and graph coloring etc. In this paper we develop an efficient heuristic ant colony algorithm for multicast routing; this algorithm can find optimal solution quickly and has a good scalability. Index Terms-ACO, CBT, MANET, Multicast Routing, PUMA, QoS, TSP. 1. INTRODUCTION A mobile ad hoc network (MANET) is a collection of wireless mobile nodes that forms a temporary network without a centralized administration and wired infrastructure. Mobile nodes communicate with each other over multi-hop wireless links. Design considerations of MANET routing protocols differ from wired network routing protocols, since a MANET is characterized by node mobility, ad hoc nodes, node/ link unreliability, limited bandwidth, high error rates, security risk, etc. The primary goal of a MANET routing protocol is to establish an efficient route between the communicating nodes to achieve the following: timely delivery of messages; reduce packet losses; provide more stable connectivity and reduce routing control overheads. Multicasting is the ability of a communication network to accept a single message from an application and deliver copies of this message to multiple recipients at different locations [1]. With the rapid development of the commercial use in internet, multimedia multicast application that satisfies the QOS Constraints attracts more and more research attention. In order to support multicast, efficient multicast routing is crucial. For delay-sensitive multimedia applications, such as real- time teleconferencing, etc.it seems more important to find a delay-constrained minimum-cost multicast tree, which has been proven to be a NP-complete problem [2]. Currently many heuristics algorithms have been proposed, most of which are centralized or centralized in nature such as BSMA (bound shortest multicast algorithm) [3], KMB[4] and CBT [5], etc.Recently, some algorithms based on genetic algorithm are also proposed [6,7,10], which are centralized algorithms, too.the research on distribution algorithms is relatively less. Ant colony optimization (ACO) is an agent-based heuristics algorithm inspired by the behavior of real ants finding food [8]. The ACO has the distributed and adaptive characteristics, which endow it with excellent performance in solving the NP-hard problems. This paper presents a novel multicast routing scheme using ant colony optimization. The scheme tries to construct the minimum-cost tree using the local information in the case that the source node does not possess the whole network information. Combined the characteristics of multicast routing, the algorithm was improved, which accelerated the convergence speed and obtained better result. 2. RELATED WORK: Several algorithms based on ACO consider the multicast routing as a mono-objective problem, minimizing the cost of the tree under multiple constraints. In Liu and Wu propose the construction of a multicast tree, where only the cost of the tree is minimized using a degree constraints. On the other hand,Gu et al. considered multiple parameters of QOS as constraints, minimizing just the cost of the tree .it can be clearly noticed that previous algorithms treat the multicast traffic engineering problem as a mono-objective problem with several constraints. Then main disadvantage of these approaches is the necessity of an a priori predefined upper bound that can exclude good practical solutions. Hiroshi Matsuo et al. [11] Accelerated Ants-Routing which increase convergence speed and obtain good routing path is discussed. Experiments on dynamic