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A Rough Penalty Genetic Algorithm for Multicast Routing in Mobile Ad Hoc Networks

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  • Chih-Hao Lin
  • Chia-Chun Chuang

Abstract

Multicast routing is an effective way to transmit messages to multiple hosts in a network. However, it is vulnerable to intermittent connectivity property in mobile ad hoc network (MANET) especially for multimedia applications, which have some quality of service (QoS) requirements. The goal of QoS provisioning is to well organize network resources to satisfy the QoS requirement and achieve good network delivery services. However, there remains a challenge to provide QoS solutions and maintain end-to-end QoS with user mobility. In this paper, a novel penalty adjustment method based on the rough set theory is proposed to deal with path-delay constraints for multicast routing problems in MANETs. We formulate the problem as a constrained optimization problem, where the objective function is to minimize the total cost of the multicast tree subject to QoS constraints. The RPGA is evaluated on three multicast scenarios and compared with two state-of-the-art methods in terms of cost, success rate, and time complexity. The performance analyses show that this approach is a self-adaptive method for penalty adjustment. Remarkably, the method can address a variety of constrained multicast routing problems even though the initial routes do not satisfy all QoS requirements.

Suggested Citation

  • Chih-Hao Lin & Chia-Chun Chuang, 2013. "A Rough Penalty Genetic Algorithm for Multicast Routing in Mobile Ad Hoc Networks," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-11, August.
  • Handle: RePEc:hin:jnljam:986985
    DOI: 10.1155/2013/986985
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    Cited by:

    1. Jaeyoung Yang & Yong-Hyuk Kim & Yourim Yoon, 2022. "A Memetic Algorithm with a Novel Repair Heuristic for the Multiple-Choice Multidimensional Knapsack Problem," Mathematics, MDPI, vol. 10(4), pages 1-15, February.

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