Abstract
A mobile ad hoc network is a conventional self-configuring network where the routing optimization problem—subject to various Quality-of-Service (QoS) constraints—represents a major challenge. Unlike previously proposed solutions, in this paper, we propose a memetic algorithm (MA) employing an adaptive mutation parameter, to solve the multicast routing problem with higher search ability and computational efficiency. The proposed algorithm utilizes an updated scheme, based on statistical analysis, to estimate the best values for all MA parameters and enhance MA performance. The numerical results show that the proposed MA improved the delay and jitter of the network, while reducing computational complexity as compared to existing algorithms.
Highlights
A Mobile Ad hoc NETwork (MANET) is a collection of arbitrarily located nodes, the interconnections between them are dynamically changing [1]
We have extended their work on Genetic Algorithms (GA) to develop a new memetic algorithm (MA) that uses an adaptive mutation parameter to solve the multicast routing problem and support QoS-featured routing, based on four different QoS parameters
This paper proposes a new adaptive mutation based MA to solve the quality of service multicast routing problem for ad hoc networks
Summary
A Mobile Ad hoc NETwork (MANET) is a collection of arbitrarily located nodes, the interconnections between them are dynamically changing [1]. The current QoS multicast routing problem is formulated around four objectives and the fitness function is to identify a multicast tree T(s, D) that minimizes the weighted combination of cost, delay, jitter, and bandwidth. The main aim of the proposed MA is to solve the multicast routing problem by finding a multicast tree of paths from source to different destinations that offers optimal fitness value. This optimal fitness is the minimized value of the weighted combination between the optimization metrics (cost, delay, jitter and bandwidth) calculated by Eq (1). All experiments were implemented on MATLAB 2016a, with an Intel 1 Core TM i5-4200M CPU, and 6 GB RAM
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