A memetic optimization algorithm for multi-constrained multicast routing in ad hoc networks.

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

A memetic optimization algorithm for multi-constrained multicast routing in <i>ad hoc</i> networks

<div><p>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.</p></div

Figs. 3a. and 3b. show the cost and delay comparisons in terms of the mean, min, and variance of the PM-EEGA and PM_ISGSA GAs and MA on the first dataset.

<p>Figs. 3a. and 3b. show the cost and delay comparisons in terms of the mean, min, and variance of the PM-EEGA and PM_ISGSA GAs and MA on the first dataset.</p

Analysis of variance for the 50 nodes network.

<p>Analysis of variance for the 50 nodes network.</p

Analysis of variance for the 15 nodes network.

<p>Analysis of variance for the 15 nodes network.</p

The proposed algorithm settings.

<p>The proposed algorithm settings.</p

MANET example.

<p>MANET example.</p

A comparison of optimal fitness solutions in generations (iteration times).

<p>A comparison of optimal fitness solutions in generations (iteration times).</p

The optimal fitness solutions identified by applying different algorithms on the second dataset.

<p>The optimal fitness solutions identified by applying different algorithms on the second dataset.</p

Analysis of variance for the 20 nodes network.

<p>Analysis of variance for the 20 nodes network.</p