NDLI: A new genetic algorithm based on modified Physarum network model for bandwidth-delay constrained least-cost multicast routing

Content Provider	Springer Nature Link
Author	Liang, Mingxin Gao, Chao Zhang, Zili
Copyright Year	2016
Abstract	A mobile ad hoc network is a kind of popular self-configuring network, in which multicast routing under the quality of service constraints, is a significant challenge. Many researchers have proved that such problem can be formulated as a NP-complete problem and proposed some swarm-based intelligent algorithms to solve the optimal solution, such as the genetic algorithm (GA), bees algorithm. However, a lower efficiency of local search ability and weak robustness still limit the computational effectiveness. Aiming to those shortcomings, a new hybrid algorithm inspired by the self-organization of Physarum, is proposed in this paper. In our algorithm, an updating scheme based on Physarum network model (PM) is used for improving the crossover operator of traditional GAs, in which the same parts of parent chromosomes are reserved and the new offspring by the PM is generated. In order to estimate the effectiveness of our proposed optimized scheme, some typical genetic algorithms and their updating algorithms (PMGAs) are compared for solving the multicast routing on four different datasets. The simulation experiments show that PMGAs are more efficient than original GAs. More importantly, the PMGAs are more robustness that is very important for solving the multicast routing problem. Moreover, a series of parameter analyses is used to find a set of better setting for realizing the maximal efficiency of our algorithm.
Starting Page	85
Ending Page	98
Page Count	14
File Format	PDF
ISSN	15677818
Journal	Natural Computing
Volume Number	16
Issue Number	1
e-ISSN	15729796
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-03-29
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multicast routing Genetic algorithm Physarum network model Theory of Computation Evolutionary Biology Processor Architectures Artificial Intelligence (incl. Robotics) Complex Systems
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications

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