TAntNet-4: A Threshold-Based AntNet Algorithm with Improved Scout Behavior

TAntNet-4: A Threshold-Based AntNet Algorithm with Improved Scout Behavior

Ayman M. Ghazy (Cairo University, Egypt) and Hesham A. Hefny (Cairo University, Egypt)
Copyright: © 2020 |Pages: 32
DOI: 10.4018/978-1-7998-1754-3.ch044

Abstract

Traffic Routing System (TRS) is one of the most important intelligent transport systems which is used to direct vehicles to good routes and reduce congestion on the road network. The performance of TRS mainly depends on a dynamic routing algorithm due to the dynamic nature of traffic on road network. AntNet algorithm is a routing algorithm inspired from the foraging behavior of ants. TAntNet is a family of dynamic routing algorithms that uses a threshold travel time to enhance the performance of AntNet algorithm when applied to traffic road networks. TAntNet-1 and TAntNet-2 adopt different techniques for path update to fast direct to the discovered good route and conserve on this good route. TAntNet-3 has been recently proposed by inspiring the scout behavior of bees to avoid the bad effect of forward ants that take bad routes. This chapter presents a new member in TAntNet family of algorithms called TAntNet-4 that uses two scouts instead of one compared with TAntNet-2. The new algorithm also saves the discovered route of each of the two scouts to use the best of them by the corresponding backward ant. The experimental results ensure the high performance of TAntNet-4 compared with AntNet, other members of TAntNet family.
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Ant routing algorithms is one of the most promising swarm intelligence (SI) methodologies that are studied in many researches (Di Caro & Dorigo, 1998; Kassabalidis et al., 2002; Kroon & Rothkrantz, 2003; Suson, 2010; Claes, R., & Holvoet 2012; Jabbarpouret al., 2014a; Yousefi & Zamani, 2013 ; Ghazy & Hefny, 2014, Jabbarpour et al., 2014b and Girme, 2015).

AntNet algorithm appears in 1998 by Di Caro & Dorigo (1998) for routing of data communication network. The algorithm with its distributed multi agent characteristics, attracted many researchers to adopt the algorithm in both data communication network and road traffic network.

It has been shown that under varying traffic loads of data networks, AntNet algorithm represents better performance than that of Dijkstra's shortest path algorithm (DhillonVan & Mieghem, 2007). Also it has been it has been shown in Kiruthika and Kalyanasundaram (2015) that, AntNet algorithm gives better results comparing with Ad hoc on demand Multipath Distance Vector algorithm. Many improvements have been proposed to the AntNet algorithm. Baran and Sosa (2001) presented a modified algorithm that initialize the routing table with data that reflects previous knowledge, about network topology rather than the presumption of uniform probabilities distribution given in original AntNet algorithm. Tekineret al. (2004) proposed a new version of the AntNet algorithm that utilized the ant/packet ratio to limit the number of used ants. Soltani et al. (2006) introduce a new type of ants called helping ants to increase cooperation among neighboring nodes, thereby reducing AntNet algorithm’s convergence time. Gupta et al. (2012) presented a study for computation of the pheromone values in AntNet. Radwan et al. (2011) introduced a modified AntNet with blocking–expanding ring search and local retransmission technique for routing of Mobile ad hoc network (MANET). Sharma et al. (2013) showed that load balancing is successfully fulfilled for ant based techniques.

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