A heuristic ant algorithm for solving QoS multicast routing problem

Chu, Chih‐Ping; Gu, Jun; Hou, Xiang; Gu, Qijun

doi:10.1109/cec.2002.1004486

Cited by 25 publications

(2 citation statements)

References 14 publications

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“…In some existed heuristic algorithms, the Ant Colony optimization Algorithm (ACA) has much wider applications for its robustness, parallelism, flexibility, self-searching and the high accuracy [7]. Researchers have succeeded in applying the ACA to solve Quality of Service (QoS)-constrained multicast problem [8][9][10]. [7] has given the strict proof of convergence of ACA under some basic assumptions.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with [8][9][10] which those ACA according to the driving by source node, the improved ACA is based on the group member nodes drive. The detailed algorithm is: the ants are generated by nodes in each group, and then they begin to migrate to the source node and record the reverse path information in the migration routing.…”

Section: B the Process Of Improved Acamentioning

confidence: 99%

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An Improved Routing Technology Based on Ant Colony Algorithm for Indoor Building Environment

Zhang

Song

2013

AMR

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Since Wireless Sensor Networks (WSNs) and applications are closely related, with the application environment from the traditional outdoor environment gradually extended to the indoor environment, it will set a higher request to WSN and the routing algorithm. The traditional Ant Colony Algorithm (ACA) converges with a slow speed in its initial phase, so a location-aware ACA is proposed which introduces geographical information into standard ACA. The new algorithm adds direction factor into the path selection probability function which can make the ants move faster to the source by getting rid of the blindness in early path selection. Experimental results show that the location-aware ACA make great improvements in both convergence speed and running time.

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