Abstract
Ant colony optimization, a swarm intelligence technique, inspired by the foraging behavior of ants in colonies was used in the past research works to compute the optimal path. The existing works of routing using ant colony optimization of MANETS face challenges in load balancing and energy efficiency. The proposed A-EEBLR approach chooses the next hop node based on metrics like delay, energy drain rate, congestion, link quality. Based on these metrics the probability of choosing next hop node as neighbor node is determined. The next hop probability determines the forward and backward ant agents to establish multiple paths among which the most optimal path is selected for transmission. The implementation results shows that the proposed A-EEBLR approach outperforms the existing A-ESR approach when evaluated by varying the number of packets, number of nodes and node mobility.
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