Survey Paper on Routing in MANETs for Optimal Route Selection Based on Routing Protocol with Particle Swarm Optimization and Different Ant Colony Optimization Protocol

Rathi, Priti Swapnil; Rao, Chung-Hwa Herman

doi:10.1007/978-981-13-9282-5_51

Cited by 5 publications

(1 citation statement)

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“…Good paths, after a while, obtain high values of the QoS measures (pheromone) as a result of the passage of more ant agents. Abandoned paths have low pheromone values due to a pheromone evaporation rate that is adjusted in the algorithm [17]. Ant agents also have an important task of discovering new good routes as an alternative to the best discovered ones.…”

Section: Introductionmentioning

confidence: 99%

Tuning the Evaporation Parameter in ACO MANET Routing Using a Satisfaction-Form Game-Theoretic Approach

2022

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A Mobile Ad Hoc Network (MANET) is a communication network that links communicating devices (nodes) and does not contain permanent infrastructure. There are no dedicated routing devices in MANETs, where the routing task is assigned to a routing algorithm installed on all communicating nodes. In this work, communicating nodes utilize one of the most widely used algorithms: Ant Colony Optimization (ACO) routing algorithms. The ACO algorithms aim to balance between exploring new routes for the communication packets vs. utilizing the best-known routes discovered during the communication session. Achieving the optimality in this tradeoff is traditionally set manually by assigning many values to some parameters and measuring the network performance after the simulation session. This manual optimality tuning approach depends on human intuition and does not cope with MANET's dynamic topology. In this research, we introduce a novel method to find an optimal balance for the exploration-exploitation tradeoff during the communication session. We formulate weighing the benefits of exploring new routes vs. exploiting known ones upon the MANET performance as a game between the two semantic players. This equilibrium is reflected as an optimal value for the pheromone evaporation parameter of the ACO algorithm during the communication session. Experimental results show a higher performance of this online tuning algorithm than the traditional offline tuning algorithms.

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Section: Introductionmentioning

confidence: 99%