Using The Hybrid GA-Ant Algorithm To Find The Optimal Path In Computer Networks

Turki, Ibtisam Kareem

doi:10.33899/csmj.2022.174416

	Using The Hybrid GA-Ant Algorithm To Find The Optimal Path In Computer Networks
AL-Rafidain Journal of Computer Sciences and Mathematics
Article 15, Volume 16, Issue 1, 2022, Pages 121-129 PDF (677.99 K)
Document Type: Research Paper
DOI: 10.33899/csmj.2022.174416
Author
Ibtisam Kareem Turki^*
College of Administration and Economics/Marketing Department/University of Mosul/Iraq/Mosul
Abstract
Cost management is one of the performance standards in computer networks and routing strategies through which we can get effective paths in the computer network, reach the target and perform highly in the network by improving the routing table (jumps). This paper is an attempt to propose a new H design mixed algorithm (ACO-GA) that includes the best features of both ACO and GA with a new application that combines both previous algorithms called( H- Hybrid (ACO-GA) hybrid algorithm technology, which differs in its parameters. In order to research and find the optimal path, the improved ant algorithm was used to explore the network, using smart beams, getting the paths generated by ants and then using them as inputs into the genetic algorithm in the form of arranged pairs of chromosomes. Experimental results through extensive simulations showed that H (ACO-GA) improves the routing schedule, represented by the pheromone values that ants leave when following their path in the network. The values given in the table( 3.2) vary according to the quality of the pheromone concentration. In this case, it is possible to give the greatest opportunity to choose the best quality according to the concentration of the pheromone. For this purpose, a network consisting of four nodes (1), (2), (3), (4) was used starting with node (1) which is the source node and the destination node (2), by calling the selection technique to update the pheromone table by choosing the path to node (1 ). For this case and for selecting the destination node (2), the pheromone table for the nodes visited by the ant is updated. We calculated the final destination )2) by dividing the ratio. Thus, we get to reduce the search area, speed up search time, and improve the quality of the solution by obtaining the optimum set of paths.
Keywords
Ants Colony Optimization (ACO); Genetic Algorithm (GA); Routing Table; Swarm Intelligence (SI)

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