CONGESTION MINIMIZATION IN COMMUNICATION NETWORKS USING GENETIC ALGORITHM AND BINARY PARTICLE SWARM OPTIMIZATION

Nowadays, Communication networks play an indispensable role and it is possible to model these networks through different approaches and studying different aspects of these models. Here, we try to focus on optimizing the performance of communication networks, which consist of routers as nodes and edges as links between routers. Effectiveness of networks depends upon numerous factors such as routing protocol, router technology, link types and congestion. In this paper, we exploit a congestion function to represent the extent of congestion on entire networks, then minimizing growth rate of this function using meta-heuristic methods such as genetic algorithm and binary particle swarm optimization (BPSO). Consequently, utilizing binary particle swarm optimization and genetic algorithm reveals more optimized network topologies than simulated annealing – what is employed beforehand in literatures – in practical time.

Keywords:

Communication network , congestion , meta-heuristic optimization algorithms , genetic algorithm , binary particle swarm optimization