Particle Swarm Optimization for Realizing Intelligent Routing in Networks with Quality Grading

TL;DR

This paper introduces a two-level node selection method combined with Particle Swarm Optimization for improved routing in networks, utilizing node grading and local knowledge to reduce iterations and enhance efficiency.

Contribution

It proposes a novel grade-based two-level node selection approach integrated with PSO for optimized network routing, emphasizing intelligent node knowledge and grading.

Findings

Significant reduction in iterations for optimal path discovery

Effective routing in various network topologies

Enhanced efficiency over traditional methods

Abstract

Significant research has been carried out in the recent years for generating systems exhibiting intelligence for realizing optimized routing in networks. In this paper, a grade based twolevel based node selection method along with Particle Swarm Optimization (PSO) technique is proposed. It assumes that the nodes are intelligent and there exist a knowledge base about the environment in their local memory. There are two levels for approaching the effective route selection process through grading. At the first level, grade based selection is applied and at the second level, the optimum path is explored using PSO. The simulation has been carried out on different topological structures and it is observed that a graded network produces a significant reduction in number of iteration to arrive at the optimal path selection.