A Comparative Analysis for Determining the Optimal Path using PSO and GA

TL;DR

This paper compares particle swarm optimization and genetic algorithms for finding optimal network paths, highlighting PSO's faster convergence and introducing region-based networks with indirect encoding.

Contribution

It presents a comparative analysis of PSO and GA for path optimization, incorporating region-based networks and indirect encoding techniques.

Findings

PSO converges faster than GA to optimal paths.

Fitness value and hop count are effective metrics in both algorithms.

Region-based networks enhance pathfinding efficiency.

Abstract

Significant research has been carried out recently to find the optimal path in network routing. Among them, the evolutionary algorithm approach is an area where work is carried out extensively. We in this paper have used particle swarm optimization (PSO) and genetic algorithm (GA) for finding the optimal path and the concept of region based network is introduced along with the use of indirect encoding. We demonstrate the advantage of fitness value and hop count in both PSO and GA. A comparative study of PSO and genetic algorithm (GA) is carried out, and it was found that PSO converged to arrive at the optimal path much faster than GA.