ADAPTIVE COST-AWARE SHORTEST PATH OPTIMIZATION IN ELECTRONIC AND TRANSPORTATION NETWORKS: A COMPARATIVE STUDY WITH FLOYD–WARSHALL AND FORD–FULKERSON

Abstract

This study addresses a key challenge in modern networks corresponding to finding the optimal path. We first examined classical algorithms like Floyd–Warshall and Ford–Fulkerson but found them limited in scalability, flexibility, and real-time adaptability. To overcome these constraints, we developed an adaptive routing method namely ACASPO (Adaptive Cost-Aware Shortest Path Optimization) that integrates real-time updates and intelligent navigation. Simulations across diverse network structures show our approach delivers greater path efficiency with lower computational cost, particularly in large-scale or dynamic settings. Our work provides a practical, context-aware framework that connects classical theory with contemporary needs for responsive and efficient routing.