An Enhanced Bio-Inspired Optimization Approach for Solving Path Selection Challenges in Network System

Authors

  • Qiang Liu

Keywords:

transportation network analysis, shortest path, Artificial Fish Swarm Algorithm

Abstract

The shortest path problem stands as a critical issue in transportation network analysis, serving as the foundation for numerous optimization tasks such as resource allocation, route design, and traffic management. This paper conducts an in-depth analysis of the basic Artificial Fish Swarm Algorithm (AFSA) and identifies its limitations in terms of accuracy and processing time when solving the shortest path problem between two points in a traffic network. To address these drawbacks, an improved AFSA is proposed, which optimizes the initialization of artificial fish populations and modifies their behaviors. Simulation experiments demonstrate that the improved algorithm can find the shortest path between any two points in a traffic network more accurately and efficiently compared to the original AFSA, verifying its feasibility and superiority in practical applications.

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Published

2025-08-25

How to Cite

Liu, Q. (2025). An Enhanced Bio-Inspired Optimization Approach for Solving Path Selection Challenges in Network System. Artificial Intelligence and Internet Studies, 1(1), 9–16. Retrieved from https://www.focuscholar.com/journal/index.php/aiis/article/view/27