An Enhanced Bio-Inspired Optimization Approach for Solving Path Selection Challenges in Network System
Keywords:
transportation network analysis, shortest path, Artificial Fish Swarm AlgorithmAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Under the terms of this license, you are free to:
-
Share — copy and redistribute the material in any medium or format.
-
Adapt — remix, transform, and build upon the material for any purpose, including commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Full License Terms:
For the complete legal code and detailed terms, please visit https://creativecommons.org/licenses/by/4.0/legalcode.