1762. Comparative Analysis of Swarm Intelligence and Predator-Prey Metaheuristics for Solving the Traveling Salesman Problem
Invited abstract in session WB-58: Heuristics for Vehicle Routing 1, stream Vehicle Routing and Logistics.
Wednesday, 10:30-12:00Room: Liberty 1.13
Authors (first author is the speaker)
| 1. | Mateusz Turek
|
| Chair of Transportation Systems, Cracow University of Technology |
Abstract
The Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem with significant applications in logistics, routing, and manufacturing. Due to its complexity, swarm intelligence-based metaheuristic approaches have been widely explored to find near-optimal solutions efficiently. This study focuses on analyzing the performance of Bee-Inspired Optimization Algorithm (BIA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) in solving TSP instances. Additionally, the Predator-Prey and Hunting-Based Algorithm is included for comparative evaluation - Discrete Grey Wolf Optimization (D-GWO). The analysis is conducted based on solution quality, computational efficiency, and convergence behavior on standard benchmark datasets using TSPLIB, a commonly used and widely available Python library for benchmarking TSP-solving algorithms.
Keywords
- Vehicle Routing
- Logistics
- Artificial Intelligence
Status: accepted
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