A Comparative Study of Optimization Algorithms: Ant Colony Optimization vs Simulated Annealing
Session
Computer Science and Communication Engineering
Description
This paper presents a comparative study of two prominent optimization algorithms, Ant Colony Optimization (ACO) and Simulated Annealing (SA), specifically in the context of solving the Traveling Salesman Problem (TSP). The TSP is a well-known combinatorial optimization problem that looks for the quickest path to travel between a set of cities exactly once and back to the starting point. ACO uses pheromone trails to gradually converge towards the best path while exploring potential solutions. It is inspired by the foraging behavior of ants. On the other hand, SA uses a probabilistic method to break out from local minima and arrive at a close to ideal solution, taking inspiration from the annealing process in metallurgy. Both algorithms are used in the study on different TSP instances in order to assess how well they perform in terms of computational efficiency, convergence speed, and solution quality. The outcomes of the experiments show that although SA exhibits strong convergence properties for smaller instances of TSP, ACO efficiently explores complex solution spaces and frequently produces better results. By highlighting the benefits and drawbacks of each algorithm, this study helps practitioners and researchers choose the best optimization strategies for the TSP. In the end, this research adds to the continuing conversation about metaheuristic algorithms and how to use them to solve optimization problems in the real world, especially in the areas of logistics and route planning.
Keywords:
traveling salesman problem, optimization algorithms, route planning.
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-982-15-3
Location
UBT Kampus, Lipjan
Start Date
25-10-2024 9:00 AM
End Date
27-10-2024 6:00 PM
DOI
10.33107/ubt-ic.2024.385
Recommended Citation
Novoberdaliu, Alma; Domi, Jeta; Rexhepi, Rreze; and Hajrizi, Edmond, "A Comparative Study of Optimization Algorithms: Ant Colony Optimization vs Simulated Annealing" (2024). UBT International Conference. 1.
https://knowledgecenter.ubt-uni.net/conference/2024UBTIC/CS/1
A Comparative Study of Optimization Algorithms: Ant Colony Optimization vs Simulated Annealing
UBT Kampus, Lipjan
This paper presents a comparative study of two prominent optimization algorithms, Ant Colony Optimization (ACO) and Simulated Annealing (SA), specifically in the context of solving the Traveling Salesman Problem (TSP). The TSP is a well-known combinatorial optimization problem that looks for the quickest path to travel between a set of cities exactly once and back to the starting point. ACO uses pheromone trails to gradually converge towards the best path while exploring potential solutions. It is inspired by the foraging behavior of ants. On the other hand, SA uses a probabilistic method to break out from local minima and arrive at a close to ideal solution, taking inspiration from the annealing process in metallurgy. Both algorithms are used in the study on different TSP instances in order to assess how well they perform in terms of computational efficiency, convergence speed, and solution quality. The outcomes of the experiments show that although SA exhibits strong convergence properties for smaller instances of TSP, ACO efficiently explores complex solution spaces and frequently produces better results. By highlighting the benefits and drawbacks of each algorithm, this study helps practitioners and researchers choose the best optimization strategies for the TSP. In the end, this research adds to the continuing conversation about metaheuristic algorithms and how to use them to solve optimization problems in the real world, especially in the areas of logistics and route planning.