Comparison of New and Old Optimization Algorithms for Traveling Salesman Problem on Small, Medium, and Large-scale Benchmark Instances

Md Al Amin Hossain; Züleyha Yılmaz Acar

Article Details

Comparison of New and Old Optimization Algorithms for Traveling Salesman Problem on Small, Medium, and Large-scale Benchmark Instances

Authors:

Md Al Amin Hossain | Züleyha Yılmaz Acar

Publisher:

Bitlis Eren University

Main Topics:

Artificial Intelligence (Other)

Journal:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 1, pp.216-231

Document Type:

Article

Publication Date:

3/2024

Document Language:

English

Document ID (Org No.):

2001313625

pISSN:

2147-3129

Keywords:

Abstract:

 The Traveling Salesman Problem TSP, a prominent combinatorial optimization issue, is the subject of this studys evaluation of the performance of new and old optimization techniques. This paper seeks to expand knowledge of optimization techniques and how they might be applied to solve TSP challenges. The goal of the research is to compare various algorithms scalability, convergence, and computation times on benchmark instances of several sizes. To achieve this goal, this paper carried out extensive testing using the Artificial Bee Colony ABC, Grey Wolf Optimization GWO, and Salp Swarm Algorithm SSA as new optimization algorithms and the Genetic Algorithm GA, Ant Colony Optimization ACO, and Simulated Annealing SA as old optimization algorithms. On small, medium, and large-scale benchmark cases, these algorithms were examined. The findings of this investigation show that the new optimization techniques are more convergent and scalable than the old ones, especially for medium-scale scenarios. They perform better performance in terms of solution quality by applying objective function values. The new methods also exhibit improved scalability, successfully adjusting to medium-scale instances. However, there were no discernible changes between the smaller and larger instances. This study makes an impact by offering insightful information about how well optimization methods perform while solving the TSP. Each algorithms strengths and downsides have been reported, and these details offer useful guidance for choosing an algorithm for a certain scenario. The results also show the practical ramifications of applying novel optimization techniques, especially in medium-scale instances..

 The Traveling Salesman Problem TSP, a prominent combinatorial optimization issue, is the subject of this studys evaluation of the performance of new and old optimization techniques. This paper seeks to expand knowledge of optimization techniques and how they might be applied to solve TSP challenges. The goal of the research is to compare various algorithms scalability, convergence, and computation times on benchmark instances of several sizes. To achieve this goal, this paper carried out extensive testing using the Artificial Bee Colony ABC, Grey Wolf Optimization GWO, and Salp Swarm Algorithm SSA as new optimization algorithms and the Genetic Algorithm GA, Ant Colony Optimization ACO, and Simulated Annealing SA as old optimization algorithms. On small, medium, and large-scale benchmark cases, these algorithms were examined. The findings of this investigation show that the new optimization techniques are more convergent and scalable than the old ones, especially for medium-scale scenarios. They perform better performance in terms of solution quality by applying objective function values. The new methods also exhibit improved scalability, successfully adjusting to medium-scale instances. However, there were no discernible changes between the smaller and larger instances. This study makes an impact by offering insightful information about how well optimization methods perform while solving the TSP. Each algorithms strengths and downsides have been reported, and these details offer useful guidance for choosing an algorithm for a certain scenario. The results also show the practical ramifications of applying novel optimization techniques, especially in medium-scale instances..

 The Traveling Salesman Problem TSP, a prominent combinatorial optimization issue, is the subject of this studys evaluation of the performance of new and old optimization techniques. This paper seeks to expand knowledge of optimization techniques and how they might be applied to solve TSP challenges. The goal of the research is to compare various algorithms scalability, convergence, and computation times on benchmark instances of several sizes. To achieve this goal, this paper carried out extensive testing using the Artificial Bee Colony ABC, Grey Wolf Optimization GWO, and Salp Swarm Algorithm SSA as new optimization algorithms and the Genetic Algorithm GA, Ant Colony Optimization ACO, and Simulated Annealing SA as old optimization algorithms. On small, medium, and large-scale benchmark cases, these algorithms were examined. The findings of this investigation show that the new optimization techniques are more convergent and scalable than the old ones, especially for medium-scale scenarios. They perform better performance in terms of solution quality by applying objective function values. The new methods also exhibit improved scalability, successfully adjusting to medium-scale instances. However, there were no discernible changes between the smaller and larger instances. This study makes an impact by offering insightful information about how well optimization methods perform while solving the TSP. Each algorithms strengths and downsides have been reported, and these details offer useful guidance for choosing an algorithm for a certain scenario. The results also show the practical ramifications of applying novel optimization techniques, especially in medium-scale instances..

 The Traveling Salesman Problem TSP, a prominent combinatorial optimization issue, is the subject of this studys evaluation of the performance of new and old optimization techniques. This paper seeks to expand knowledge of optimization techniques and how they might be applied to solve TSP challenges. The goal of the research is to compare various algorithms scalability, convergence, and computation times on benchmark instances of several sizes. To achieve this goal, this paper carried out extensive testing using the Artificial Bee Colony ABC, Grey Wolf Optimization GWO, and Salp Swarm Algorithm SSA as new optimization algorithms and the Genetic Algorithm GA, Ant Colony Optimization ACO, and Simulated Annealing SA as old optimization algorithms. On small, medium, and large-scale benchmark cases, these algorithms were examined. The findings of this investigation show that the new optimization techniques are more convergent and scalable than the old ones, especially for medium-scale scenarios. They perform better performance in terms of solution quality by applying objective function values. The new methods also exhibit improved scalability, successfully adjusting to medium-scale instances. However, there were no discernible changes between the smaller and larger instances. This study makes an impact by offering insightful information about how well optimization methods perform while solving the TSP. Each algorithms strengths and downsides have been reported, and these details offer useful guidance for choosing an algorithm for a certain scenario. The results also show the practical ramifications of applying novel optimization techniques, especially in medium-scale instances..

Article Details

Comparison of New and Old Optimization Algorithms for Traveling Salesman Problem on Small, Medium, and Large-scale Benchmark Instances

Authors: Md Al Amin Hossain | Züleyha Yılmaz Acar

Publisher: Bitlis Eren University

Main Topics: Artificial Intelligence (Other)

Journal: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 1, pp.216-231

Document Type: Article

Publication Date: 3/2024

Document Language: English

Document ID (Org No.): 2001313625

pISSN: 2147-3129

Artificial Bee Colony

Benchmark Instances

Optimization

Traveling Salesman Problem

Issues

Authors:

Md Al Amin Hossain | Züleyha Yılmaz Acar

Publisher:

Bitlis Eren University

Main Topics:

Artificial Intelligence (Other)

Journal:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 1, pp.216-231

Document Type:

Article

Publication Date:

3/2024

Document Language:

English

Document ID (Org No.):

2001313625

pISSN:

2147-3129