A Comparative Analysis of Optimization Heuristics Algorithms as Optimal Solution for Travelling Salesman Problem

Ajayi, Binyamin Adeniyi; Magaji, Mohammed Abubakar; Musa, Samaila; Olanrewaju, Rashidah Funke; Salihu, Abdullahi Audu

doi:10.1109/ited56637.2022.10051627

Cited by 1 publication

(1 citation statement)

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“…Middle-scale TSP situations need faster solving. Ajayi et al [6] sought TSP optimization methods. ACO was compared to Dijkstra's method and particle swarm optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Hossain,

Yılmaz Acar

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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The Traveling Salesman Problem (TSP), a prominent combinatorial optimization issue, is the subject of this study's 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 algorithm's 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..

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“…Middle-scale TSP situations need faster solving. Ajayi et al [6] sought TSP optimization methods. ACO was compared to Dijkstra's method and particle swarm optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Hossain,

Yılmaz Acar

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

A Comparative Analysis of Optimization Heuristics Algorithms as Optimal Solution for Travelling Salesman Problem

Cited by 1 publication

References 5 publications

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

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

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