Route Optimization of Airplane Travel Plans Using the Tabu-Simulated Annealing Algorithm to Solve the Traveling Salesman Challenge 2.0

Ahmad, Edwin Dwi; Muklason, Ahmad; Nurkasanah, Ika

doi:10.1109/cenim51130.2020.9297892

Cited by 4 publications

(8 citation statements)

References 12 publications

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“…To see the performance of the developed algorithm, a comparison was made with previous studies. The previous study [25] A large number of explorations runs has a more significant effect on large datasets. Large datasets will have more extensive search space solutions.…”

Section: Comparison With Previous Studiesmentioning

confidence: 91%

“…The algorithm was found to be superior to several previous studies and discovered new best-known solutions. Another hybrid algorithm was developed in 2020 by combining the TS and SA algorithms [25]. The SA algorithm served as the primary method for finding solutions while the TS algorithm was used to increase diversity for low-level heuristic (LLH).…”

Section: Related Workmentioning

confidence: 99%

“…The first is the number of iterations. Research [25] which is used as a comparison, runs the algorithm with parameter settings equivalent to 46,051,691 iterations. This research uses parameter settings designed to get the same number of iterations to get a fair comparison.…”

Section: Parameter Settingmentioning

confidence: 99%

“…This algorithm is intended to solve one of the TSP problems, namely the Traveling Salesman Challenge 2.0 (TSC 2.0) case study. The results of the algorithm development show out-performing results from Ahmad et al [25], which used a hybridization algorithm between simulated annealing and Tabu search (TS).…”

Section: Introductionmentioning

confidence: 98%

“…The ILS algorithm has been proven to produce promising solutions for various kinds of optimization problems, such as timetabling [11]- [13], vehicle routing problems [14]- [16], and TSP [17], [18]. Meanwhile, the SA algorithm also shows promising results, such as in the fields of bin packing [19], timetabling [20]- [22], and TSP [23]- [25]. Meanwhile, the HC algorithm is widely used in the development of hybridization algorithms to improve the final results of the optimization process [26].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Hybrid iterated local search algorithm for optimization route of airplane travel plans

Muklason

Premananda

2023

IJECE

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<span lang="EN-US">The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the Tabu-SA algorithm.</span>

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Section: Comparison With Previous Studiesmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Section: Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Hybrid iterated local search algorithm for optimization route of airplane travel plans

Muklason

Premananda

2023

IJECE

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Navigating Intelligence: A Survey of Google OR‐Tools and Machine Learning for Global Path Planning in Autonomous Vehicles

Benoit,

Asef

2024

Advanced Intelligent Systems

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We offer a new in‐depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining samplingrobot named ROMIE. GPP is essential for ROMIE's optimal performance, which istranslated into solving the traveling salesman problem, a complexgraph theory challenge that is crucial for determining the most effective routeto cover all sampling locations in a mining field. This problem is central toenhancing ROMIE's operational efficiency and competitiveness against humanlabor by optimizing cost and time. The primary aim of this research is toadvance GPP by developing, evaluating, and improving a cost‐efficient softwareand web application. We delve into an extensive comparison and analysis of Google operations research (OR)‐Tools optimization algorithms. Our study is driven by the goal of applyingand testing the limits of OR‐Tools capabilities by integrating ReinforcementLearning techniques for the first time. This enables us to compare thesemethods with OR‐Tools, assessing their computational effectiveness andreal‐world application efficiency. Our analysis seeks to provide insights intothe effectiveness and practical application of each technique. Our findingsindicate that Q‐Learning stands out as the optimal strategy, demonstratingsuperior efficiency by deviating only 1.2% on average from the optimalsolutions across our datasets.

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Investigation of a Genetic Algorithm for Solving the Travelling Salesman Problem

Pyrih,

Masiuk,

Pyrih

et al. 2024

Lecture Notes in Electrical Engineering

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Route Optimization of Airplane Travel Plans Using the Tabu-Simulated Annealing Algorithm to Solve the Traveling Salesman Challenge 2.0

Cited by 4 publications

References 12 publications

Hybrid iterated local search algorithm for optimization route of airplane travel plans

Hybrid iterated local search algorithm for optimization route of airplane travel plans

Navigating Intelligence: A Survey of Google OR‐Tools and Machine Learning for Global Path Planning in Autonomous Vehicles

Investigation of a Genetic Algorithm for Solving the Travelling Salesman Problem

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