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Optimizing the travelling salesman problem : a genetic algorithm enhanced with Tabu search. 6

By: Suzzette Ann H. Latoza, Maricel L. Arcega. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 4535246Edition: Description: Content type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: ABSTRACT: The provoting of the logistics industry caused by the pandemic surely raised the pressure on delivery providers to resolve decreasing the travel time while still having optimal ways to deliver each parcel. Traveling salesman problem (TSP) is a common problem encountered in the logistics industry. It is a common problem encountered in the logistics industry. It is known that the traveling salesman needs to choose the most efficient route to travel all of the locations and return to the original city where the salesman started. The study aims to improve the quality of solutions for TSP instances by leveraging the complimentary strength of the Genetic Algorithm (GA) and Tabu Search (TS). Genetic algorithms are commonly used for optimization that stimulates searching that are observed in natural evolution. The main objective of the study is to enhance the genetic algorithm in order to further optimize the routing problem present in TSP and still be able to present efficient and optimal results. Specifically, the study focuses on the algorithmic enhancement, solution quality improvement, and convergence speed of the offspring producing suboptimal solutions using Tabu search. The results show that the existing algorithm still produces the same optimal solution and its convergence rate and RMSE remains an inconsistency that needs improvement. Moreover, the proposed algorithm produces multiple optimal solutions compared to the current method. The proposed algorithm also shows great reliability in its solutions. This output demonstrates the algorithm's ability to produce consistent despite variations in convergence rate. Other editions:

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Undergraduate Thesis : (Bachelor of Science major in Computer Science) - Pamantasan Lungsod ng Maynila, 2024. 56

ABSTRACT: The provoting of the logistics industry caused by the pandemic surely raised the pressure on delivery providers to resolve decreasing the travel time while still having optimal ways to deliver each parcel. Traveling salesman problem (TSP) is a common problem encountered in the logistics industry. It is a common problem encountered in the logistics industry. It is known that the traveling salesman needs to choose the most efficient route to travel all of the locations and return to the original city where the salesman started. The study aims to improve the quality of solutions for TSP instances by leveraging the complimentary strength of the Genetic Algorithm (GA) and Tabu Search (TS). Genetic algorithms are commonly used for optimization that stimulates searching that are observed in natural evolution. The main objective of the study is to enhance the genetic algorithm in order to further optimize the routing problem present in TSP and still be able to present efficient and optimal results. Specifically, the study focuses on the algorithmic enhancement, solution quality improvement, and convergence speed of the offspring producing suboptimal solutions using Tabu search. The results show that the existing algorithm still produces the same optimal solution and its convergence rate and RMSE remains an inconsistency that needs improvement. Moreover, the proposed algorithm produces multiple optimal solutions compared to the current method. The proposed algorithm also shows great reliability in its solutions. This output demonstrates the algorithm's ability to produce consistent despite variations in convergence rate.

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