A new local search for the bees algorithm to optimize multiple traveling salesman problem
This paper presents a new local search operator with the Bees Algorithm (BA) to solve the Multiple Traveling Salesman Problem (MTSP), which is a kind of combinatorial optimization problem. Many algorithms and techniques were used to solve this problem; however, they could not find the optimal soluti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-05-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305323000674 |
| _version_ | 1797804553158000640 |
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| author | Ali Hamza Ahmed Haj Darwish Omar Rihawi |
| author_facet | Ali Hamza Ahmed Haj Darwish Omar Rihawi |
| author_sort | Ali Hamza |
| collection | DOAJ |
| description | This paper presents a new local search operator with the Bees Algorithm (BA) to solve the Multiple Traveling Salesman Problem (MTSP), which is a kind of combinatorial optimization problem. Many algorithms and techniques were used to solve this problem; however, they could not find the optimal solutions. Some of those algorithms also have high computational costs. In this paper, the Bees Algorithm is adopted to form a robust optimizer with low execution time. The proposed method uses local search operators to achieve that. These operators are used to exploit the neighbors for local solutions. Many local search operators are described thoroughly. Two operators are used with the Bees Algorithm (BA). Then, a new local search operator called SBESTSO is proposed to improve the performance of the Algorithm. The efficiency of the Bees Algorithm with the new local search operator was evaluated using different MTSP benchmark datasets. The results obtained were compared with those of other optimization algorithms for the same datasets. The comparisons proved the robustness of the Bees Algorithm in finding optimal solutions of Multiple Traveling Salesman Problems. |
| first_indexed | 2024-03-13T05:38:55Z |
| format | Article |
| id | doaj.art-bf0210fc41c648b4b61cb9ce7b3976bc |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-13T05:38:55Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-bf0210fc41c648b4b61cb9ce7b3976bc2023-06-14T04:34:56ZengElsevierIntelligent Systems with Applications2667-30532023-05-0118200242A new local search for the bees algorithm to optimize multiple traveling salesman problemAli Hamza0Ahmed Haj Darwish1Omar Rihawi2Corresponding author.; Dept. Artificial Intelligence and Natural Languages, Faculty of Informatics Engineering, University of Aleppo, Aleppo, SyriaDept. Artificial Intelligence and Natural Languages, Faculty of Informatics Engineering, University of Aleppo, Aleppo, SyriaDept. Artificial Intelligence and Natural Languages, Faculty of Informatics Engineering, University of Aleppo, Aleppo, SyriaThis paper presents a new local search operator with the Bees Algorithm (BA) to solve the Multiple Traveling Salesman Problem (MTSP), which is a kind of combinatorial optimization problem. Many algorithms and techniques were used to solve this problem; however, they could not find the optimal solutions. Some of those algorithms also have high computational costs. In this paper, the Bees Algorithm is adopted to form a robust optimizer with low execution time. The proposed method uses local search operators to achieve that. These operators are used to exploit the neighbors for local solutions. Many local search operators are described thoroughly. Two operators are used with the Bees Algorithm (BA). Then, a new local search operator called SBESTSO is proposed to improve the performance of the Algorithm. The efficiency of the Bees Algorithm with the new local search operator was evaluated using different MTSP benchmark datasets. The results obtained were compared with those of other optimization algorithms for the same datasets. The comparisons proved the robustness of the Bees Algorithm in finding optimal solutions of Multiple Traveling Salesman Problems.http://www.sciencedirect.com/science/article/pii/S2667305323000674Bees algorithmBACombinatorial optimization problemMultiple traveling salesman problemMTSPLocal operators |
| spellingShingle | Ali Hamza Ahmed Haj Darwish Omar Rihawi A new local search for the bees algorithm to optimize multiple traveling salesman problem Intelligent Systems with Applications Bees algorithm BA Combinatorial optimization problem Multiple traveling salesman problem MTSP Local operators |
| title | A new local search for the bees algorithm to optimize multiple traveling salesman problem |
| title_full | A new local search for the bees algorithm to optimize multiple traveling salesman problem |
| title_fullStr | A new local search for the bees algorithm to optimize multiple traveling salesman problem |
| title_full_unstemmed | A new local search for the bees algorithm to optimize multiple traveling salesman problem |
| title_short | A new local search for the bees algorithm to optimize multiple traveling salesman problem |
| title_sort | new local search for the bees algorithm to optimize multiple traveling salesman problem |
| topic | Bees algorithm BA Combinatorial optimization problem Multiple traveling salesman problem MTSP Local operators |
| url | http://www.sciencedirect.com/science/article/pii/S2667305323000674 |
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