Remora optimization algorithm-based adaptive fusion via ant colony optimization for traveling salesman problem
The traditional ant colony optimization (ACO) is easy to fall into local optimal when solving large-scale traveling salesman problem (TSP), and the convergence speed is slow. In order to enhance the local search ability of ACO, speed up the efficiency of ACO and avoid the premature problem, this pap...
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| Vydáno v: | Computer Science and Information Systems Ročník 21; číslo 4; s. 1651 - 1672 |
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| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
01.09.2024
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| ISSN: | 1820-0214, 2406-1018 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | The traditional ant colony optimization (ACO) is easy to fall into local optimal when solving large-scale traveling salesman problem (TSP), and the convergence speed is slow. In order to enhance the local search ability of ACO, speed up the efficiency of ACO and avoid the premature problem, this paper proposes a novel remora optimization algorithm-based adaptive fusion via ant colony optimization for solving TSP. Firstly, an improved K-means clustering method is used to obtain the best clustering results and the optimal solutions of each class quickly by adaptive clustering strategy based on the maximum and minimum distance and class density. By using an improved Remora optimization algorithm, adjacent classes are fused to effectively improve the accuracy of the initial solution. In addition, the initial solution is optimized by the k-opt strategy. Finally, the random recombination strategy is used to recombine the pheromone and random excitation to make the algorithm jump out of the local optimal as far as possible and improve the accuracy of the algorithm. The experimental results show that the proposed algorithm not only guarantees the accuracy of solution, but also improves the stability when solving large-scale TSP. |
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| ISSN: | 1820-0214 2406-1018 |
| DOI: | 10.2298/CSIS240314052P |