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Remora Optimization Algorithm-based Adaptive Fusion via Ant Colony Optimization for Traveling Salesman Problem

Lin Piao¹

1. Department of Education, Liaoning National Normal College
   No. 45, Chongdong Road, Huanggu District, Shenyang 110032 China
   pllnco@163.com

Abstract

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.

Key words

TSP, ACO, Remora optimization algorithm, K-means clustering, Adaptive fusion

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS240314052P

Publication information

Volume 21, Issue 4 (September 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Piao, L.: Remora Optimization Algorithm-based Adaptive Fusion via Ant Colony Optimization for Traveling Salesman Problem. Computer Science and Information Systems, Vol. 21, No. 4, 1651–1672. (2024), https://doi.org/10.2298/CSIS240314052P