Akademik Veri Yönetim Sistemi
Neural Computing and Applications, cilt.38, sa.2, 2026 (Scopus)
The Close-Enough Traveling Salesman Problem (CETSP) is a complex variant of the classical TSP in which each target is represented by a disk-shaped neighborhood, and a visit is considered valid if any point within that neighborhood is reached. This formulation provides a more realistic model for real-world applications such as surveillance, wireless sensor routing, and Unmanned Aerial Vehicle (UAV) path planning. However, due to its NP-hard nature, solving large-scale CETSP instances efficiently remains a significant challenge. The main objective of this study is to develop scalable and robust Parallel Memetic Algorithms (PMAs) that enhance computational efficiency and solution quality for large CETSP instances. Two complementary strategies are proposed: (i) a Multi-Population PMA that maintains diversity through distributed crossover and parallel local optimization, and (ii) a Single-Population, Multi-Fitness PMA that accelerates convergence via simultaneous parallel fitness evaluations. Extensive experiments conducted on 42 benchmark instances demonstrate that the proposed algorithms outperform state-of-the-art approaches, achieving 21 new best-known solutions and up to a 10–15 times reduction in the execution times compared to the sequential MA-CETSP algorithm. The results confirm that the Multi-Population PMA provides superior exploration capabilities for complex instances, while the Single-Population PMA delivers faster convergence with comparable solution quality. Overall, this study establishes new performance benchmarks for the CETSP and demonstrates the effectiveness of parallel memetic algorithms in solving large-scale combinatorial optimization problems.