<![CDATA[The advancement of optimization algorithms in computer science has encouraged various approaches to solving classical problems such as the Travelling Salesman Problem (TSP), which involves finding the shortest route from one point to all others without revisiting any point. While recursive and iterative approaches have been widely applied individually, each has its limitations—particularly in execution time and memory usage when applied to large-scale data. This study proposes and implements a hybrid recursive-iterative approach to enhance algorithmic performance in solving TSP. The experiment, conducted using the python programming language, used a randomly generated symmetric graph dataset with 10 sample with city description A-J. Three methods were compared: iterative, recursive, and hybrid. The results showed that all approaches produced identical total route distances (246 units), yet varied significantly in execution time and memory usage. The hybrid method recorded the fastest execution time of 11.3550 seconds—50.1% faster than the iterative approach and 73.3% faster than the recursive approach. In terms of memory, the hybrid used 1.14 KB, slightly higher than the iterative (0.86 KB) but lower than the recursive (1.12 KB). These findings indicate that the hybrid approach offers the best trade-off between speed and resource usage, making it an efficient solution for medium to large-scale TSP scenarios. This study contributes to the development of optimization algorithms based on multi-paradigm adaptation.]]>
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