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Generalized traveling salesman problem (GTSP) is a classical combinatorial optimization problem, in which the optimization goal is the minimum route combination. Since the GTSP is a more complex problem than the traveling salesman problem (TSP), the GTSP can be considered an extension of the TSP. At present, compared with TSP, GTSP has been widely used in practice. In GTSP, n nodes are divided into m clusters, and the problem is divided into two categories according to different constraints. According to the second kind of the GTSP, we propose a parallel biochemical algorithm to solve it. We use deoxyribonucleic acid (DNA) biological strands to represent different vertices, point groups and weights, and get the optimal solutions to a series of different biochemical reaction combinations of DNA sequences. Compared with other algorithms, our algorithm reduces the time complexity to O(n2), and proves the feasibility.

Parallel DNA Algorithms of Generalized Traveling Salesman Problem-Based Bioinspired Computing Model