A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model

Abstract

The quota traveling salesman problem (QTSP) is a variant of the traveling salesman problem (TSP), which is a classical optimization problem. In the QTSP, the salesman visits some of the n cities to meet a given sales quota Q while having minimized travel costs. In this paper, we develop a DNA algorithm based on Adleman-Lipton model to solve the quota traveling salesman problem. Its time complexity is O n 2 + Q , which is a significant improvement over previous algorithms with exponential complexity. A coding scheme of element information is pointed out, and a reasonable biological algorithm is raised by using limited conditions, whose feasibility is verified by simulation experiments. The innovation of this study is to propose a polynomial time complexity algorithm to solve the QTSP. This advantage will become more obvious as the problem scale increases compared with the algorithm of exponential computational complexity. The proposed DNA algorithm also has the significant advantages of having a large storage capacity and consuming less energy during the operation. With the maturity of DNA manipulation technology, DNA computing, as one of the parallel biological computing methods, has the potential to solve more complex NP-hard problems.

Organoid Intelligence: Bridging Artificial Intelligence for Biological Computing and Neurological Insights

“…However, the field of biocomputing truly began to take shape in the 1990s, with the advent of DNA computing. The pioneer of this field was Leonard Adleman, a computer scientist from the University of Southern California [37]. In 1994, Adleman published a groundbreaking paper in which he demonstrated that DNA could be used to solve a well-known mathematical problem, the seven-point Hamiltonian path problem, also known as the "traveling salesman" problem.…”