Shortest Hamiltonian circuit algorithm for complex environments

Abstract

Simply taking into account the static path length is insufficient to compute the shortest Hamiltonian circuit in complicated situations. To incorporate the influence of many factors on the shortest Hamiltonian circuit, this paper introduces a multi-objective fuzzy comprehensive evaluation model and quantitatively assesses the fuzzy factors. To better correctly reflect each influencing factor's importance in the problem-solving process, this paper also builds a hierarchical model to decide the weights assigned to each one. Finally, this paper adopts the state compression dynamic programming algorithm to solve the dynamic shortest Hamiltonian loop. In this study, an example analysis is also performed to confirm the algorithm's efficacy. Compared with the traditional algorithm, the algorithm designed in this paper comprehensively considers the influence of several factors when calculating the shortest Hamiltonian loop, and can more comprehensively evaluate the advantages and disadvantages of the path.