A Novel Discrete Group Teaching Optimization Algorithm for TSP Path Planning with Unmanned Surface Vehicles

Abstract

A growing number of researchers are interested in deploying unmanned surface vehicles (USVs) in support of ocean environmental monitoring. To accomplish these missions efficiently, multiple-waypoint path planning strategies for survey USVs are still a key challenge. The multiple-waypoint path planning problem, mathematically equivalent to the traveling salesman problem (TSP), is addressed in this paper using a discrete group teaching optimization algorithm (DGTOA). Generally, the algorithm consists of three phases. In the initialization phase, the DGTOA generates the initial sequence for students through greedy initialization. In the crossover phase, a new greedy crossover algorithm is introduced to increase diversity. In the mutation phase, to balance the exploration and exploitation, this paper proposes a dynamic adaptive neighborhood radius based on triangular probability selection to apply in the shift mutation algorithm, the inversion mutation algorithm, and the 3-opt mutation algorithm. To verify the performance of the DGTOA, fifteen benchmark cases from TSPLIB are implemented to compare the DGTOA with the discrete tree seed algorithm, discrete Jaya algorithm, artificial bee colony optimization, particle swarm optimization-ant colony optimization, and discrete shuffled frog-leaping algorithm. The results demonstrate that the DGTOA is a robust and competitive algorithm, especially for large-scale TSP problems. Meanwhile, the USV simulation results indicate that the DGTOA performs well in terms of exploration and exploitation.