Optimisation of single starting point path for offshore wind farm inspection based on MTSP

As the global economy grows and countries' demand for energy expands, many countries are looking to reduce their dependence on fossil fuels by developing offshore wind energy. However, as wind farms expand in size and face challenges such as difficult maintenance and high construction costs, traditional inspection methods are inefficient and wasteful of human resources. To address these challenges, this paper investigates inspection path optimisation for offshore wind farms based on Genetic Algorithm (GA) and Ant Colony Algorithm (ACO) combined with Multiple Travelling Salesman Problem (MTSP). For medium and large-scale offshore wind farms, a single starting point path optimisation model for inspection ships is designed. In order to shorten the path taken by inspection ships, this paper adopts genetic algorithm and ant colony algorithm for optimisation, taking the sum of the total distances taken by all inspection ships as the objective function, and finally compares the performance of the two algorithms for the optimisation of inspection paths of offshore wind farms through simulation experiments, and the results show that the optimised paths of inspection of wind farms with genetic algorithm are shorter than that of ant colony algorithm. Therefore, the application of genetic algorithm in offshore wind farm inspection path optimisation can reduce the operation and inspection cost.