Title: A chaotic simulated annealing genetic algorithm with asymmetric time for offshore wind farm inspection path planning

Authors: Lei Kou; Yukuan Wang; Fangfang Zhang; Quande Yuan; Zhen Wang; Jingya Wen; Wende Ke

Addresses: Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, China ' School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, China ' Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China

Abstract: The harsh environment of offshore wind farms causes wind turbines to be easily broken down. To ensure the normal operation of wind turbines, it is necessary to carry out inspections of offshore wind farms. Path planning is an important step to complete the inspection. The unmanned surface vessel (USV) is subject to the water current, leading to deceleration and acceleration, which makes the round-trip travelling time of the USV between two wind turbines asymmetric. To sum up, it belongs to asymmetric travelling salesman problem. To address this problem, a chaotic simulated annealing genetic algorithm (CSAGA) considering asymmetric time is proposed in this paper. Firstly, the initial sequence with high quality, as the initial solution of the simulated annealing algorithm, is generated by logistic-tent chaotic mapping. Then, effective solutions are obtained by a series of operations of the simulating annealing algorithm and is used to replace the worst fitness individuals in the initial population of the genetic algorithm. Finally, genetic operations such as selection, crossover, mutation, and reversion are applied to the population to obtain the optimal solution. The feasibility of the algorithm is verified by simulation experiments. The results display that CSAGA has better performances compared to other algorithms.

Keywords: travelling salesman problem; TSP; inspection path planning; simulated annealing algorithm; genetic algorithm; offshore wind farm.

DOI: 10.1504/IJBIC.2025.145514

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.2, pp.69 - 78

Received: 08 May 2024
Accepted: 11 Aug 2024
Published online: 02 Apr 2025 *

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