A Novel Hyperheuristic Algorithm Based on Evolutionary Strategy for Complex Mission Planning of AUVs in Marine Environment

作     者：Wei, Dunwen Ma, Hongjiao Yu, Huijun Dai, Xiaolin Wang, Gang Peng, Bei 

作者机构：Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China 

出 版 物：《IEEE JOURNAL OF OCEANIC ENGINEERING》 (IEEE J Oceanic Eng)

年 卷 期：2022年第47卷第4期

页      面：936-949页

核心收录：

学科分类：0808[工学-电气工程] 0707[理学-海洋科学] 08[工学] 0824[工学-船舶与海洋工程] 0814[工学-土木工程] 

基　　金：National Natural Science Foundation of China [51705066, 51975107] Sichuan Science and Technology Program [2019YFG0343] Fundamental Research Funds for the Central Universities of China [ZYGX2019J041, ZYGX2021YGCX011] 

主　　题：Heuristic algorithms Planning Task analysis Costs Software algorithms Energy consumption Robustness Autonomous underwater vehicles (AUVs) evolu-tionary strategy hyperheuristic algorithm (HHA) metaheuristic mission planning 

摘      要：Autonomous underwater vehicles (AUVs) have been widely implemented to explore marine resources or perform marine missions;meanwhile, complex mission planning has been expected to enhance the intelligence level, improve energy efficiency, and expand AUVs application areas. The mission planning model needs to consider energy consumption, mission quality, and mission quantity of long-term working in the marine environment. In this article, one unified and robust model was proposed based on the above requirements in the complex marine environment to improve the automaticity of AUV. However, solving this mission planning model is a nontrivial problem, especially when high robustness, high efficiency, fast response, and near-optimal results are required. Therefore, we propose a novel hyperheuristic algorithm based on evolutionary strategy (ES-HH). The proposed ES-HH combines a metaheuristic framework with a selection function to evaluate the performance of low-level heuristic operators online. This evolutionary strategy endows the hyperheuristic algorithm with the online learning feature, giving the ES-HH algorithm better computing efficiency, robustness, and near-optimal results. The experiment results show that the ES-HH algorithm can achieve better convergence and higher robustness than other algorithms, such as ant colony optimization and biogeography-based optimization algorithms. Compared with ACO and BBO algorithms, the proposed ES-HH algorithm can improve the mission completion rate by 3.42% and reduce the average energy consumption of a single task by 8.02%.