A Multiobjective Optimization Algorithm for Safety and Optimality of 3-D Route Planning in UAV

作     者：Abdel-Basset, Mohamed Mohamed, Reda Sallam, Karam M. Hezam, Ibrahim M. Munasinghe, Kumudu Jamalipour, Abbas 

作者机构：Zagazig Univ Al-Sharqia 7120001 Egypt Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Univ Canberra Sch IT & Syst Canberra ACT 2601 Australia King Saud Univ Coll Sci Stat Operat Res Dept 3 Riyadh Saudi Arabia Univ Sydney Sydney NSW 2006 Australia 

出 版 物：《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》 (IEEE Trans. Aerosp. Electron. Syst.)

年 卷 期：2024年第60卷第3期

页      面：3067-3080页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：Researchers Supporting Project 

主　　题：Costs Autonomous aerial vehicles Fuels Planning Metaheuristics Measurement Convergence Multiobjective Pareto optimality path planning swarm-based optimization algorithms unmanned aerial vehicle (UAV) 

摘      要：Finding a feasible path for an unmanned aerial vehicle (UAV) in a complex environment is a crucial part of any UAV mission planning system. Many algorithms have been developed to identify optimal or nearly optimal pathways for UAVs;however, the vast majority of those algorithms do not deal with this problem as multiobjective. Therefore, this study is presented to propose a new multiobjective optimization technique, namely the hybrid slime mould algorithm (HSMA), based on hybridizing the slime mould algorithm with a new updating mechanism to strengthen its performance when applied to tackle the multiobjective path planning problem in 3-D space. This algorithm employs Pareto optimality to tradeoff between various objectives. Those objectives include path optimality for minimizing the fuel cost and consumed time to reach the target location, flying away from threats to ensure safe operation, and finally the smooth cost to assess the climbing and turning rates. HSMA was evaluated using six benchmarking scenarios with various difficulty levels and compared to several recently published and well-established algorithms to show its effectiveness for several performance metrics, such as the convergence curve, Wilcoxon rank-sum test, and inverted generational distance metric. The experimental findings expose that HSMA is more effective than all the compared optimizers in terms of all performance metrics. Hence, it is the best alternative for efficiently creating high-quality pathways for UAVs.