Biologically-inspired multi-objective optimization for UAV path planning

Planning an optimal path for UAVs (Unmanned Aerial Vehicle) is one of critical and challenging tasks in many applications. Biologically-inspired multi-objective optimization is a promising optimization method using the concepts from natural computing. The feasibility of applying multi-objective optimization algorithms inspired by biology in UAV path planning is investigated in this research by analyzing algorithm performance including the accuracy, processing time, and precision. A mathematical model for optimizing the UAV path is first developed. Then two objective functions are built using the mathematical model to formulate a Multi-Objective Optimization problem properly. Afterwards, the optimization problem is solved using three bio-inspired multi-objective optimization algorithms separately (SPEA2, NSGA2 and MOEA/D) and their performance is comprehensively compared. The experimental results obtained from the simulations show that multi-objective optimization algorithms used can generate a feasible and effective path for the UAV successfully. Among the three algorithms used in this research, it suggests that all the three algorithms have the comparable performance and would be good bio-inspired multi-objective optimization approach for UAV offline path planning applications.