Three-dimensional path planning for unmanned aerial vehicles based on multi-objective genetic algorithm

Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. This paper presents a genetic algorithm (GA) specifically for problems with multiple objectives. It differs primarily from traditional GA by using specialized mechanisms to promote solution diversity. Then the genetic algorithm approach is applied to three-dimensional path planning for unmanned aerial vehicles (UAVs). Specially, several mutation operators are extended and new mutation operators are introduced for path planning based on the modified problems of traditional path planning. The objective of the proposed mutation operator is to expel the solutions out of restricted area. Experiment results show the effectiveness of the proposed GA approach.