Unmanned aerial vehicles (UAVs) are desirable platforms for time-efficient and cost-effective task execution. 3-D path planning is a key challenge for task decision-making. This paper proposes an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) with an adaptive areal weight adjustment (AAWA) strategy to make a tradeoff between the total flight path length and the terrain threat. AAWA is designed to improve the diversity of the solutions. More specifically, AAWA first removes a crowded individual and its weight vector from the current population and then adds a sparse individual from the external elite population to the current population. To enable the newly-added individual to evolve towards the sparser area of the population in the objective space, its weight vector is constructed by the objective function value of its neighbors. The effectiveness of MOEA/D-AAWA is validated in twenty synthetic scenarios with different number of obstacles and four realistic scenarios in comparison with other three classical methods.

翻译：无人机是高效且经济地执行任务的理想平台，三维路径规划是任务决策的关键挑战。本文提出一种改进的基于分解的多目标进化算法（MOEA/D），结合自适应面积权重调整（AAWA）策略，以平衡总飞行路径长度与地形威胁。AAWA旨在提高解的多样性。具体而言，AAWA首先从当前种群中移除一个拥挤个体及其权重向量，然后从外部精英种群向当前种群添加一个稀疏个体。为使新增个体向目标空间中种群更稀疏的区域进化，其权重向量由相邻个体的目标函数值构建。通过将MOEA/D-AAWA与其他三种经典方法在20个具有不同障碍物数量的合成场景和四个真实场景中进行比较，验证了该算法的有效性。