IEEE Trans Cybern. 2016 Jan;46(1):245-57. doi: 10.1109/TCYB.2015.2399616. Epub 2015 Feb 27.
The need for determining a path from an initial location to a target one is a crucial task in many applications, such as virtual simulations, robotics, and computer games. Almost all of the existing algorithms are designed to find optimal or suboptimal solutions considering only a single objective, namely path length. However, in many real life application path length is not the sole criteria for optimization, there are more than one criteria to be optimized that cannot be transformed to each other. In this paper, we introduce a novel multiobjective incremental algorithm, multiobjective D* lite (MOD* lite) built upon a well-known path planning algorithm, D* lite. A number of experiments are designed to compare the solution quality and execution time requirements of MOD* lite with the multiobjective A* algorithm, an alternative genetic algorithm we developed multiobjective genetic path planning and the strength Pareto evolutionary algorithm.
在许多应用中，如虚拟仿真、机器人技术和电脑游戏，从初始位置到目标位置确定路径是一项至关重要的任务。几乎所有现有的算法都是为了寻找最佳或次最佳解决方案而设计的，这些解决方案只考虑了一个目标，即路径长度。然而，在许多现实生活中的应用中，路径长度并不是优化的唯一标准，还有多个不能相互转换的标准需要优化。在本文中，我们引入了一种新的多目标增量算法，即基于知名路径规划算法 D* lite 的多目标 D* lite (MOD* lite)。设计了一些实验来比较 MOD* lite 与多目标 A*算法、我们开发的替代遗传算法多目标遗传路径规划和强度 Pareto 进化算法在解决方案质量和执行时间要求方面的性能。
