Xie Guo, Du Xulong, Li Siyu, Yang Jing, Hei Xinhong, Wen Tao
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China.
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China.
ISA Trans. 2022 Feb;121:206-216. doi: 10.1016/j.isatra.2021.03.041. Epub 2021 Mar 29.
Path planning problem is attracting wide attention in autonomous system and process industry system. The existed research mainly focuses on finding the shortest path from the source vertex to the termination vertex under loose constraints of vertex and edge. However, in realistic, the constraints such as specified vertexes, specified paths, forbidden paths and forbidden vertexes have to be considered, which makes the existing algorithms inefficient even infeasible. Aiming at solving the problems of complex path planning with multiple routing constraints, this paper organizes transforms the constraints into appropriate mathematical analytic expressions. Then, in order to overcome the defects of existing coding and optimization algorithms, an adaptive strategy for the vertex priority is proposed in coding, and an efficient and global optimization methodology based on swarm intelligence algorithms is put forward, which can make full use of the high efficiency of the local optimization algorithm and the high search ability of the global optimization algorithm. Moreover, the optimal convergence condition of the methodology is proved theoretically. Finally, two experiments are inducted, and the results demonstrated its efficiency and superiority.
路径规划问题在自主系统和过程工业系统中受到广泛关注。现有的研究主要集中在顶点和边的宽松约束下寻找从源顶点到终止顶点的最短路径。然而，在现实中，必须考虑诸如指定顶点、指定路径、禁止路径和禁止顶点等约束，这使得现有算法效率低下甚至不可行。针对解决具有多个路由约束的复杂路径规划问题，本文将约束进行整理并转化为适当的数学解析表达式。然后，为了克服现有编码和优化算法的缺陷，在编码中提出了一种顶点优先级自适应策略，并提出了一种基于群体智能算法的高效全局优化方法，该方法可以充分利用局部优化算法的高效率和全局优化算法的高搜索能力。此外，从理论上证明了该方法的最优收敛条件。最后，进行了两个实验，结果证明了其效率和优越性。
