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.

路径规划问题在自治系统和流程工业系统中受到广泛关注。现有研究主要集中在在顶点和边的松散约束下寻找源顶点到终止顶点的最短路径。然而，在现实中，必须考虑指定顶点、指定路径、禁止路径和禁止顶点等约束条件，这使得现有算法效率低下甚至不可行。针对具有多个路由约束的复杂路径规划问题，本文组织将约束转换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，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，引入了两个实验，结果证明了它的效率和优越性。