Grid-Based Path Planner Using Multivariant Optimization Algorithm

作     者：Baolei Li Danjv Lv Xinling Shi Zhenzhou An Yufeng Zhang Jianhua Chen 

作者机构：School of Information Science and EngineeringYunnan University Oil Equipment Intelligent Control Engineering Laboratory of Henan ProvicePhysics & Electronic Engineering CollegeNanyang Normal University School of Information Technology and EngineeringYuxi Normal University 

出 版 物：《Journal of Harbin Institute of Technology(New Series)》 (哈尔滨工业大学学报（英文版）)

年 卷 期：2015年第22卷第5期

页      面：89-96页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 

基　　金：Sponsored by the National Natural Science Foundation of China(Grant No.61261007,61002049) the Key Program of Yunnan Natural Science Foundation(Grant No.2013FA008) 

主　　题：multivariant optimization algorithm shortest path planning heuristic search grid map optimality of algorithm 

摘      要：To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.