Grid-Based Path Planner Using Multivariant Optimization Algorithm

Baolei Li^1,2, Danjv Lv¹, Xinling Shi¹, Zhenzhou An³, Yufeng Zhang¹,Jianhua Chen¹

(1.School of Information Science and Engineering, Yunnan University, Kunming 650091, China;2.Oil Equipment Intelligent Control Engineering Laboratory of Henan Provice, Physics & Electronic Engineering College, Nanyang Normal University, Nanyang Henan 473061, China;3.School of Information Technology and Engineering, Yuxi Normal University, Yuxi 653100, China)



Abstract:

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.

Key words:  multivariant optimization algorithm  shortest path planning  heuristic search  grid map  optimality of algorithm

DOI£º10.11916/j.issn.1005-9113.2015.05.014

Clc Number:TP24

Fund: