| Related citation: | Baolei Li,Danjv Lv,Xinling Shi,Zhenzhou An,Yufeng Zhang,Jianhua Chen.Grid-Based Path Planner Using Multivariant Optimization Algorithm[J].Journal of Harbin Institute Of Technology(New Series),2015,22(5):89-96.DOI:10.11916/j.issn.1005-9113.2015.05.014. |
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| Author Name | Affiliation | | Baolei Li | School of Information Science and Engineering, Yunnan University, Kunming 650091, China
Oil Equipment Intelligent Control Engineering Laboratory of Henan Provice, Physics & Electronic Engineering College, Nanyang Normal University, Nanyang Henan 473061, China | | Danjv Lv | School of Information Science and Engineering, Yunnan University, Kunming 650091, China | | Xinling Shi | School of Information Science and Engineering, Yunnan University, Kunming 650091, China | | Zhenzhou An | School of Information Technology and Engineering, Yuxi Normal University, Yuxi 653100, China | | Yufeng Zhang | School of Information Science and Engineering, Yunnan University, Kunming 650091, China | | Jianhua Chen | School of Information Science and Engineering, Yunnan University, Kunming 650091, China |
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| 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：10.11916/j.issn.1005-9113.2015.05.014 |
| Clc Number:TP24 |
| Fund: |
