| 基于改进多元优化算法的动态路径规划 |
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| 引用本文： | 李宝磊,施心陵,李敬敬,吕丹桔. 基于改进多元优化算法的动态路径规划[J]. 航空学报, 2015, 36(7): 2319-2328. DOI: 10.7527/S1000-6893.2015.0016 |
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| 作者姓名： | 李宝磊 施心陵 李敬敬 吕丹桔 |
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| 作者单位： | 1. 云南大学 信息学院, 昆明 650091;2. 西南林业大学 计算机与信息学院, 昆明 650224 |
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| 基金项目： | 国家自然科学基金 (61261007); 云南省重点自然科学基金 (2013FA008) |
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| 摘 要： | 为满足动态路径规划实时性强和动态跟踪精度高的需求,提出一种基于能够同时发现并追踪多条最优以及次优路径的改进多元优化算法(IMOA)的求解方法。首先,通过利用贝赛尔曲线描述路径的方法把动态路径规划问题转化为动态优化问题;然后,把相似性检测操作引入到多元优化算法(MOA)中,增加算法同时跟踪多个不同最优以及次优解的概率;最后,用IMOA对贝赛尔曲线的控制点进行寻优。实验结果表明:当最优路径由于环境变化而变为非优或者不可行时,利用IMOA对多个最优以及次优解动态跟踪的特点,能够快速调整寻优策略对其他次优路径进行寻优以期望再次找到最优路径;其综合离线性能较其他方法也有一定的提高。因此,IMOA满足动态路径规划的实际需求,适用于解决动态环境中的路径规划问题。
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| 关 键 词： | 改进多元优化算法 动态路径规划 动态优化 贝赛尔曲线 多模态优化 多群优化算法 |
| 收稿时间： | 2014-08-22 |
| 修稿时间： | 2015-01-13 |
Dynamic path planning based on improved multivariant optimization algorithm |
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| LI Baolei,SHI Xinling,LI Jingjing,LYU Danju. Dynamic path planning based on improved multivariant optimization algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(7): 2319-2328. DOI: 10.7527/S1000-6893.2015.0016 |
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| Authors: | LI Baolei SHI Xinling LI Jingjing LYU Danju |
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| Affiliation: | 1. School of Information Science and Engineering, Yunnan University, Kunming 650091, China;2. Shool of Computer and Information, Southwest Forestry University, Kunming 650224, China |
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| Abstract: | To meet the demands for hard real time and high tracking accuracy in dynamic path planning problems, a solver based on the improved multivariant optimization algorithm (IMOA) which can simultaneously locate and track multiple optimal and sub-optimal paths is proposed. Firstly, the dynamic path planning problems are transferred into the dynamic optimization problems by defining a path with a Bezier curve. Then, the probability of tracking different optimal and sub-optimal solutions simultaneously is improved through introducing a similarity check operation into multivariate optimization algorithm (MOA). Finally, the IMOA is applied to optimize the control points of Bezier curve. Experiment results show that once the optimal path becomes less optimal or infeasible, the IMOA, by making use of its characteristics of tracking multiple dynamic optimal and suboptimal solutions, has the ability to quickly adjust optimization strategy to refine other suboptimal paths in the purpose of finding the optimal path again. What is more, the overall offline performance is improved compared with other algorithms. The presented IMOA method is adaptable to the dynamic path planning problems and meets the real demands in dynamic environments. |
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| Keywords: | improved multivariant optimization algorithm dynamic path planning dynamic optimization Bezier curve multimodal optimization multi-swarm optimization algorithm |
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