Ju Ming-Yi, Wang Siao-En, Guo Jian-Horn
Department of Computer Science and Information Engineering, National University of Tainan, Tainan 70005, Taiwan.
ScientificWorldJournal. 2014;2014:746260. doi: 10.1155/2014/746260. Epub 2014 May 28.
A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation.
本文提出了一种用于路径规划的采用可扩展编码方法的混合进化算法。该可扩展表示基于二叉树结构编码。为了解决混合遗传算法和粒子群优化的问题，在二叉树中添加了“虚拟节点”来处理不同长度的表示。实验结果表明，所提出的混合方法在为移动机器人导航生成无碰撞短路径时，比传统进化算法使用更少的转折点。
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