| 蚁群算法求解离散最小约束去除问题 |
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| 引用本文： | 许波,闵华清,肖芳雄. 蚁群算法求解离散最小约束去除问题[J]. 上海交通大学学报, 2015, 49(3): 383-386 |
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| 作者姓名： | 许波 闵华清 肖芳雄 |
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| 作者单位： | （华南理工大学 软件学院，广州 510006） |
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| 基金项目： | 国家自然科学基金项目(6126200)，中国博士后科学基金（2014M562177）资助 |
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| 摘 要： | 摘要： 引入蚁群算法解决最小约束去除运动规划问题，在求解过程中对蚁群算法的启发函数以及信息素更新策略进行改进，使其不再易于陷入局部极值并适合求解该问题.仿真实验结果表明，该算法在解的质量和收敛速度上优于精确搜索与贪心算法.
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| 关 键 词： | 离散最小约束去除 运动规划问题 机器人路径规划 蚁群算法 |
| 收稿时间： | 2014-06-01 |
Ant Colony Algorithm for Solving Discrete Minimum Constraint Removal (MCR) Problem |
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| XU Bo,MIN Hua qing,XIAO Fang xiong. Ant Colony Algorithm for Solving Discrete Minimum Constraint Removal (MCR) Problem[J]. Journal of Shanghai Jiaotong University, 2015, 49(3): 383-386 |
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| Authors: | XU Bo MIN Hua qing XIAO Fang xiong |
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| Affiliation: | (School of Software Engineering, South China University of Technology, Guangzhou 510006, China) |
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| Abstract: | Abstract： This paper introduces the ant colony algorithm to solve the minimum constraint removal (MCR) problem. The inspired function and pheromone update strategy of the ant colony algorithm(ACO) were improved in the solving process, so that it is no longer easy to fall into local extremum. The simulation results show that the solution quality and convergence rate of the algorithm is better than those of the precise search and greedy algorithm.
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| Keywords: | discrete minimum constraint removal(MCR)； motion planning problem； robot path planning ant colony algorithm |
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