徐晓光,胡楠,徐禹翔,王雷.改进萤火虫算法在路径规划中的应用[J].电子测量与仪器学报,2016,30(11):1735-1742
改进萤火虫算法在路径规划中的应用
Application of improved firefly algorithm in path planning
  
DOI:10.13382/j.jemi.2016.11.015
中文关键词:  移动机器人  路径规划  萤火虫算法  小生境技术  信息共享
英文关键词:mobile robot  path planning  firefly algorithm  niche technology  information sharing
基金项目:安徽省高等学校省级自然科学研究项目(KJ2014A024)资助
作者单位
徐晓光 安徽工程大学电气工程学院检测技术与自动化装置重点实验室芜湖241000 
胡楠 安徽工程大学电气工程学院检测技术与自动化装置重点实验室芜湖241000 
徐禹翔 安徽工程大学电气工程学院检测技术与自动化装置重点实验室芜湖241000 
王雷 安徽工程大学电气工程学院检测技术与自动化装置重点实验室芜湖241000 
AuthorInstitution
Xu Xiaoguang Key Laboratory on Detection Technology and Automation of Electrical Engineering College, Anhui Polytechnic University, Wuhu 241000, China 
Hu Nan Key Laboratory on Detection Technology and Automation of Electrical Engineering College, Anhui Polytechnic University, Wuhu 241000, China 
Xu Yuxiang Key Laboratory on Detection Technology and Automation of Electrical Engineering College, Anhui Polytechnic University, Wuhu 241000, China 
Wang Lei Key Laboratory on Detection Technology and Automation of Electrical Engineering College, Anhui Polytechnic University, Wuhu 241000, China 
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中文摘要:
      为了保证移动机器人路径规划的解的多样性, 提出了小生境萤火虫算法(NFA)。首先, 根据环境特点, 建立合理的路径规划模型, 将萤火虫算法(FA)的目标函数设置为移动步数, 并重新设计了亮度公式、初始化方式和萤火虫移动方式; 其次, 在FA的基础上, 引入小生境技术, 并在小生境种群间加入共享信息。仿真实验表明, NFA一次运行可得到多个最优路径。相比FA, NFA的移动步数和目标函数均值分别减少了7.14%、6.76%, 萤火虫亮度均值增加了8.33%; 相比GA, NFA的移动步数和目标函数均值分别减少了7.14%、9.79%。结果表明NFA在算法性能上更优。
英文摘要:
      In order to ensure the diversity of solution of mobile robot path planning, a niche firefly algorithm (NFA) is proposed. Firstly, according to the characteristics of environment, a rational path planning model is established, the objective function of firefly algorithm (FA) is set to mobile step count, and brightness formula, initialization method and firefly mobile method are redesigned. Secondly, on the basis of FA, niche technology is introduced, and sharing information between niche populations is added. The results show that NFA run once can obtain multiple optimal paths. At the same time, comparing with FA, mobile step count and the objective function average of NFA are respectively reduced by 7.14% and 6.76%, the brightness average of firefly are increased by 8.33%, comparing with GA, mobile step count and the objective function average of NFA are separately reduced by 7.14% and 9.79%. It shows that NFA is better in performance.
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