Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (9): 1-8.DOI: 10.3778/j.issn.1002-8331.2111-0563

• Research Hotspots and Reviews • Previous Articles     Next Articles

Summary of Research and Application of Neighborhood Field Optimization Algorithm

WU Zhou, ZHANG Hongrui, ZHANG Haijun, SONG Qing   

  1. 1.School of Automation, Chongqing University, Chongqing 400044, China
    2.School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Shenzhen, Guangdong 518055, China
    3.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Online:2022-05-01 Published:2022-05-01



  1. 1.重庆大学 自动化学院,重庆 400044
    2.哈尔滨工业大学(深圳) 计算机科学与技术学院,广东 深圳 518055
    3.北京邮电大学 人工智能学院,北京 100876

Abstract: Neighborhood field optimization algorithm(NFO)?is a new swarm intelligence optimization algorithm inspired by the learning behavior of biological individuals from neighbors. The algorithm has the advantages of few parameters, simple structure and good local optimization performance, and it has attracted domestic and foreign many scholars have carried out research on it.?This paper briefly describes the optimization mechanism and search steps of NFO algorithm, the improvement of the algorithm is analyzed, including hybrid algorithm, coding mode and search step size, etc., and summarizes the applications of the algorithm in energy efficiency, path planning, economic scheduling and so on.?Combined with the characteristics of NFO algorithm and the existing research results, the future research content and direction of the algorithm are prospected.

Key words: neighborhood field optimization, swarm intelligence, bionic, artificial intelligence, smart construction

摘要: 近邻场优化算法(neighborhood field optimization,NFO)是一种受生物个体向邻居学习行为启发的新型群体智能优化算法,该算法具有参数较少、结构简单和局部寻优性能强等优点,吸引了国内外众多学者的关注和研究。简单阐述NFO算法的寻优原理和搜索步骤,并分析了现有的算法的改进研究,包括混合算法、编码方式以及搜索步长等改进策略,同时对算法在能源效率、路径规划、经济调度等方面的应用进行概括总结。结合NFO算法的特点及现有研究成果,对算法的未来研究内容与方向做出展望。

关键词: 近邻场优化, 群体智能, 仿生, 人工智能, 智能建造