近邻场优化算法研究与应用综述

Summary of Research and Application of Neighborhood Field Optimization Algorithm

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。

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.