高斯混沌火鹰优化算法求解动态优化问题的研究及应用  

Gaussian Chaotic Fire Hawk Optimization Algorithm for Solving Dynamic Optimization Problems

作　　者：陈泳璋 莫愿斌[1] Chen Yongzhang;Mo Yuanbin(School of Artificial Intelligence,Guangxi Minzu University,Nanning 530006,China)

机构地区：[1]广西民族大学人工智能学院,广西南宁530006

出　　处：《系统仿真学报》2025年第2期436-449,共14页Journal of System Simulation

基　　金：国家自然科学基金(2146608);广西自然科学基金(2019CXNSFAA185017);广西民族大学科研项目(2021MDKJ004)。

摘　　要：在化学工业上有许多重要的化学过程依赖于动态优化,存在非线性与不连续性等因素,为寻找更高效的求解算法,在火鹰优化算法的基础上提出高斯混沌火鹰优化算法,在将控制变量参数化后用该算法求解此类问题。使用tent混沌映射替换原来的初始化种群方式,以使算法的最初分布更具合理性;在分析在火鹰位置更新、猎物位置更新后引入了竞争协同捕猎和逃避效应权重,提升了算法的开发和探索能力,同时还嵌入了高斯采样提高了种群的多样性,进一步增强了算法局部寻优和动态适应能力。仿真结果证明了算法在求解化工动态优化问题的有效性。There are many important chemical processes in the chemical industry rely on dynamic optimization with factors such as nonlinearity and discontinuity.In order to find a more efficient solution algorithm,Gaussian Chaotic fire hawk optimization algorithm is proposed based on the fire hawk optimization algorithm,which is used to solve such problems after parameterizing the control variables.The original way of initializing the populations is replaced using tent chaotic mapping in order to make more sense of the initial distribution of the algorithm;a more targeted update method has been proposed in the analysis of fire hawk location updates and prey location updates,enhancing the ability to develop and explore algorithms,Gaussian sampling is also embedded to improve the diversity of the population,further enhancing the algorithm's local search and dynamic adaptation capabilities.The results show the effectiveness of the algorithm in solving chemical dynamic optimization problems.

关 键 词：化工动态优化 高斯采样 控制变量参数化 混沌映射 火鹰优化 

分 类 号：TQ021.8[化学工程] TP391[自动化与计算机技术—计算机应用技术]