适应性引导的花朵授粉算法  

Adaptive guidance flower pollination algorithm

作　　者：郭肇禄[1,2] 石涛 杨火根 张文生[2] GUO Zhaolu;SHI Tao;YANG Huogen;ZHANG Wensheng(School of Science,Jiangxi University of Science and Technology,Ganzhou 341000,Jiangxi,China;Institute of Automation,Chinese Academy of Science,Beijing 100190,China)

机构地区：[1]江西理工大学理学院,江西赣州341000 [2]中国科学院自动化研究所,北京100190

出　　处：《陕西师范大学学报(自然科学版)》2025年第1期114-130,共17页Journal of Shaanxi Normal University:Natural Science Edition

基　　金：国家自然科学基金(12161043,61662029);江西省自然科学基金(20192BAB201007);江西省教育厅科技项目(GJJ160623,GJJ170495);江西理工大学青年英才支持计划项目(2018)。

摘　　要：针对传统花朵授粉算法在求解一些复杂优化问题时存在着开采能力不足的缺点,提出了一种适应性引导的花朵授粉算法(AGFPA)。所提算法设计了环优策略和向优策略相结合的适应性引导机制,适应性地控制最优个体对种群演化的引导作用,既增强算法的开采能力,又尽可能维持种群的多样性。适应性引导机制中的环优策略在最优个体的周围执行导向开采,使得种群集中搜索最优个体的邻域;而向优策略利用最优个体的引导进行定向搜索,使得搜索有向地覆盖较广的未知区域。此外,设计了适应性参数控制策略,根据不同演化阶段的需求,调整全局授粉转换概率和最优引导的步长因子,从而维持开采能力和勘探能力的平衡。为检验所提算法的性能,在群智能研究领域中常用的18个基准测试函数上进行了策略有效性分析,并将AGFPA分别与几种改进的FPA和PSO算法进行比较;同时,应用AGFPA估计发酵动力学参数。实验结果表明,在求解大多数单峰、多峰和复杂函数时,AGFPA均具有较为优秀的寻优能力;在发酵动力学参数估计应用中,AGFPA也具有一定的优势。The traditional flower pollination algorithms tend to exhibit poor exploitation when facing some complex optimization problems.Aiming at this weakness of the traditional flower pollination algorithms,an adaptive guidance flower pollination algorithm(AGFPA)is proposed.In the proposed AGFPA,an adaptive guidance mechanism(AGM)is introduced,which combines the global best individual surrounding strategy and the global best approaching strategy.The introduced AGM adaptively utilizes the global best individual to guide the population evolution,enhancing the exploitation capabilities while preserving the population diversity as much as possible.Specifically,the global best individual surrounding strategy focuses on exploiting the neighborhoods around the global best individual.Meanwhile,the global best approaching strategy utilizes the global best individual to guide the search directions,enabling the algorithm to explore a wide unknown area.In addition,an adaptive parameter control strategy is presented in the proposed AGFPA.The two key parameters,global pollination transform probability and step size factor,are adjusted according to the needs of different evolution stages,maintaining a good balance between exploitation and exploration.To test the performance of AGFPA,18 benchmark functions are utilized in the experiments,which are commonly used in the field of swarm intelligence.The effectiveness of the strategies is discussed.Moreover,AGFPA is compared with several existing flower pollination algorithms and particle swarm optimization algorithms.Additionally,AGFPA is also used to estimate the fermentation kinetic parameters in the biochemical engineering.The experimental results show that AGFPA can exhibit promising performance on the most unimodal,multimodal and complex functions.Moreover,AGFPA can yield excellent results in the biochemical engineering applications.

关 键 词：花朵授粉算法 适应性引导机制 环优策略 向优策略 适应性参数控制策略 发酵动力学参数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]