计算机应用研究2026，Vol.43Issue(6)：1776-1784,9.DOI:10.19734/j.issn.1001-3695.2025.10.0426
基于适应度景观与雅可比矩阵引导梯度下降的改进花授粉算法
Improved flower pollination algorithm by adopting fitness landscape and Jacobian matrix-guided gradient descent mechanism
摘要
Abstract
To address the defects of flower pollination algorithm(FPA),such as its susceptibility to local optima,inadequate optimization accuracy,and tendency toward premature convergence,this paper proposed an improved FPA by adopting the fit-ness landscape and Jacobian matrix-guided gradient descent mechanism,namely FJFPA.Firstly,FJFPA dynamically divided the population into an elite swarm and a common swarm based on individuals' fitness values and their relative distances,ba-lancing between exploration and exploitation.Secondly,FJFPA introduced the fitness landscape and Jacobian matrix for gradi-ent descent mechanism.This bolstered vanilla FPA's single-mechanism update and insufficient adaptability.Thirdly,FJFPA also incorporated a refined full opposition-based learning strategy.It generated opposite solutions by leveraging the weighted center and performed the stochastic dimension-mixture operation to further increase the randomness of the opposite solutions,and then retained high-quality solutions through simulated annealing mechanism.This strategy could further mitigate the likeli-hood of being trapped in local optima.The CEC2022 test suit was used as the benchmark to evaluate the performance of FJFPA with the vanilla FPA and 6 other state-of-the-art improved algorithms:DMEFPA,HASMFP,NGFPA,PMFPA,DMSSA and MSNSSA.Friedman tests based on the experimental results indicate that FJFPA can achieve the supreme performance among all co-evaluated algorithms.The results of ablation experiment also demonstrates that FJFPA can attain the outstanding per-formance when all 3 improvement strategies are synergistically combined.关键词
花授粉算法/动态双子群/适应度景观/反向学习/模拟退火Key words
flower pollination algorithm/dynamic dual-swarm mechanism/fitness landscape/opposition-based learning/simulated annealing分类
信息技术与安全科学引用本文复制引用
李大海,朱云峰,王振东..基于适应度景观与雅可比矩阵引导梯度下降的改进花授粉算法[J].计算机应用研究,2026,43(6):1776-1784,9.基金项目
国家自然科学基金资助项目(61563019,615620237) （61563019,615620237）
江西理工大学校级基金资助项目(205200100013) （205200100013）
