安庆师范大学学报（自然科学版）2026，Vol.32Issue(2)：47-56,10.DOI:10.13757/j.cnki.cn34-1328/n.2026.02.007
多策略改进的沙猫群优化低照度图像增强算法
Multi-Strategy Improved Sand Cat Swarm Optimization Low-Light Image Enhancement Algorithm
摘要
Abstract
Aiming at the problems of insufficient diversity,slow convergence speed and low convergence efficiency in the initial population stage of the sand cat swarm optimization algorithm,an improved sand cat swarm optimization algorithm based on hybrid multi-strategies is proposed in this paper.Firstly,the good point set initialization strategy is introduced to im-prove the generation mechanism of the initial population and enrich the diversity of the population.Secondly,a nonlinear con-vergence factor is incorporated to balance global search and local exploitation,which improves the overall convergence effi-ciency of the algorithm.The periodic variation strategy is utilized to enhance the ability of the algorithm to escape from local optimal traps in the searching stage.And the dynamic spiral search strategy is used to further accelerate the convergence speed.Finally,16 benchmark test functions are selected for comparative experiments,and the results are analyzed using the Wilcoxon rank-sum test.Experimental results demonstrate that in low-light image enhancement scenarios,the proposed algo-rithm outperforms the comparison algorithms by an average of 0.075 6 in information entropy,1.018 6 in standard deviation,and 0.230 2 in average gradient,respectively.This fully verifies that the proposed algorithm has stronger optimization capabili-ty,can effectively improve image processing,and outputs visual images with superior quality.关键词
智能优化算法/沙猫群优化算法/非完全beta函数/佳点集初始化/低照度图像增强Key words
intelligent optimization algorithm/sand cat swarm optimization algorithm/incomplete beta function/good point set initialization/low illumination image enhancement分类
信息技术与安全科学引用本文复制引用
张柳莹,纪娟娟,戚茗方,庞子祥,杜明,查申龙..多策略改进的沙猫群优化低照度图像增强算法[J].安庆师范大学学报（自然科学版）,2026,32(2):47-56,10.基金项目
国家自然科学基金项目(62205005)和安徽省自然科学基金青年基金项目(2108085QD182) （62205005）
