沈阳大学学报（自然科学版）2025，Vol.37Issue(2)：134-146,13.
基于多策略改进的蜣螂优化算法及其应用
Optimization Algorithm for Dung Beetle Based on Multi Strategy Improvement and its Application
摘要
Abstract
A multi strategy improved dung beetle optimization algorithm(SDBO)was proposed to address the issues of insufficient diversity,easy falling into local optima,and slow convergence speed in traditional dung beetle optimization algorithms(DBO).SDBO uses a Tent chaotic map hybrid elite reverse learning strategy to increase the diversity of the initialization population.The golden sine algorithm is introduced to enhance the global exploration and local search performance of the algorithm.The Lévy flight strategy is introduced to avoid the algorithm falling into local optima.Dynamic weight coefficients are used to improve convergence speed of the algorithm.To evaluate the performance of SDBO,12 standard test functions were adopted to test it.Experimental results show that SDBO performs better than other seven heuristic algorithms in terms of overall optimization performance.The SDBO algorithm was applied to two classical constrained engineering design optimization problems,which achieved significant optimization results.关键词
蜣螂优化算法/混沌映射/莱维飞行/黄金正弦算法/SDBOKey words
dung beetle optimization algorithm/chaotic mapping/Lévy flight/golden sine algorithm/SDBO分类
计算机与自动化引用本文复制引用
刘洋,李思,柴海龙..基于多策略改进的蜣螂优化算法及其应用[J].沈阳大学学报（自然科学版）,2025,37(2):134-146,13.基金项目
2023年辽宁省"揭榜挂帅"科技计划项目(技术攻关类)(2023JH1/10400099). （技术攻关类）
