多策略改进蜣螂优化算法及其应用  

Multi-strategy improvement of dung beetle optimization algorithm and its application

作　　者：盛斌 张军 SHENG Bin;ZHANG Jun(College of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,CHN)

机构地区：[1]上海电力大学自动化工程学院,上海200090

出　　处：《制造技术与机床》2025年第3期65-76,共12页Manufacturing Technology & Machine Tool

基　　金：国家自然科学基金项目(61273190)。

摘　　要：为了改善标准蜣螂优化算法(dung beetle optimization,DBO)的收敛精度低、稳定性不足和易陷入局部最优等问题,提出了多策略改进蜣螂优化(multi-strategy improved dung beetle optimization,MSIDBO)算法。首先,使用融合Fun混沌与逆向学习策略初始化蜣螂种群,增加种群多样性和随机性;其次,引进鱼鹰算法的第一阶段的全局勘探策略替换蜣螂滚球阶段的位置更新,弥补蜣螂算法在滚球阶段依赖最差值,加快算法的求解速度和求解精度;再次,根据小蜣螂觅食位置更新引入自适应步长策略与凸透镜成像策略的集成,提高了算法全局开发和局部探索的能力;最后,对偷窃蜣螂的觅食行为进行自适应t分布扰动,使得算法更快跳出局部最优。将MSIDBO和其他算法在14个函数上进行测试,结果表明相对于其他群智能优化算法,MSIDBO的寻优能力、收敛能力等明显高于其他算法。将改进的算法用于压力弹簧设计优化问题,进一步证明改进后的算法具有较好的优化性能。In order to solve the problems of the standard dung beetle optimization(DBO)algorithm,such as low convergence accuracy,lack of stability and easy to fall into local optimality,a multi-strategy improved dung beetle optimization(MSIDBO)algorithm was proposed.Firstly,the combination of Fun chaos and reverse learning strategy was used to initialize the dung beetle population,which increased the diversity and randomness of the population.Secondly,the global exploration strategy of the first stage of the osprey algorithm is introduced to replace the position update of the dung beetle rolling stage,make up for the fact that the dung beetle algorithm only relies on the worst value in the rolling stage,and accelerate the solving speed and accuracy of the algorithm.Thirdly,the integration of adaptive step size strategy and convex lens imaging strategy based on the updated foraging position of the small beetle has improved the algorithm's ability to develop globally and explore locally.Finally,adaptive tdistribution perturbation is applied to the foraging behavior of the thief beetle,enabling the algorithm to quickly jump out of local optima.The MSIDBO and other algorithms are tested on 14 functions.The results show that compared with other swarm intelligent optimization algorithms,MSIDBO's optimization ability and convergence ability are significantly higher than other algorithms.The improved algorithm is applied to the pressure spring design optimization problem,and it is further proved that the improved algorithm has better optimization performance.

关 键 词：蜣螂优化算法 逆向学习 自适应步长 凸透镜成像 T分布 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]