摘要
蜣螂优化器(dung beetle optimizer,DBO)是一种有效的元启发式算法。蜣螂优化算法虽然具有寻优能力强,收敛速度快的特点,但同时也存在全局探索和局部开发能力不平衡,容易陷入局部最优,且全局探索能力较弱的缺点。提出了一种改进的DBO算法来解决全局优化问题,命名为MSADBO。受改进正弦算法(improved sine algorithm,MSA)的启发,赋予蜣螂MSA的全局探索和局部开发能力,扩大其搜索范围,提高全局探索能力,减少陷入局部最优的可能性。同时加入了混沌映射初始化和变异算子进行扰动。为了验证MSADBO的有效性,对该算法采用23个基准测试函数进行了测试,并与其他知名的元启发式算法进行了比较。结果表明,该算法具有良好的性能。为了进一步阐述MSADBO算法的实际应用潜力,将该算法成功地应用于3个工程设计问题。实验结果表明,所提出的MSADBO算法可以有效地处理实际应用问题。
Dung beetle optimizer(DBO)is an effective meta-heuristic algorithm.Dung beetle optimization algorithm has the characteristics of strong searching ability and fast convergence speed.But at the same time,it also has the disadvantages of unbalanced global exploration and local exploitation ability,easy to fall into local optimization,and weak global search ability.Therefore,an improved DBO algorithm is proposed to solve the global optimization problem,named MSADBO.Inspired by the improved sine algorithm(MSA),this paper endows dung beetles with global exploration and local development capabilities of MSA to expand their search scope,improve their global search capability,and reduce the possibility of falling into local optimal.Chaotic mapping initialization and mutation operator are added to the perturbation.In order to verify the effectiveness of the proposed MSADBO algorithm,23 benchmark functions are tested and compared with other well-known meta-heuristic algorithms.The results show that the algorithm has good performance.Finally,in order to further illustrate the practical application potential of MSADBO algorithm,the algorithm is successfully applied to three engineering design problems.Experimental results show that the proposed MSADBO algorithm can deal with practical application problems effectively.
作者
潘劲成
李少波
周鹏
杨贵林
吕东超
PAN Jincheng;LI Shaobo;ZHOU Peng;YANG Guilin;LYU Dongchao(School of Mechanical Engineering,Guizhou University,Guiyang 550025,China;State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China)
出处
《计算机工程与应用》
CSCD
北大核心
2023年第22期92-110,共19页
Computer Engineering and Applications
基金
国家重点研发计划(2020YFB1713300)
国家自然科学基金面上项目(52275480)。
关键词
蜣螂优化算法
改进正弦算法
MSADBO
混沌映射初始化
变异算子
基准测试函数
工程设计问题
dung beetle optimization algorithm
improved sine algorithm
MSADBO
initialize chaotic map
mutation operator
benchmark test function
engineering design problems
作者简介
潘劲成(1997-),男,硕士研究生,CCF会员,研究方向为智能制造与多源数据融合;通信作者:李少波(1970-),男,教授,博士生导师,研究方向为智能制造与多源数据融合,E-mail:lishaobo@gzu.edu.cn;周鹏(1988-),男,博士,硕士生导师,副教授,研究方向为数据驱动的复杂系统过程监控与质量控制;杨贵林(1999-),男,硕士研究生,研究方向为智能制造、人工智能;吕东超(1996-),男,硕士研究生,研究方向为智能制造、人工智能。
