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吉林化工学院学报, 2026, 43(1): 35-41     https://doi.org/10.16039/j.cnki.cn22-1249.2026.01.006
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多策略改进的蜣螂优化算法
王一凡1*,王海波1*,赵传哲1**,宋亚迪1**,王荣林1***,李鹏涛2***,文皓1***,刘春杰1***
1 吉林化工大学 机电工程学院,吉林 吉林132022 ;2 吉林化工大学 信息与控制工程学院,吉林 吉林132022
Multi Strategy Improved Beetle Optimization Algorithm
WANG Yifan1*,WANG Haibo1*,ZHAO Chuanzhe1**,SONG Yandi1**,WANG Ronglin1***,LI Pengtao2***,WEN Hao1***,LIU Chunjie1***
1 School of Mechanical and Electrical Engineering, Jilin Institute of Chemical Technology, Jilin City 132022,China;2 School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin City, 132022,China

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摘要 提出一种多策略改进的蜣螂优化算法( Improved Dung Beetle Optimizer,IDBO)来解决蜣螂优化算法(Dung Beetle Optimizer,DBO)的收敛速度慢与寻优精度不高。首先,采用Piecewise混沌映射对种群进行初始化,从而增加种群的多样性以便在复杂问题中快速找到更优解;其次,使用自适应惯性权重策略对蜣螂位置信息进行动态更新,从而提高算法的全局探索能力与局部开发能力,最后,加入自适应t分布变异,提高算法的收敛速率。通过实验仿真,对比6个基准测试函数的收敛曲线、最优值、均值和标准差,结果表明,改进后的蜣螂算法在收敛速度、精度、稳定性和寻优性方面均表现出提升,验证了其优越性。未来研究将基于此改进算法,探索其在工程优化中的应用,以解决实际工程问题。
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作者相关文章
王一凡
王海波
赵传哲
宋亚迪
王荣林
李鹏涛
文皓
刘春杰
关键词:  蜣螂优化算法   Piecewise混沌映射   自适应t分布变异   自适应惯性权重    
Abstract: A multi strategy improved Dung Beetle Optimizer (IDBO) is proposed to address the slow convergence speed and low optimization accuracy of the Dung Beetle Optimizer (DBO) algorithm. Firstly, the population is initialized using Piecewise chaotic mapping to improve diversity, avoid early convergence to local optima, and make the initial solution distribution more uniform to improve the coverage of the search space and quickly find better solutions in complex problems; Secondly, adaptive inertia weights are used to update the position information of the beetle, thereby improving the global exploration and local development capabilities of the algorithm. Finally, adaptive t-distribution mutation is used to dynamically adjust the population position to improve the convergence rate of the algorithm. In the experimental simulation, by comparing the convergence curves, optimal values, means, and standard deviations of six benchmark test functions, the results showed that the improved beetle algorithm exhibited significant advantages in convergence speed, accuracy, stability, and optimality, verifying its superiority. Future research will be based on this improved algorithm to explore its application in engineering optimization, in order to solve practical engineering problems.
Key words:  dungeon optimization algorithm    Piecewise chaotic mapping    adaptive t-distribution variation    adaptive inertia weight
               出版日期:  2026-01-25      发布日期:  2026-07-11      整期出版日期:  2026-01-25
ZTFLH:  TP18  
引用本文:    
王一凡, 王海波, 赵传哲, 宋亚迪, 王荣林, 李鹏涛, 文皓, 刘春杰. 多策略改进的蜣螂优化算法[J]. 吉林化工学院学报, 2026, 43(1): 35-41.
WANG Yifan, WANG Haibo, ZHAO Chuanzhe, SONG Yandi, WANG Ronglin, LI Pengtao, WEN Hao, LIU Chunjie. Multi Strategy Improved Beetle Optimization Algorithm. Journal of Jilin Institute of Chemical Technology, 2026, 43(1): 35-41.
链接本文:  
https://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2026.01.006  或          https://xuebao.jlict.edu.cn/CN/Y2026/V43/I1/35
[1] 宋亚迪, 王海波, 赵传哲, 王荣林, 李志锋, 李鹏涛. 多策略融合改进的黏菌优化算法[J]. 吉林化工学院学报, 2024, 41(9): 62-69.
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