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| Multi Strategy Improved Beetle Optimization Algorithm |
| WANG Yifan1*，WANG Haibo1*，ZHAO Chuanzhe1**，SONG Yandi1**，WANG Ronglin1***，LI Pengtao2***，WEN Hao1***，LIU Chunjie1***
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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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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.
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Published: 11 July 2026
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