An improved dung beetle optimizer

Jinxiang Feng, Jingyang Li, Yufeng Zhang, H. Baoyin
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Abstract

Dung Beetle Optimizer(DBO) is an effective metaheuristic algorithm proposed in 2022. But at the same time, DBO also suffers from a local-global imbalance in the exploration process, tends to fall into local optimization and exploitability needs to be further improved, etc. Therefore, we propose an improved DBO algorithm to address these shortcomings and named it CDBO. Firstly, Tent chaotic mapping can be used for the purpose of initializing the population, improving the quality of initial solutions, promoting the enhancement of population variety, and augmenting the global search capability of the algorithm. Secondly, introducing dynamic weighting factors enables the algorithm to fully search for local areas while also taking into account global exploration. To assess the effectiveness of CDBO, a total of 12 benchmark test functions were utilized to evaluate the performance of this algorithm, wherein CDBO was compared with other widely recognized metaheuristic algorithms. The results showed that CDBO had improved search accuracy and convergence speed. Finally, CDBO was applied to airfoil optimization problem, verifying the feasibility of applying CDBO to practical engineering problems.
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改进的蜣螂优化器
Dung Beetle Optimizer(DBO)是2022年提出的一种有效的元启发式算法。但同时,DBO也存在探索过程中局部与全局不平衡、容易陷入局部优化、可利用性有待进一步提高等问题。因此,针对这些不足,我们提出了一种改进的DBO算法,并将其命名为CDBO。首先,Tent 混沌映射可用于初始化种群,提高初始解的质量,促进种群多样性的提高,增强算法的全局搜索能力。其次,引入动态加权因子可以使算法在充分搜索局部区域的同时兼顾全局探索。为了评估 CDBO 的有效性,我们使用了 12 个基准测试函数来评估该算法的性能,并将 CDBO 与其他公认的元启发式算法进行了比较。结果表明,CDBO 提高了搜索精度和收敛速度。最后,将 CDBO 应用于机翼优化问题,验证了将 CDBO 应用于实际工程问题的可行性。
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