基于精英反向学习策略的改进蜣螂优化算法
Improved Dung Beetle Optimizer Based on Elite Opposition Learning Strategy

为了提高蜣螂优化算法(DBO)全局搜索过程中的种群多样性和避免陷入局部最优的风险，利用混沌理论和精英反向学习策略提出了一种基于精英反向学习策略的改进蜣螂优化算法(EoDBO)。首先，在蜣螂初始化种群个体位置时引入Sinusoidal map混沌映射策略，以提高寻优前蜣螂种群整体质量，利于加快全局搜索速度；其次，在算法后期采用精英反向学习策略，对部分较优的蜣螂位置进行扰动以调高算法的局部开发能力。利用12个国际基准测试函数测试改进算法的性能，并于DBO算法、麻雀搜索算法(SSA)、灰狼优化算法(GWO)、鲸鱼优化算法(WOA)、黑猩猩优化算法(ChOA)进行对比分析，实验表明EoDBO算法在收敛精度和算法的稳定性方面均变现更优，且收敛速度更快。
In order to improve the population diversity and avoid the risk of falling into local optimum during the global search of Dung Beetle Optimizer (DBO), an improved Dung Beetle Optimizer based on Elite Opposition Learning Strategy (EoDBO) is proposed using chaos theory and elite opposition learning strategy. First, a Sinusoidal map chaotic mapping strategy is introduced in the initialization of individual population positions of dung beetles to improve the overall quality of the dung beetle population before the search for optimality and to facilitate faster global search; second, an elite opposition learning strategy is used in the later stage of the algorithm to perturb some of the better dung beetle positions to tune up the local exploitation capability of the algorithm. The performance of the improved algorithm is tested using 12 international benchmarking functions and compared with DBO algorithm, Sparrow Search Algorithm (SSA), Gray Wolf Optimization Algorithm (GWO), Whale Optimization Algorithm (WOA), and Chimpanzee Optimization Algorithm (ChOA) for analysis. The experiments show that the EoDBO algorithm turns out to be superior in terms of convergence accuracy and stability of the algorithm, and converges faster.