Multi-strategy Improved Dung Beetle Optimizer and Its Application

doi:10.3778/j.issn.1673-9418.2308020

Abstract

Abstract: Dung beetle optimizer (DBO) is an intelligent optimization algorithm proposed in recent years. Like other optimization algorithms, DBO also has disadvantages such as low convergence accuracy and easy to fall into local optimum. A multi-strategy improved dung beetle optimizer (MIDBO) is proposed. Firstly, it improves acceptance of local and global optimal solutions by brood balls and thieves, so that the beetles can dynamically change according to their own searching ability, which not only improves the population quality but also maintains the good searching ability of individuals with high fitness. Secondly, the follower position updating mechanism in the sparrow search algorithm is integrated to disturb the algorithm, and the greedy strategy is used to update the location, which improves the convergence accuracy of the algorithm. Finally, when the algorithm stagnates, Cauchy Gaussian variation strategy is introduced to improve the ability of the algorithm to jump out of the local optimal solution. Based on 20 benchmark test functions and CEC2019 test function, the simulation experiment verifies the effectiveness of the three improved strategies. The convergence analysis of the optimization results of the improved algorithm and the comparison algorithms and Wilcoxon rank sum test prove that MIDBO has good optimization performance and robustness. The validity and reliability of MIDBO in solving practical engineering problems are further verified by applying MIDBO to the solution of automobile collision optimization problems.

Key words: dung beetle optimization algorithm, local optimal solution, sparrow search algorithm, Cauchy Gaussian variation, car collision optimization problems, Wilcoxon rank sum test

摘要： 蜣螂优化算法（DBO）是近年提出的智能优化算法，与其他优化算法一样，DBO也存在收敛精度低、易陷入局部最优等缺点。针对DBO的这些局限性，提出一种多策略改进的蜣螂优化算法（MIDBO）。首先，改进雏球和偷窃蜣螂对局部最优解和全局最优解的接受程度，使其根据自身搜索能力动态变化，既提升了种群质量又保持了适应度高的个体的良好搜索能力；其次，融合麻雀搜索算法中的追随者位置更新机制对算法进行扰动，并用贪婪策略更新位置，提升了算法的收敛精度；最后，当算法陷入停滞时引入柯西高斯变异策略，提高了算法跳出局部最优解的能力。仿真实验基于20个基准测试函数和CEC2019测试函数，验证了3种改进策略的有效性，将所改进算法和对比算法的优化结果进行收敛性分析和Wilcoxon秩和检验，证明了MIDBO具有良好的寻优性能和鲁棒性。将MIDBO运用在汽车碰撞优化问题的求解上，进一步验证了MIDBO在求解实际工程问题中的有效性和可靠性。

关键词: 蜣螂优化算法, 局部最优解, 麻雀搜索算法, 柯西高斯变异, 汽车碰撞优化问题, Wilcoxon秩和检验

GUO Qin, ZHENG Qiaoxian. Multi-strategy Improved Dung Beetle Optimizer and Its Application[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(4): 930-946.

郭琴, 郑巧仙. 多策略改进的蜣螂优化算法及其应用[J]. 计算机科学与探索, 2024, 18(4): 930-946.

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