文章聚焦于蜣螂优化算法所固有的局限性问题,具体表现为其易于陷入局部最优解、在全局搜索能力上有所欠缺,以及收敛速度相对缓慢。针对这些不足,文章提出了一种创新性的改进策略——多策略融合的改进型蜣螂优化算法(简称MSIDBO)。在该...文章聚焦于蜣螂优化算法所固有的局限性问题,具体表现为其易于陷入局部最优解、在全局搜索能力上有所欠缺,以及收敛速度相对缓慢。针对这些不足,文章提出了一种创新性的改进策略——多策略融合的改进型蜣螂优化算法(简称MSIDBO)。在该改进方案中,首先于算法的初始化阶段引入了Logistic混沌映射机制,旨在有效提升种群分布的均匀程度;其次,采用鱼鹰优化算法替换原有蜣螂算法中的滚球位置更新机制,以解决原算法仅依赖最差值进行位置更新、缺乏个体间即时交流及参数冗余的问题;最后,实施了自适应t分布扰动策略,旨在迭代初期强化全局探索能力,而在迭代末期则加强局部搜索效率,并加速了算法的收敛进程。为了验证MSIDBO算法的有效性,对14个经典测试函数和工程应用问题进行测试,结果表明,引入的3种策略能有效提升蜣螂优化算法的性能。This study examines the intrinsic limitations of the dung beetle optimization algorithm, particularly its propensity to converge on local optima, its insufficient global search capabilities, and its relatively slow convergence rate. To mitigate these issues, the paper introduces a novel enhancement strategy termed the Improved Dung Beetle Optimization Algorithm with Multi-Strategy Fusion (MSIDBO). This enhancement involves several key modifications: first, a Logistic Chaos mapping mechanism is incorporated during the initialization phase of the algorithm to enhance the uniformity of population distribution. Second, the Fishhawk optimization algorithm is employed to replace the original rolling ball position update mechanism of the dung beetle algorithm. This substitution addresses the original algorithm’s reliance on the worst value for position updates, the absence of instantaneous communication among individuals, and the presence of parameter redundancy. Lastly, an adaptive t-distribution perturbation strategy is introduced to bolster global exploration during the initial iterations while simultaneously improving local search efficiency in the later stages, thereby accelerating the overall convergence of the algorithm. To evaluate the efficacy of the MSIDBO algorithm, a series of tests involving 14 classical benchmark functions and engineering application problems were conducted. The results indicate that the three strategies implemented significantly enhance the performance of the dung beetle optimization algorithm.展开更多
文摘文章聚焦于蜣螂优化算法所固有的局限性问题,具体表现为其易于陷入局部最优解、在全局搜索能力上有所欠缺,以及收敛速度相对缓慢。针对这些不足,文章提出了一种创新性的改进策略——多策略融合的改进型蜣螂优化算法(简称MSIDBO)。在该改进方案中,首先于算法的初始化阶段引入了Logistic混沌映射机制,旨在有效提升种群分布的均匀程度;其次,采用鱼鹰优化算法替换原有蜣螂算法中的滚球位置更新机制,以解决原算法仅依赖最差值进行位置更新、缺乏个体间即时交流及参数冗余的问题;最后,实施了自适应t分布扰动策略,旨在迭代初期强化全局探索能力,而在迭代末期则加强局部搜索效率,并加速了算法的收敛进程。为了验证MSIDBO算法的有效性,对14个经典测试函数和工程应用问题进行测试,结果表明,引入的3种策略能有效提升蜣螂优化算法的性能。This study examines the intrinsic limitations of the dung beetle optimization algorithm, particularly its propensity to converge on local optima, its insufficient global search capabilities, and its relatively slow convergence rate. To mitigate these issues, the paper introduces a novel enhancement strategy termed the Improved Dung Beetle Optimization Algorithm with Multi-Strategy Fusion (MSIDBO). This enhancement involves several key modifications: first, a Logistic Chaos mapping mechanism is incorporated during the initialization phase of the algorithm to enhance the uniformity of population distribution. Second, the Fishhawk optimization algorithm is employed to replace the original rolling ball position update mechanism of the dung beetle algorithm. This substitution addresses the original algorithm’s reliance on the worst value for position updates, the absence of instantaneous communication among individuals, and the presence of parameter redundancy. Lastly, an adaptive t-distribution perturbation strategy is introduced to bolster global exploration during the initial iterations while simultaneously improving local search efficiency in the later stages, thereby accelerating the overall convergence of the algorithm. To evaluate the efficacy of the MSIDBO algorithm, a series of tests involving 14 classical benchmark functions and engineering application problems were conducted. The results indicate that the three strategies implemented significantly enhance the performance of the dung beetle optimization algorithm.
