蜣螂优化算法(DBO)作为一种新兴的智能优化算法,在求解复杂优化问题中显示出巨大潜力。然而,其在收敛精度和易陷入局部最优方面的局限性限制了其应用范围。本文提出了一种多策略改进的蜣螂优化算法(ODBO),通过佳点集群初始化和周期突变...蜣螂优化算法(DBO)作为一种新兴的智能优化算法,在求解复杂优化问题中显示出巨大潜力。然而,其在收敛精度和易陷入局部最优方面的局限性限制了其应用范围。本文提出了一种多策略改进的蜣螂优化算法(ODBO),通过佳点集群初始化和周期突变机制增强种群多样性,引入Beta分布动态生成反射解决方案以探索更广的搜索空间,并采用莱维飞行处理边界违规问题。进一步融合鲸鱼优化算法的螺旋搜索更新机制,结合随机策略更新位置,显著提升了算法的收敛精度和鲁棒性。当算法陷入停滞时,引入t分布扰动变异策略,有效提高了算法跳出局部最优解的能力。通过在17个基准测试函数验证了改进策略的有效性。此外,本文还将ODBO应用于车间排产调度问题,进一步证实了其在解决实际工程问题中的有效性和可靠性。The Dung Beetle Optimization algorithm (DBO), as an emerging intelligent optimization algorithm, shows great potential in solving complex optimization problems. However, its limitations in convergence precision and susceptibility to local optima hinder its broader application. This paper proposes an improved multi-strategy Dung Beetle Optimization algorithm (ODBO), which enhances population diversity through good point set initialization and periodic mutation mechanisms. A Beta distribution is introduced to dynamically generate reflected solutions to explore a wider search space, and Lévy flight is applied to handle boundary violation issues. Additionally, the spiral search update mechanism from the Whale Optimization Algorithm is integrated, along with random strategy updates for position, significantly improving the algorithm’s convergence accuracy and robustness. When the algorithm stagnates, a t-distribution mutation perturbation strategy is introduced, effectively enhancing its ability to escape local optima. Simulations on 17 benchmark functions test functions validate the effectiveness of the improved strategies. Moreover, the application of ODBO to the job-shop scheduling problem confirms its effectiveness and reliability in solving real-world engineering problems.展开更多
针对BDLS(Blockchain version of DLS)共识算法在含有大量节点且具有层次结构的系统中共识效率低下的问题,提出一种基于BDLS的区块链共识改进算法HBDLS(Hierarchical BDLS)。首先,根据实际应用中节点的属性将节点分为两个层次,每个高层...针对BDLS(Blockchain version of DLS)共识算法在含有大量节点且具有层次结构的系统中共识效率低下的问题,提出一种基于BDLS的区块链共识改进算法HBDLS(Hierarchical BDLS)。首先,根据实际应用中节点的属性将节点分为两个层次,每个高层节点分别管理一个低层节点簇;其次,将所有低层节点进行分簇共识,并将共识结果汇报至相应的高层节点;最后,所有高层节点对低层的共识结果再次共识,通过高层共识的数据将被写入区块链。理论分析和仿真实验结果表明,在36个节点且单个区块包含4500个交易的情况下,HBDLS的吞吐量相较于BDLS算法提高了21%;在44个节点且单个区块包含3000个交易的情况下,HBDLS的吞吐量相较于BDLS算法提高了约52%;在44个节点且单个区块包含1个交易的情况下,HBDLS的共识时延相较于BDLS算法下降了26%。实验结果表明,在节点数多且交易量大的系统中,HBDLS能够大幅提高系统的共识效率。展开更多
文摘蜣螂优化算法(DBO)作为一种新兴的智能优化算法,在求解复杂优化问题中显示出巨大潜力。然而,其在收敛精度和易陷入局部最优方面的局限性限制了其应用范围。本文提出了一种多策略改进的蜣螂优化算法(ODBO),通过佳点集群初始化和周期突变机制增强种群多样性,引入Beta分布动态生成反射解决方案以探索更广的搜索空间,并采用莱维飞行处理边界违规问题。进一步融合鲸鱼优化算法的螺旋搜索更新机制,结合随机策略更新位置,显著提升了算法的收敛精度和鲁棒性。当算法陷入停滞时,引入t分布扰动变异策略,有效提高了算法跳出局部最优解的能力。通过在17个基准测试函数验证了改进策略的有效性。此外,本文还将ODBO应用于车间排产调度问题,进一步证实了其在解决实际工程问题中的有效性和可靠性。The Dung Beetle Optimization algorithm (DBO), as an emerging intelligent optimization algorithm, shows great potential in solving complex optimization problems. However, its limitations in convergence precision and susceptibility to local optima hinder its broader application. This paper proposes an improved multi-strategy Dung Beetle Optimization algorithm (ODBO), which enhances population diversity through good point set initialization and periodic mutation mechanisms. A Beta distribution is introduced to dynamically generate reflected solutions to explore a wider search space, and Lévy flight is applied to handle boundary violation issues. Additionally, the spiral search update mechanism from the Whale Optimization Algorithm is integrated, along with random strategy updates for position, significantly improving the algorithm’s convergence accuracy and robustness. When the algorithm stagnates, a t-distribution mutation perturbation strategy is introduced, effectively enhancing its ability to escape local optima. Simulations on 17 benchmark functions test functions validate the effectiveness of the improved strategies. Moreover, the application of ODBO to the job-shop scheduling problem confirms its effectiveness and reliability in solving real-world engineering problems.
文摘针对BDLS(Blockchain version of DLS)共识算法在含有大量节点且具有层次结构的系统中共识效率低下的问题,提出一种基于BDLS的区块链共识改进算法HBDLS(Hierarchical BDLS)。首先,根据实际应用中节点的属性将节点分为两个层次,每个高层节点分别管理一个低层节点簇;其次,将所有低层节点进行分簇共识,并将共识结果汇报至相应的高层节点;最后,所有高层节点对低层的共识结果再次共识,通过高层共识的数据将被写入区块链。理论分析和仿真实验结果表明,在36个节点且单个区块包含4500个交易的情况下,HBDLS的吞吐量相较于BDLS算法提高了21%;在44个节点且单个区块包含3000个交易的情况下,HBDLS的吞吐量相较于BDLS算法提高了约52%;在44个节点且单个区块包含1个交易的情况下,HBDLS的共识时延相较于BDLS算法下降了26%。实验结果表明,在节点数多且交易量大的系统中,HBDLS能够大幅提高系统的共识效率。
