This paper presents a Butterfly Optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as WDBOA.To further balance the basic BOA algorithm's exploration and exploitation capabil...This paper presents a Butterfly Optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as WDBOA.To further balance the basic BOA algorithm's exploration and exploitation capabilities,the butterfly actions were divided into downwind and upwind states.The algorithm of exploration ability was improved with the wind,while the algorithm of exploitation ability was improved against the wind.Also,a mechanism of avoiding natural enemies based on Lévy flight was introduced for the purpose of enhancing its global searching ability.Aiming at improving the explorative performance at the initial stages and later stages,the fragrance generation method was modified.To evaluate the effectiveness of the suggested algorithm,a comparative study was done with six classical metaheuristic algorithms and three BOA variant optimization techniques on 18 benchmark functions.Further,the performance of the suggested technique in addressing some complicated problems in various dimensions was evaluated using CEC 2017 and CEC 2020.Finally,the WDBOA algorithm is used proportional-integral-derivative(PID)controller parameter optimization.Experimental results demonstrate that the WDBOA based PID controller has better control performance in comparison with other PID controllers tuned by the Genetic Algorithm(GA),Flower Pollination Algorithm(FPA),Cuckoo Search(CS)and BOA.展开更多
针对风驱动优化(Wind Driven Optimization,WDO)算法易陷入局部最优的问题,提出了一种基于Levy飞行机制的风驱动优化算法(Wind Driven Optimization based on Levy flight,LWDO)。Levy飞行独特的随机游走行为能够有效提升算法种群的多...针对风驱动优化(Wind Driven Optimization,WDO)算法易陷入局部最优的问题,提出了一种基于Levy飞行机制的风驱动优化算法(Wind Driven Optimization based on Levy flight,LWDO)。Levy飞行独特的随机游走行为能够有效提升算法种群的多样性、扩大搜索范围,在风驱动算法空气质点初始位置更新完成后采用飞行概率选择性的对质点进行Levy飞行,可有效提高风驱动算法的种群全局搜索能力。最后,采用不同维度测试函数分别对原始风驱动优化算法、小波变异风驱动优化算法及LWDO算法进行比较。实验结果表明,在求解全局最优时LWDO算法能够及时跳出局部最优点,其寻优效率、寻优速率和算法稳定性都优于其他两种算法,尤为适合多峰值函数的迭代求解。展开更多
风电场/场群规模化接入电网背景下,电网的故障暂态特性发生了根本性改变。然而,现有单机等值型无法精确表征风电场/场群的故障暂态特性。该文提出一种基于特征影响因子和改进人工神经网络反向传播(backpropagation neuronnetworks,BP)...风电场/场群规模化接入电网背景下,电网的故障暂态特性发生了根本性改变。然而,现有单机等值型无法精确表征风电场/场群的故障暂态特性。该文提出一种基于特征影响因子和改进人工神经网络反向传播(backpropagation neuronnetworks,BP)算法的直驱风机风电场建模方法。首先,建立直驱风机暂态模型,通过理论分析构建风机与公共连接点(point of common coupling,PCC)距离、直流侧限流措施投入情况、风速、出口处无功功率等故障特征影响因子。然后,对特征影响因子集计算欧式距离,基于改进最大最小距离法提取风机的分类初始中心。通过改进BP算法,以特征影响因子和分类初始中心为训练集,实现神经网络的快速收敛。最后,通过仿真算例,对所提方法进行验证。仿真结果表明,所述方法在收敛速度、建模精度方面,与传统BP算法和单机等值建模方法相比均有较大提升。展开更多
基金This work was supported by National Natural Science Foundation of China under Grant U21A20464,62066005Project of the Guangxi Science and Technology under Grant No.ZL23014016.
文摘This paper presents a Butterfly Optimization Algorithm(BOA)with a wind-driven mechanism for avoiding natural enemies known as WDBOA.To further balance the basic BOA algorithm's exploration and exploitation capabilities,the butterfly actions were divided into downwind and upwind states.The algorithm of exploration ability was improved with the wind,while the algorithm of exploitation ability was improved against the wind.Also,a mechanism of avoiding natural enemies based on Lévy flight was introduced for the purpose of enhancing its global searching ability.Aiming at improving the explorative performance at the initial stages and later stages,the fragrance generation method was modified.To evaluate the effectiveness of the suggested algorithm,a comparative study was done with six classical metaheuristic algorithms and three BOA variant optimization techniques on 18 benchmark functions.Further,the performance of the suggested technique in addressing some complicated problems in various dimensions was evaluated using CEC 2017 and CEC 2020.Finally,the WDBOA algorithm is used proportional-integral-derivative(PID)controller parameter optimization.Experimental results demonstrate that the WDBOA based PID controller has better control performance in comparison with other PID controllers tuned by the Genetic Algorithm(GA),Flower Pollination Algorithm(FPA),Cuckoo Search(CS)and BOA.
文摘针对风驱动优化(Wind Driven Optimization,WDO)算法易陷入局部最优的问题,提出了一种基于Levy飞行机制的风驱动优化算法(Wind Driven Optimization based on Levy flight,LWDO)。Levy飞行独特的随机游走行为能够有效提升算法种群的多样性、扩大搜索范围,在风驱动算法空气质点初始位置更新完成后采用飞行概率选择性的对质点进行Levy飞行,可有效提高风驱动算法的种群全局搜索能力。最后,采用不同维度测试函数分别对原始风驱动优化算法、小波变异风驱动优化算法及LWDO算法进行比较。实验结果表明,在求解全局最优时LWDO算法能够及时跳出局部最优点,其寻优效率、寻优速率和算法稳定性都优于其他两种算法,尤为适合多峰值函数的迭代求解。
文摘风电场/场群规模化接入电网背景下,电网的故障暂态特性发生了根本性改变。然而,现有单机等值型无法精确表征风电场/场群的故障暂态特性。该文提出一种基于特征影响因子和改进人工神经网络反向传播(backpropagation neuronnetworks,BP)算法的直驱风机风电场建模方法。首先,建立直驱风机暂态模型,通过理论分析构建风机与公共连接点(point of common coupling,PCC)距离、直流侧限流措施投入情况、风速、出口处无功功率等故障特征影响因子。然后,对特征影响因子集计算欧式距离,基于改进最大最小距离法提取风机的分类初始中心。通过改进BP算法,以特征影响因子和分类初始中心为训练集,实现神经网络的快速收敛。最后,通过仿真算例,对所提方法进行验证。仿真结果表明,所述方法在收敛速度、建模精度方面,与传统BP算法和单机等值建模方法相比均有较大提升。
