Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to f...Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to fall into local optimum and leads to slow convergence speed.The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms.In the present study,we propose a chaos-enhanced MFO(CMFO)by incorporating chaos maps into the MFO algorithm to enhance its performance.The chaotic map is utilized to initialize the moths’population,handle the boundary overstepping,and tune the distance parameter.The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one.The performance of the CMFO is also verified by using two real engineering problems.The statistical results clearly demonstrate that the appropriate chaotic map(singer map)embedded in the appropriate component of MFO can significantly improve the performance of MFO.展开更多
针对新能源渗透率提升带来的电压稳定风险,同时考虑柔性互联装置逐步在电力系统试点应用的背景,提出一种考虑电压稳定的含智能储能软开关(soft open point with energy storage system integration,E-SOP)配电系统分布式电源双层规划模...针对新能源渗透率提升带来的电压稳定风险,同时考虑柔性互联装置逐步在电力系统试点应用的背景,提出一种考虑电压稳定的含智能储能软开关(soft open point with energy storage system integration,E-SOP)配电系统分布式电源双层规划模型。首先,分析电压稳定指标及E-SOP的作用机理。其次,基于拉丁超立方采样和经K-medoids算法融合的改进同步回代缩减法得到典型概率日场景。然后,建立含E-SOP接入的双层规划模型,上层模型以年综合费用最小为目标,对风电、光伏等设备进行选址定容;下层模型以电压稳定性、网络损耗、平均电压偏移等为目标,实施含E-SOP的有功无功协同优化。最后,采用改进飞蛾扑火算法进行模型求解。经IEEE 33节点配电系统算例分析,其结果表明,该模型能有效提高配电系统的经济性和实时运行的电压稳定性,验证了求解算法的优越性。展开更多
基金supported by the Military Science Project of the National Social Science Foundation of China(15GJ003-141)
文摘Moth-flame optimization(MFO)is a novel metaheuristic algorithm inspired by the characteristics of a moth’s navigation method in nature called transverse orientation.Like other metaheuristic algorithms,it is easy to fall into local optimum and leads to slow convergence speed.The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms.In the present study,we propose a chaos-enhanced MFO(CMFO)by incorporating chaos maps into the MFO algorithm to enhance its performance.The chaotic map is utilized to initialize the moths’population,handle the boundary overstepping,and tune the distance parameter.The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one.The performance of the CMFO is also verified by using two real engineering problems.The statistical results clearly demonstrate that the appropriate chaotic map(singer map)embedded in the appropriate component of MFO can significantly improve the performance of MFO.
文摘针对新能源渗透率提升带来的电压稳定风险,同时考虑柔性互联装置逐步在电力系统试点应用的背景,提出一种考虑电压稳定的含智能储能软开关(soft open point with energy storage system integration,E-SOP)配电系统分布式电源双层规划模型。首先,分析电压稳定指标及E-SOP的作用机理。其次,基于拉丁超立方采样和经K-medoids算法融合的改进同步回代缩减法得到典型概率日场景。然后,建立含E-SOP接入的双层规划模型,上层模型以年综合费用最小为目标,对风电、光伏等设备进行选址定容;下层模型以电压稳定性、网络损耗、平均电压偏移等为目标,实施含E-SOP的有功无功协同优化。最后,采用改进飞蛾扑火算法进行模型求解。经IEEE 33节点配电系统算例分析,其结果表明,该模型能有效提高配电系统的经济性和实时运行的电压稳定性,验证了求解算法的优越性。
文摘针对无人机长期跟踪过程中尺度变换导致目标丢失和跟踪精度低的问题,提出了一种基于飞蛾扑火优化(moth-flame optimization,MFO)的尺度比例感知空间长期跟踪器。首先,设计了高斯初始化以代替飞蛾扑火优化算法的随机初始化策略,降低优化算法在跟踪过程中的计算复杂度,减少算力浪费;其次,结合快速梯度直方图特征,构建了改进的飞蛾扑火优化跟踪器;然后,为了解决无人机航拍长期跟踪中目标尺度变化的问题,设计了一种自适应尺度变换的判别尺度空间跟踪(discriminative scale space tracking,DSST)算法,进一步提出了一种尺度比例感知空间跟踪器,解决了尺度滤波器中因长宽比固定而导致的跟踪漂移;同时,分析了滤波器响应峰值在各背景下的变化情况,提出了一种能反映环境变化下跟踪置信度的指标,并通过置信度将MFO优化跟踪框架与尺度比例感知空间跟踪器相结合,解决了尺度变化与长期跟踪目标丢失的问题;最后,在无人机长期跟踪数据集上开展了性能验证。结果表明:提出的算法可有效防止漂移现象的发生,提升跟踪效率;与目前跟踪领域中12种同类文献算法进行对比可知,提出的算法精度较高,满足实时性,能够有效解决无人机长期跟踪下的尺度变化及目标丢失等问题。
