To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damagin...To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.展开更多
综合能源系统(integrated energy system,IES)参与电力现货市场交易时,由于市场供需关系的变化导致交易价格具有不确定性。因此,对综合能源系统运行边际成本进行精细化分析,研究充分利用综合能源系统灵活性资源参与市场的最优调度策略...综合能源系统(integrated energy system,IES)参与电力现货市场交易时,由于市场供需关系的变化导致交易价格具有不确定性。因此,对综合能源系统运行边际成本进行精细化分析,研究充分利用综合能源系统灵活性资源参与市场的最优调度策略。首先,分析了外部现货市场环境下市场价格不确定性典型场景处理方法,并研究了综合能源系统内部多种源荷可调资源及运行成本结构;其次,建立了在电力市场价格不确定性条件下考虑系统边际成本交易优化模型,并提出沙猫群优化算法进行求解。最后,通过对实际案例的仿真验证。结果表明:该策略不仅可以降低IES的运行成本,还能增强其对市场价格不确定性的适应能力,为综合能源系统在电力现货市场环境下的运行提供了新的思路和方法,有助于实现能源系统参与市场调度的经济性和可靠性双重优化。展开更多
针对农业温室复杂环境中的超宽带(Ultra wide band,UWB)定位精度受非视距(Non line of sight,NLOS)效应和多路径影响的问题,本文提出了一种融合Chan-Taylor与改进沙猫群优化粒子滤波(Chan-Taylor and improved sand cat swarm intellige...针对农业温室复杂环境中的超宽带(Ultra wide band,UWB)定位精度受非视距(Non line of sight,NLOS)效应和多路径影响的问题,本文提出了一种融合Chan-Taylor与改进沙猫群优化粒子滤波(Chan-Taylor and improved sand cat swarm intelligence optimization particle filter,CT+ISCSO-PF)定位算法。首先,利用Chan-Taylor算法实现对目标初始位置的快速估算,为粒子滤波提供准确初值;随后,引入ISCSO(Improved sand cat swarm optimization particle filter)引导粒子向高似然区域移动,通过三角游走策略提升全局搜索能力,结合Levy飞行机制增强局部收敛效率,从而有效抑制粒子退化问题。本文模拟了3种不同噪声水平的环境。仿真结果表明,CT+ISCSO-PF算法在3种环境下,相比于传统的粒子滤波(Particle filter,PF)、Chan-Taylor与粒子滤波(Chan-Taylor and particle filter,CT+PF)、Chan-Taylor与沙猫群优化粒子滤波(Chan-Taylor and sand cat swarm intelligence optimization particle filter,CT+SCSO-PF)、Chan-Taylor与灰狼优化粒子滤波(Chan-Taylor and grey wolf optimizer particle filter,CT+GWO-PF)均表现出明显优势。进一步以农用履带车辆为载体开展温室环境定位试验,结果显示:在LOS场景下,该算法较PF、CT+PF、CT+SCSO-PF和CT+GWO-PF的均方根误差分别降低27.9%、17.8%、7.8%和10.2%;在NLOS场景下,均方根误差降幅分别达21.4%、15.6%、7.6%和5.2%。展开更多
为推动名优茶叶采摘自动化,茶叶采摘机械臂快速、高质量路径规划是实现高效采摘的关键。针对传统群智能优化算法在茶园复杂环境及约束条件下存在的路径质量差、算法耗时长及规划不稳定等问题。提出一种改进豪猪优化器(Crested Porcupine...为推动名优茶叶采摘自动化,茶叶采摘机械臂快速、高质量路径规划是实现高效采摘的关键。针对传统群智能优化算法在茶园复杂环境及约束条件下存在的路径质量差、算法耗时长及规划不稳定等问题。提出一种改进豪猪优化器(Crested Porcupine Optimizer,CPO)的机械臂路径规划方法。通过引入动态种群收缩策略,在迭代过程中缩减种群规模,减少计算成本,使用末位淘汰机制及对算法结构改良提升全局寻优能力,增加个体多样性,并引入动态调整因子λ_t改进第一防御策略,平衡算法在不同阶段的探索与优化比例。通过Lindenmayer系统及UR5机械臂构建茶叶采摘仿真场景,进行仿真路径规划实验。在10个不同环境中,改进CPO算法相比原算法,平均计算时间减少4.7%,平均路径长度缩短0.78%;与灰狼优化(Grey Wolf Optimizer,GWO)、蜣螂优化(Dung Beetle Optimizer,DBO)、快速扩展随机树(Rapidly-exploring Random Trees,RRT)等算法相比,平均耗时相较GWO、DBO分别下降25%、24%,路径长度相较RRT算法减少23%、平均规划成功率高28%。改进CPO算法相较其他算法耗时更短,同时具有更好的路径质量及规划成功率,验证了其在茶叶采摘机械臂路径规划问题上的实用价值。展开更多
基金Sponsored by Beijing Priority Laboratory Fund of China(SYS10070522)
文摘To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.
文摘综合能源系统(integrated energy system,IES)参与电力现货市场交易时,由于市场供需关系的变化导致交易价格具有不确定性。因此,对综合能源系统运行边际成本进行精细化分析,研究充分利用综合能源系统灵活性资源参与市场的最优调度策略。首先,分析了外部现货市场环境下市场价格不确定性典型场景处理方法,并研究了综合能源系统内部多种源荷可调资源及运行成本结构;其次,建立了在电力市场价格不确定性条件下考虑系统边际成本交易优化模型,并提出沙猫群优化算法进行求解。最后,通过对实际案例的仿真验证。结果表明:该策略不仅可以降低IES的运行成本,还能增强其对市场价格不确定性的适应能力,为综合能源系统在电力现货市场环境下的运行提供了新的思路和方法,有助于实现能源系统参与市场调度的经济性和可靠性双重优化。
文摘为推动名优茶叶采摘自动化,茶叶采摘机械臂快速、高质量路径规划是实现高效采摘的关键。针对传统群智能优化算法在茶园复杂环境及约束条件下存在的路径质量差、算法耗时长及规划不稳定等问题。提出一种改进豪猪优化器(Crested Porcupine Optimizer,CPO)的机械臂路径规划方法。通过引入动态种群收缩策略,在迭代过程中缩减种群规模,减少计算成本,使用末位淘汰机制及对算法结构改良提升全局寻优能力,增加个体多样性,并引入动态调整因子λ_t改进第一防御策略,平衡算法在不同阶段的探索与优化比例。通过Lindenmayer系统及UR5机械臂构建茶叶采摘仿真场景,进行仿真路径规划实验。在10个不同环境中,改进CPO算法相比原算法,平均计算时间减少4.7%,平均路径长度缩短0.78%;与灰狼优化(Grey Wolf Optimizer,GWO)、蜣螂优化(Dung Beetle Optimizer,DBO)、快速扩展随机树(Rapidly-exploring Random Trees,RRT)等算法相比,平均耗时相较GWO、DBO分别下降25%、24%,路径长度相较RRT算法减少23%、平均规划成功率高28%。改进CPO算法相较其他算法耗时更短,同时具有更好的路径质量及规划成功率,验证了其在茶叶采摘机械臂路径规划问题上的实用价值。
