When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game ...When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.展开更多
针对动态不确定战场环境下多无人机对多区域、多目标的协同察打任务规划过程中存在的信息不确定、任务多约束及航迹强耦合的多目标优化与决策问题,结合Dubins航迹规划算法,提出了一种融合多种改进策略的灰狼优化算法(grey wolf optimiza...针对动态不确定战场环境下多无人机对多区域、多目标的协同察打任务规划过程中存在的信息不确定、任务多约束及航迹强耦合的多目标优化与决策问题,结合Dubins航迹规划算法,提出了一种融合多种改进策略的灰狼优化算法(grey wolf optimization algorithm incorporating multiple improvement strategies,IMISGWO).首先,针对动态环境带来的无人机巡航速度及察打任务消失时间的不确定性,基于可信性理论建立了以最大化任务收益为指标的任务规划数学模型;其次,为实现该问题的快速求解,设计了初始解均匀分布、个体通信机制调整、动态权重更新和跳出局部最优等策略,提升算法解搜索能力;最后,构建了多无人机察打一体典型任务仿真场景,通过数字仿真以及虚实结合半实物仿真试验验证了算法的可行性和有效性.仿真结果表明:算法在求解不确定环境下耦合航迹的多无人机察打一体任务规划问题时,能够生成多机高效的任务执行序列和满足无人机飞行性能约束的飞行轨迹,且能够适用于无人机数量增加导致问题复杂度增加情形下此类问题的求解.展开更多
为解决光伏序列的强噪音干扰以及单一模型在光伏功率预测方面精度偏低和泛化性较差的问题,提出了一种基于特征优化和混合改进灰狼算法优化双向长短时记忆网络(bi-directional long short-term memory,BiLSTM)的短期光伏功率预测方法。首...为解决光伏序列的强噪音干扰以及单一模型在光伏功率预测方面精度偏低和泛化性较差的问题,提出了一种基于特征优化和混合改进灰狼算法优化双向长短时记忆网络(bi-directional long short-term memory,BiLSTM)的短期光伏功率预测方法。首先,运用互信息算法进行输入数据的变量选择,以消除冗余变量。其次,通过互补集合经验模态分解和改进的小波阈值算法对筛选后的数据进行特征重构,旨在降低数据中的噪声干扰并完成输入变量的特征优化。随后,结合改进的Tent混沌映射、非线性递减因子、动态权重策略和差分进化算法对标准灰狼优化算法进行混合优化,以确定双向长短期记忆神经网络的最优超参数组合,并引入注意力机制以挖掘数据中的关键时序信息,最终构建出一种新型的短期光伏功率预测模型。仿真实验表明,相较于最小二乘支持向量机、长短期记忆网络和双向长短期记忆网络,所提模型在晴天、多云、阴天和降雨等不同工况下的均方根误差平均分别降低了12.45%、7.95%和5.37%,显示出优秀的预测性能、良好的泛化能力和潜在的工程应用价值。展开更多
基金National Natural Science Foundation of China(NSFC61773142,NSFC62303136)。
文摘When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
文摘为解决光伏序列的强噪音干扰以及单一模型在光伏功率预测方面精度偏低和泛化性较差的问题,提出了一种基于特征优化和混合改进灰狼算法优化双向长短时记忆网络(bi-directional long short-term memory,BiLSTM)的短期光伏功率预测方法。首先,运用互信息算法进行输入数据的变量选择,以消除冗余变量。其次,通过互补集合经验模态分解和改进的小波阈值算法对筛选后的数据进行特征重构,旨在降低数据中的噪声干扰并完成输入变量的特征优化。随后,结合改进的Tent混沌映射、非线性递减因子、动态权重策略和差分进化算法对标准灰狼优化算法进行混合优化,以确定双向长短期记忆神经网络的最优超参数组合,并引入注意力机制以挖掘数据中的关键时序信息,最终构建出一种新型的短期光伏功率预测模型。仿真实验表明,相较于最小二乘支持向量机、长短期记忆网络和双向长短期记忆网络,所提模型在晴天、多云、阴天和降雨等不同工况下的均方根误差平均分别降低了12.45%、7.95%和5.37%,显示出优秀的预测性能、良好的泛化能力和潜在的工程应用价值。
