水电站应用传统的无功分配方法,只能将总的无功需求量在机组间简单分配.为满足水电站自动电压控制(Automatic Voltage Control,AVC)的多目标调节需求,对进化规划算法进行改进,利用考虑决策者偏好信息的方法优化进化规划算法中随机产生...水电站应用传统的无功分配方法,只能将总的无功需求量在机组间简单分配.为满足水电站自动电压控制(Automatic Voltage Control,AVC)的多目标调节需求,对进化规划算法进行改进,利用考虑决策者偏好信息的方法优化进化规划算法中随机产生初始寻优种群的过程,以实现水电站中有功网络损耗最小化和无功收益最大化;结合算例与仿真验证,将改进的进化规划算法得到的分配结果与水电站工程实例中应用的等功率因数法作对比,结果显示改进的进化规划算法比等功率因数法在减少有功网络损耗方面,单次调节降低了1.6%;在增加无功收益方面,单次调节增加了978元,优化效果明显,说明改进的进化规划算法可用于求解有功网络损耗最小化和无功收益最大化问题.展开更多
To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.Fir...To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.展开更多
文摘水电站应用传统的无功分配方法,只能将总的无功需求量在机组间简单分配.为满足水电站自动电压控制(Automatic Voltage Control,AVC)的多目标调节需求,对进化规划算法进行改进,利用考虑决策者偏好信息的方法优化进化规划算法中随机产生初始寻优种群的过程,以实现水电站中有功网络损耗最小化和无功收益最大化;结合算例与仿真验证,将改进的进化规划算法得到的分配结果与水电站工程实例中应用的等功率因数法作对比,结果显示改进的进化规划算法比等功率因数法在减少有功网络损耗方面,单次调节降低了1.6%;在增加无功收益方面,单次调节增加了978元,优化效果明显,说明改进的进化规划算法可用于求解有功网络损耗最小化和无功收益最大化问题.
基金Project(60925011) supported by the National Natural Science Foundation for Distinguished Young Scholars of ChinaProject(9140A06040510BQXXXX) supported by Advanced Research Foundation of General Armament Department,China
文摘To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.
