Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T...Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.展开更多
Coverage control for each sensor is based on a 2D directional sensing model in directional sensor networks conventionally. But the 2D model cannot accurately characterize the real environment. In order to solve this p...Coverage control for each sensor is based on a 2D directional sensing model in directional sensor networks conventionally. But the 2D model cannot accurately characterize the real environment. In order to solve this problem,a new 3D directional sensor model and coverage enhancement algorithm is proposed. We can adjust the pitch angle and deviation angle to enhance the coverage rate. And the coverage enhancement algorithm is based on an improved gravitational search algorithm. In this paper the two improved strategies of GSA are directional mutation strategy and individual evolution strategy. A set of simulations show that our coverage enhancement algorithm has a good performance to improve the coverage rate of the wireless directional sensor network on different number of nodes,different virtual angles and different sensing radius.展开更多
针对跳点搜索(jump point search,JPS)算法路径存在斜向穿越障碍物、搜索过程中存在较多冗余跳点、路径拐点多且靠近障碍物的问题,提出一种安全快速的跳点搜索(safe fast jump point search,SFJPS)算法。该算法重新定义跳点判断规则,使...针对跳点搜索(jump point search,JPS)算法路径存在斜向穿越障碍物、搜索过程中存在较多冗余跳点、路径拐点多且靠近障碍物的问题,提出一种安全快速的跳点搜索(safe fast jump point search,SFJPS)算法。该算法重新定义跳点判断规则,使生成的跳点均为安全跳点,解决了路径中斜向穿越障碍物的情况;加入基于角度的搜索方向优先级判断,有效减少了搜索过程中的冗余节点,加快了搜索速度;基于Bresenham算法对路径上的跳点进行关键跳点筛选,关键跳点生成的路径拐点明显减少,贴近障碍物的路径长度大幅减小,整体路径长度也有所减小。结果表明在不同场景下本文算法相较于A*算法和JPS算法,路径长度分别最大减小了5.42%和4.48%,搜索时间分别最大缩短了98.33%和67.83%,搜索节点数最大减少了99.08%和56.72%,路径拐点数分别最大减少了90.91%和83.33%。相较于Theta*算法路径长度增加了1.17%,搜索时间缩短了91.07%,搜索节点数减少了98.9%。仿真试验证明本文算法规划速度快,路径安全且拐点更少,更加适用于移动机器人路径规划问题。展开更多
This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration spa...This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.展开更多
为了提高异构多核处理器平台的计算性能,从任务调度的角度出发,提出了一种使用黄金正弦和莱维飞行机制改进的麻雀搜索算法(Fusion of Golden Sinusoidal and Levy Flight in Sparrow Search Algorithm,GSLF-SSA)来优化异构多核处理器的...为了提高异构多核处理器平台的计算性能,从任务调度的角度出发,提出了一种使用黄金正弦和莱维飞行机制改进的麻雀搜索算法(Fusion of Golden Sinusoidal and Levy Flight in Sparrow Search Algorithm,GSLF-SSA)来优化异构多核处理器的任务调度。通过对异构任务调度的分析,将异构任务建模为DAG(Directed Acyclic Graph)任务模型,通过对其优先级进行随机编码分配,实现了GSLF-SSA算法求解域从连续到离散的映射,使该算法更能适用于异构多核任务调度之中。将DAG任务的最优调度长度作为算法的适应度值进行迭代寻优,通过与目前应用广泛的麻雀搜索算法(SSA)、混合式任务调度算法(IHSSA)、人工蜂群算法(ABC)等多种启发式算法在异构任务调度环境下的实验对比表明,GSLF-SSA能获得更优的调度长度与更短的调度执行时间。展开更多
为解决快速扩展随机树算法(rapid-exploration random tree,RRT*)在三维环境中盲目搜索路径以及缺乏节点扩展记忆性等问题,提出一种融合蚁群算法的双向搜索算法ACO-RRT*。为适应精细化三维建模环境和解决地面起伏不平坦等问题,对RRT*算...为解决快速扩展随机树算法(rapid-exploration random tree,RRT*)在三维环境中盲目搜索路径以及缺乏节点扩展记忆性等问题,提出一种融合蚁群算法的双向搜索算法ACO-RRT*。为适应精细化三维建模环境和解决地面起伏不平坦等问题,对RRT*算法进行改进优化。采用双向搜索策略,在起点和终点同时运行改进后的RRT算法和蚁群算法,相向而行,对路径长度和运行时间进行优化。针对生成路径不够平滑等问题,引入B样条曲线平滑策略优化路径。仿真结果表明,所提算法能够有效用于机器人三维路径规划。展开更多
基金supported partly by the National Science and Technology Major Project of China(Grant No.2016ZX05025-001006)Major Science and Technology Project of CNPC(Grant No.ZD2019-183-007)
文摘Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61175126)National Research Foundation for the Doctoral Program of Higher Education of China(Grant No.20112304110009)the Fundamental Research Funds for the Central Universities of China(Grant No.HEUCFZ1209)
文摘Coverage control for each sensor is based on a 2D directional sensing model in directional sensor networks conventionally. But the 2D model cannot accurately characterize the real environment. In order to solve this problem,a new 3D directional sensor model and coverage enhancement algorithm is proposed. We can adjust the pitch angle and deviation angle to enhance the coverage rate. And the coverage enhancement algorithm is based on an improved gravitational search algorithm. In this paper the two improved strategies of GSA are directional mutation strategy and individual evolution strategy. A set of simulations show that our coverage enhancement algorithm has a good performance to improve the coverage rate of the wireless directional sensor network on different number of nodes,different virtual angles and different sensing radius.
文摘针对跳点搜索(jump point search,JPS)算法路径存在斜向穿越障碍物、搜索过程中存在较多冗余跳点、路径拐点多且靠近障碍物的问题,提出一种安全快速的跳点搜索(safe fast jump point search,SFJPS)算法。该算法重新定义跳点判断规则,使生成的跳点均为安全跳点,解决了路径中斜向穿越障碍物的情况;加入基于角度的搜索方向优先级判断,有效减少了搜索过程中的冗余节点,加快了搜索速度;基于Bresenham算法对路径上的跳点进行关键跳点筛选,关键跳点生成的路径拐点明显减少,贴近障碍物的路径长度大幅减小,整体路径长度也有所减小。结果表明在不同场景下本文算法相较于A*算法和JPS算法,路径长度分别最大减小了5.42%和4.48%,搜索时间分别最大缩短了98.33%和67.83%,搜索节点数最大减少了99.08%和56.72%,路径拐点数分别最大减少了90.91%和83.33%。相较于Theta*算法路径长度增加了1.17%,搜索时间缩短了91.07%,搜索节点数减少了98.9%。仿真试验证明本文算法规划速度快,路径安全且拐点更少,更加适用于移动机器人路径规划问题。
文摘This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.
文摘为了提高异构多核处理器平台的计算性能,从任务调度的角度出发,提出了一种使用黄金正弦和莱维飞行机制改进的麻雀搜索算法(Fusion of Golden Sinusoidal and Levy Flight in Sparrow Search Algorithm,GSLF-SSA)来优化异构多核处理器的任务调度。通过对异构任务调度的分析,将异构任务建模为DAG(Directed Acyclic Graph)任务模型,通过对其优先级进行随机编码分配,实现了GSLF-SSA算法求解域从连续到离散的映射,使该算法更能适用于异构多核任务调度之中。将DAG任务的最优调度长度作为算法的适应度值进行迭代寻优,通过与目前应用广泛的麻雀搜索算法(SSA)、混合式任务调度算法(IHSSA)、人工蜂群算法(ABC)等多种启发式算法在异构任务调度环境下的实验对比表明,GSLF-SSA能获得更优的调度长度与更短的调度执行时间。
文摘为解决快速扩展随机树算法(rapid-exploration random tree,RRT*)在三维环境中盲目搜索路径以及缺乏节点扩展记忆性等问题,提出一种融合蚁群算法的双向搜索算法ACO-RRT*。为适应精细化三维建模环境和解决地面起伏不平坦等问题,对RRT*算法进行改进优化。采用双向搜索策略,在起点和终点同时运行改进后的RRT算法和蚁群算法,相向而行,对路径长度和运行时间进行优化。针对生成路径不够平滑等问题,引入B样条曲线平滑策略优化路径。仿真结果表明,所提算法能够有效用于机器人三维路径规划。
