The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth...The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.展开更多
To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) an...To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.展开更多
Search speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A＊ （RE...Search speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A＊ （REA＊）, is presented that improves the performance of A＊ significantly. REA＊ explores maps in units of unblocked rectangles. All unnecessary points inside the rectangles are pruned and boundaries of the rectangles （instead of individual points within those boundaries） are used as search nodes. This makes the algorithm plot fewer points and have a much shorter open list than A＊. REA＊ returns jump and grid-optimal path points, but since the line of sight between jump points is protected by the unblocked rectangles, the resulting path of REA＂ is usually better than grid-optimal. The algorithm is entirely online and requires no offline pre-processing. Experimental results for typical benchmark problem sets show that REA＊ can speed up a highly optimized A＊ by an order of magnitude and more while preserving completeness and optimality. This new algorithm is competitive with other highly successful variants of A＊.展开更多
An improved hybrid Time of Arrival (ToA)/ Angle of Arrival (AoA) location algorithm by adopting Gauss-Newton iterative algorithm is proposed. It is with the advantage of fast convergence and combining with the grid-se...An improved hybrid Time of Arrival (ToA)/ Angle of Arrival (AoA) location algorithm by adopting Gauss-Newton iterative algorithm is proposed. It is with the advantage of fast convergence and combining with the grid-search-based method to optimize the initial object coordinates of the iteration, meanwhile, under the condition of small measurement errors caused by noises of ToA and AoA, the algorithm performance can be improved effectively. In the Non-Line-of-Sight (NLoS) environments of the Wireless Sensor Network (WSN), simulation results show that improved accuracy is gained with moderate flexibility and fast steady convergence compared with the existing algorithms.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.11002086)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61261007,61002049)the Key Program of Yunnan Natural Science Foundation(Grant No.2013FA008)
文摘To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.
基金supported by the National Natural Science Foundation of China (No.61573283)
文摘Search speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A＊ （REA＊）, is presented that improves the performance of A＊ significantly. REA＊ explores maps in units of unblocked rectangles. All unnecessary points inside the rectangles are pruned and boundaries of the rectangles （instead of individual points within those boundaries） are used as search nodes. This makes the algorithm plot fewer points and have a much shorter open list than A＊. REA＊ returns jump and grid-optimal path points, but since the line of sight between jump points is protected by the unblocked rectangles, the resulting path of REA＂ is usually better than grid-optimal. The algorithm is entirely online and requires no offline pre-processing. Experimental results for typical benchmark problem sets show that REA＊ can speed up a highly optimized A＊ by an order of magnitude and more while preserving completeness and optimality. This new algorithm is competitive with other highly successful variants of A＊.
基金supported by National Natural Science Foundation of China under Grant No.61172073State Key Laboratory of Networking and Switching Technology (Beijing Universityof Posts and Telecommunications) under Grant No.SKLNST-2009-1-09+1 种基金Open Research Fund of National Mobile Communications Research Laboratory, Southeast University, P. R.ChinaChina Fundamental Research Funds for the Central Universities:Beijing Jiaotong University
文摘An improved hybrid Time of Arrival (ToA)/ Angle of Arrival (AoA) location algorithm by adopting Gauss-Newton iterative algorithm is proposed. It is with the advantage of fast convergence and combining with the grid-search-based method to optimize the initial object coordinates of the iteration, meanwhile, under the condition of small measurement errors caused by noises of ToA and AoA, the algorithm performance can be improved effectively. In the Non-Line-of-Sight (NLoS) environments of the Wireless Sensor Network (WSN), simulation results show that improved accuracy is gained with moderate flexibility and fast steady convergence compared with the existing algorithms.
