Pathfinding algorithm addresses the problem of finding the shortest path from source to destination and avoiding obstacles. One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in com...Pathfinding algorithm addresses the problem of finding the shortest path from source to destination and avoiding obstacles. One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. In this work, A* search algorithm is used to find the shortest path between the source and destination on image that represents a map or a maze. Finding a path through a maze is a basic computer science problem that can take many forms. The A* algorithm is widely used in pathfinding and graph traversal. Different map and maze images are used to test the system performance (100 images for each map and maze). The system overall performance is acceptable and able to find the shortest path between two points on the images. More than 85% images can find the shortest path between the selected two points.展开更多
String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that...String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.展开更多
文摘Pathfinding algorithm addresses the problem of finding the shortest path from source to destination and avoiding obstacles. One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. In this work, A* search algorithm is used to find the shortest path between the source and destination on image that represents a map or a maze. Finding a path through a maze is a basic computer science problem that can take many forms. The A* algorithm is widely used in pathfinding and graph traversal. Different map and maze images are used to test the system performance (100 images for each map and maze). The system overall performance is acceptable and able to find the shortest path between two points on the images. More than 85% images can find the shortest path between the selected two points.
文摘String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.
