https://onlineresource.ucsy.edu.mm/handle/123456789/26 Sat, 18 Jul 2026 00:40:22 GMT 2026-07-18T00:40:22Z https://onlineresource.ucsy.edu.mm/handle/123456789/2541 A novel solution for simultaneously finding the shortest and possible paths in complex networks Hlaing, Wai Mar; Liu, Shi-Jian; Pan, Jeng-Shyang A Novel graph approach named Combined Forward and Backward Heuristic Search (CFBHS) is proposed in this paper, which can be used to solve optimization problems in areas such as transportation and network routing. There are two major aspects distinct our method from the most cited ones. Firstly, though focuses on getting the shortest path in a graph when both source and destination are given, this work can also find other possible paths as outputs. Secondly, the proposed algorithm is a high-performance one, which is achieved by (1) reducing unnecessary nodes and edges to reach a target optimum based on dynamically calculated heuristic values and (2) finding the results by using the subdivision scheme instead of computing over the whole graph. Experiments are carried out for the complex road network of Yangon Region. The comparisons show that our algorithm is about 100 times faster than the bidirectional Dijkstra’s algorithm. Besides, benefit from the heuristic forward and backward search, the proposed method can achieve very low time complexity, which is similar to the A*, but A* can only produce the shortest path. By contrast, the proposed algorithm is competent for finding not only the shortest but also many possible paths in complex road networks such as undirected graph and hypergraph networks. Fri, 01 Nov 2019 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2541 2019-11-01T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2540 Neighbor Search with Hash Map Indexing Technique for Complex Networks Hlaing, Wai Mar; Sein, Myint Myint Neighbor Search with Hash Map Indexing Technique is used to get the high performance when the optimal path is searched in the complex networks. This system can also give advice the public bus passengers about the travel route depend on the travel time and cost. Moreover, the proposed technique is highly performance one if it compares about the response time of many other popular cited shortest path algorithms. Especially it contains two main parts for finding the optimal path, the first one is dividing the complex large tree into small sub-trees using divide and conquer at an optimal threshold value. The second one is using heuristic neighbor search instead of searching the heuristic values of all expanded nodes at current level. Heuristic neighbor search and hash-map indexing technique is used together to reduce the time complexity when the heuristic values are searched dynamically depend on the user query to reach the target. The proposed system is faster than the popular bi-directional heuristic search A* algorithm, previously proposed combined forward and backward heuristic search algorithm and modified heuristic search algorithm. Road network and bus network in Yangon Region is used as the case study for spatial database. Wed, 26 Feb 2020 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2540 2020-02-26T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2539 K-means Nearest Point Search Algorithm and Heuristic Search for Transportation Hlaing, Wai Mar; Sein, Myint Myint This paper aims to support the most suitable route for passengers of the taxi system using the proposed heuristic search method. Furthermore, k-Means Nearest Point Search (kMNPS) algorithm is proposed to produce the nearest road point for start and end addresses. Yangon downtown in Myanmar is selected as a case study for the transportation system. The proposed heuristic method and kMNPS algorithm reduce the distance calculations and achieve the very low time complexity for the real time transportation applications. Moreover, the proposed system can produce not only the optimal route on the map but also the popular spots near the optimal route. Thu, 13 Dec 2018 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2539 2018-12-13T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2538 Search Space Reduction using K-means Clustering and Adjacency matrices for GIS Usage Information Retrieval Hlaing, Wai Mar; Sein, Myint Myint Nowadays, people widespread use of smartphones and ubiquitous devices for map based services. As the transport network is complicated and massive, people may be confused to reach the desired location after finding a location. Many searching techniques are used for finding the shortest path, might still not be fast enough in certain realtime applications because of complexing transport network. Search time can be reduced if we pruned unnecessary clusters in a complex large graph. Memory utilization is safe for the processing time if we reduce search space in complex network. For removing unnecessary clusters, adjacency matrices, distance based methods and K-means clustering can be used. ArcGIS software and popular shortest path algorithms are applied to find the shortest path from one location to another on the Android mobile platform. In addition, the performance of finding the shortest path using popular A* and Dijkstra algorithms with bidirectional search can be compared before and after removing unnecessary clusters. Sat, 10 Dec 2016 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2538 2016-12-10T00:00:00Z