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In the field of graph theory, the shortest path problem is one of the most significant problems. However, since varieties of indeterminated factors appear in complex networks, determining of the shortest path from one vertex to another in complex networks may be a lot more complicated than the cases in deterministic networks. To illustrate this problem, the model of uncertain random digraph will be proposed via chance theory, in which some arcs exist with degrees in probability measure and others exist with degrees in uncertain measure. The main focus of this paper is to investigate the main properties of the shortest path in uncertain random digraph. Methods and algorithms are designed to calculate the distribution of shortest path more efficiently. Besides, some numerical examples are presented to show the efficiency of these methods and algorithms. [ABSTRACT FROM AUTHOR]

Most of the time, people experience difficulty in identifying the best shortest paths to reach their destination while travelling. This becomes the main motive to research the shortest path identification problem. In this work, an undirected network is considered for the research evaluation of the proposed method. Previously, various types of techniques have been developed by many researchers for this shortest path issue in a networking environment. However, most of them exhibit computational complexity issues when updating their weights for every iteration. Based on this observation, the proposed work plans to solve SPP (Shortest Path Problem) in an undirected network using Fuzzy Dijkstra's approach based on improved WSM (Weighted Sum Method) with heuristic optimization. Here, the weight updating process of WSM is optimized by Sea Lion Optimization approach in order to improve the working performance of WSM while combining with Fuzzy Dijkstra method. The proposed concept is evaluated in MATLAB, and the performance evaluation is done using time consumption, energy utilization, network lifetime and prediction accuracy by comparing it with Dijkstra, Fuzzy-MPSO and WSDA methods. [ABSTRACT FROM AUTHOR]