Novel binary-addition tree algorithm (BAT) for binary-state network reliability problem

Abstract

Various real-life applications, for example, Internet of Things, wireless sensor networks, smart grids, transportation networks, communication networks, social networks, and computer grid systems, are always modeled as network structures. The binary-state network composed of binarystate (e.g., functioning or failed) components (arcs and/or nodes) is one of the most popular network structures. The two-terminal network reliability is a success probability that the network is still functioning and can be calculated by verifying the connectivity between two specific nodes, and is an effective and popular technique for evaluating the performance of all types of networks. To obtain complete information for a making better decisions, a multi-terminal network reliability extends the two specific nodes to a specific node subset in which all nodes are connected. In this study, a new algorithm called the all-multiterminal BAT is proposed by revising the binary-addition-tree algorithm (BAT) and the layered-search algorithm (LSA) to calculate all multi-terminal reliabilities. The efficiency and effectiveness of the proposed all-multiterminal BAT are analyzed from the perspective of time complexity and explained via numerical experiments to solve the all-multiterminal network reliability problems.

Predicting Spread Probability of Learning‐Effect Computer Virus

“…e BAT proposed by Yeh [40] is a heuristic search method similar to the depth-first search (DFS), breadth-first search (BFS), and universal generating function methodology (UGFM). e BAT is more efficient than the DFS and more economical with regard to computer memory than the BFS and UGFM, both of which can crash the computer system because of computer memory overflow problems.…”

“…Let X be a binary-state vector with an n-tuple and X i be the value of its i th coordinate (Algorithm 2). e source code of the BAT is presented below [40]:…”

“…In realistic industrial processes, the operation time is reduced because the workers' skill or the flow process improves steadily, and this phenomenon is also called the learning effect. In many real-world applications [7,28,29,40,[43][44][45][46][47][48][49], the learning effect is pragmatic. Hence, the learning effect is introduced in this work to study the proposed computer-virus spread area prediction problem and offers a defense mechanism (antivirus protection) in the scale-free model to prevent or slow down virus propagation.…”

“…where α represents the learning rate and is set to 0.35, as in [39][40][41]. Owing to the learning effect, lim t⟶∞ p i,j,t � 0 and p i,j,t < p * i,j,t ≤ 1 for all t * < t < ∞ and 0 < p i,j,t , in accordance with Equation (6).…”

“…Similar to the DFS, BFS, and UGFM, the BAT is an implicit enumeration search method that can find all the feasible state vectors. However, the BAT is easier to code, more flexible to modify, and more efficient in execution than the other methods [40,50,51]. Hence, the BAT is adopted in this study and developed in this section formally to solve the proposed problem.…”