MEA-CNDP: A Membrane Evolutionary Algorithm for Solving Biobjective Critical Node Detection Problem

The critical node detection problem (CNDP) refers to the identification of one or more nodes that have a significant impact on the entire complex network according to the importance of each node in a complex network. Most methods consider the CNDP as a single-objective optimization problem, which requires more prior knowledge to a certain extent. This paper proposes a membrane evolution algorithm MEA-CNDP to solve biobjective CNDP. MEA-CNDP includes a population initialization strategy based on the evaluation of decision variables, a strategy to transform the main objective, a strategy to update the membrane inherited pool, and four membrane evolutionary operators. The numerical experiments on 16 benchmark problems with random and logarithmic weights show that MEA-CNDP outperforms other algorithms in most cases. In particular, MEA-CNDP has unique advantages in dealing with large-scale sparse bi-CNDP.

Approaching the bi-objective critical node detection problem with a smart initialization-based evolutionary algorithm

Determining the critical nodes in a complex network is an essential computation problem. Several variants of this problem have emerged due to its wide applicability in network analysis. In this article we study the bi-objective critical node detection problem (BOCNDP), which is a new variant of the well-known critical node detection problem, optimizing two objectives at the same time: maximizing the number of connected components and minimizing the variance of their cardinalities. Evolutionary multi-objective algorithms (EMOA) are a straightforward choice to solve this type of problem. We propose three different smart initialization strategies which can be incorporated into any EMOA. These initialization strategies take into account the basic properties of the networks. They are based on the highest degree, random walk (RW) and depth-first search. Numerical experiments were conducted on synthetic and real-world network data. The three different initialization types significantly improve the performance of the EMOA.

Fault Tolerance Model for Efficient Actor Recovery Paradigm in WSAN

Wireless sensor and actor networks (WSAN) is an area where sensors and actors collaborate to sense, handle and perform tasks in real-time. Thus, reliability is an important factor. Due to the natural of WSAN, actor nodes are open to failure. Failure of actor nodes degrades the network performance and may lead to network disjoint. Thus, fault tolerance techniques should be applied to insure the efficiency of the network. In an earlier work, the authors proposed an efficient actor recovery paradigm (EAR) for WSAN which handles the critical actor node failure and recovery while maintaining QoS. EAR is supported with node monitoring and critical node detection (NMCND), network integration and message forwarding (NIMF), priority-based routing for node failure avoidance (PRNFA) and backup selection algorithms. In this article, the authors extend the work by adding a fault tolerance mathematical model. By evaluating the model, EAR shows to manage fault tolerance in deferent levels. To evaluate the effectiveness, the EAR fault tolerance is evaluated by simulation using OMNET++ Simulation. In addition, EAR reliability is measured and compared with RNF, DPCRA, ACR, and ACRA.