Identifying the most influential communicators as an issue of maximizing influence has become one of the most notable topics in social network analysis, as it has achieved success in viral marketing. Only by understanding our opponent's way of thinking can we effectively defend against or attack them. We particularly focus on one type of such attacks called the weight stable set attack problem with budget constraint. Given a social network, each node has a weight and removal cost. This problem is to remove a subset of the nodes whose total removal cost does not exceed a given threshold, such that the maximum weight stable set in the resulting graph is minimized. We first propose a 2$\alpha$-approximation algorithm for this problem on the graph without odd cycles, where$\alpha$is the approximation ratio for the algorithm of the minimum knapsack problem. Interestingly, this algorithm can be extended to general graphs. We also design a genetic algorithm for general graphs. Finally, we conducted many experiments on both artificial networks and real-world networks. For the graph without odd cycles, the results show that our performance ratio is less than 1.21. For general graphs, we compare the algorithm that extends the idea of graphs without odd cycles with the genetic algorithm. The results show that this algorithm is better than the genetic algorithm in some settings.

In science and engineering, multiobjective optimization problems (MOPs) usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This article aims to solve the challenges brought by multiple complex constraints. First, this article analyzes the relationship between single-constrained Pareto front (SCPF) and their common Pareto front (PF) subconstrained PF (SubCPF). Next, we discussed the SCPF, SubCPF, and unconstraint PF (UPF)’s help to solve constraining PF (CPF). Then, further discusses what kind of cooperation should be used between multiple populations constrained multiobjective optimization algorithm (CMOEA) to better deal with multiconstrained MOPs (mCMOPs). At the same time, based on the discussion in this article, we propose a new multipopulation CMOEA called MCCMO, which uses a new cooperation mechanism. MCCMO uses C+2 (C is the number of constraints) populations to find the UPF, SCPF, and SubCPF at an appropriate time. Furthermore, MCCMO uses the newly proposed activation dormancy detection (ADD) to accelerate the optimization process and uses the proposed combine occasion detection (COD) to find the appropriate time to find the SubCPF. The performance on 32 mCMOPs and real-world mCMOPs shows that our algorithm can obtain competitive solutions on MOPs with multiple constraints.

The elucidation of gene regulatory networks (GRNs) is one of the central challenges of systems biology, which is crucial for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but identifying redundant regulation remains a fundamental problem. Although considering topological properties and edge importance measures simultaneously can identify and reduce redundant regulations, how to address their respective weaknesses whilst leveraging their strengths is a critical problem faced by researchers. Here, we propose a network structure refinement method for GRN (NSRGRN) that effectively combines the topological properties and edge importance measures during GRN inference. NSRGRN has two major parts. The first part constructs a preliminary ranking list of gene regulations to avoid starting the GRN inference from a directed complete graph. The second part develops a novel network structure refinement (NSR) algorithm to refine the network structure from local and global topology perspectives. Specifically, the Conditional Mutual Information with Directionality and network motifs are applied to optimise the local topology, and the lower and upper networks are used to balance the bilateral relationship between the local topology's optimisation and the global topology's maintenance. NSRGRN is compared with six state-of-the-art methods on three datasets (26 networks in total), and it shows the best all-round performance. Furthermore, when acting as a post-processing step, the NSR algorithm can improve the results of other methods in most datasets.

There are usually multiple constraints in constrained multiobjective optimization. Those constraints reduce the feasible area of the constrained multiobjective optimization problems (CMOPs) and make it difficult for current multiobjective optimization algorithms (CMOEAs) to obtain satisfactory feasible solutions. In order to solve this problem, this article studies the relationship between constraints, then obtains the priority between constraints according to the relationship between the pareto front (PF) of the single constraint and their common PF. Meanwhile, this article proposes a multistage CMOEA and applies this priority, which can save computing resources while helping the algorithm converge. The proposed algorithm completely abandons the feasibility in the early stage to better explore the objective space, and obtains the priority of constraints according to the relationship. Then, the algorithm evaluates a single constraint in the medium stage to further explore the objective space according to this priority, and abandons the evaluation of some less important constraints according to the relationship to save the evaluation times. At the end stage of the algorithm, the feasibility will be fully considered to improve the quality of the solutions obtained in the first two stages, and finally get the solutions with good convergence, feasibility, and diversity. The results on five CMOP suites and three real-world CMOPs show that the algorithm proposed in this article can have strong competitiveness in existing constrained multiobjective optimization.

To solve the power supply problem of the high-voltage side detection module in HVDC applications, a multi-relay wireless power transfer (WPT) system with an operating frequency of 800 kHz is designed. This paper establishes the model of multi-relay WPT system and derives the expressions of the efficiency and output power. By analyzing the influence of the insulator metal flanges and optimizing the coil spacing, the energy transfer efficiency of the system can be improved. A 1.1m prototype with 8 resonant coils and 2 metal flanges is built and the experimental results verify the influence of metal flanges on the multi-relay WPT system and the feasibility of optimized arrangement of coils.