Evolutionary Multi-Objective Optimization Algorithms (EMOAs) are extensively utilised to address issues involving conflicting objectives. Recent studies suggest that decision-makers (DMs) are typically only concerned with a portion of the Pareto frontier. The Interactive Evolutionary Multi-Objective Optimization Algorithm (iEMOA) has been developed to address this issue by integrating the preference of DM into the optimization process through multiple interactions. However, extant approaches often treat these preferences as static, neglecting the dynamic nature of DM. This paper proposes a preference-based reinforcement learning (PbRL) method that utilises list-wise preferences to dynamically align shifting preference of DM in a multi-objective optimization problem (MOP). First, a Markov decision process (MDP) model for list-wise preference is developed. Second, modifications are made to the reward and resampling mechanisms in the basic deep deterministic policy gradient (DDPG) algorithm. Third, the machine decision-maker (MDM) model is enhanced to account for preference shift. The efficacy of the proposed approach is demonstrated through comparative experiments with state-of-the-art methods, highlighting its superior performance in handling the complex preference behaviours exhibited by real-world DM.

Aiming at problems such as incomplete, inconsistent, and inaccurate requirements that often occur in the process of obtaining high-end equipment functional requirements, this paper presents a requirement quality improvement method. Referring to the agile development theory of requirements engineering, the quality improvement method constructs a functional requirement model based on user stories, defines the concept of functional requirement quality, designs functional requirement quality evaluation criteria, and constructs a functional requirement quality evaluation process. A case study and sensitivity analysis of new energy vehicle requirement development are conducted to confirm the feasibility and effectiveness of the method, and the experimental results show the superiority of the proposed method in improving the quality of high-end equipment functional requirements.

In this study, we focus on the efficient disintegration method in spatial networks. First, we present an optimization model for the disintegration strategy in the spatial network and introduce a heuristic algorithm to seek the efficient disintegration strategy. Experiment in American fiber network indicates that our method could seek the efficient disintegration strategy with the tabu search. Also, the disintegration effect based on tabu search substantially exceeds the degree-core strategy and betweenness-core strategy.

Finding the connected components of an undirected network is a fundamental computational problem at the heart of many network applications and has been applied in many fields, such as communication network robustness and computer vision. This paper presents a new form of graph contraction referred to as the “hub contraction,” in which the node with the maximum degree is selected as the central node, after which this node and its immediate neighbors are combined to form a new central node. Based on the hub contraction principle, we propose a fast connected component algorithm, which exploits the existence of high-degree nodes to obtain the number and size of connected components. The efficiency of the proposed algorithm was verified by comparing it with other connected component algorithms within random networks and scale-free networks. The results showed the performance of our algorithm to be superior in terms of the processing time it requires, most notably when applied to scale-free networks.

We propose a new consensus model for group decision making (GDM) problems, using an interval type-2 fuzzy environment. In our model, experts are asked to express their preferences using linguistic terms characterized by interval type-2 fuzzy sets (IT2 FSs), because these can provide decision makers with greater freedom to express the vagueness in real-life situations. Consensus and proximity measures based on the arithmetic operations of IT2 FSs are used simultaneously to guide the decision-making process. The majority of previous studies have taken into account only the importance of the experts in the aggregation process, which may give unreasonable results. Thus, we propose a new feedback mechanism that generates different advice strategies for experts according to their levels of importance. In general, experts with a lower level of importance require a larger number of suggestions to change their initial preferences. Finally, we investigate a numerical example and execute comparable models and ours, to demonstrate the performance of our proposed model. The results indicate that the proposed model provides greater insight into the GDM process.

We introduce the concept of natural connectivity as a measure of structural robustness in complex networks. The natural connectivity characterizes the redundancy of alternative routes in a network by quantifying the weighted number of closed walks of all lengths. This definition leads to a simple mathematical formulation that links the natural connectivity to the spectrum of a network. The natural connectivity can be regarded as an average eigenvalue that changes strictly monotonically with the addition or deletion of edges. We calculate both analytically and numerically the natural connectivity of three typical networks: regular ring lattices, random graphs, and random scale-free networks. We also compare the proposed natural connectivity to other structural robustness measures within a scenario of edge elimination and demonstrate that the natural connectivity provides sensitive discrimination of structural robustness that agrees with our intuition.