Complexity最新文献

As a common social phenomenon, group imitation behavior holds significant research value in the fields of biological group collaboration and artificial swarm intelligence. This paper constructs a behavior imitation model integrating information dissemination mechanisms based on the theory of multiagent systems. The model aims to reveal the influence mechanism of group dynamic characteristics and information interaction intensity on the consistency of group behavior. The model architecture consists of two parts. The first part is an information dissemination model improved upon the SIR model, which introduces a perception radius to analyze how neighboring interactions affect the information diffusion rate. The second part is a multiagent group aggregation model based on social mechanics, enabling individuals to form groups through parameters like attraction, repulsion, speed, and movement direction. Groups spread aggregation and imitation information through interactions with neighboring individuals. Then, based on the breadth of the information they receive, they imitate exemplary groups through intergroup imitation effects. Through complex system simulations, the experimental results show that the consistency of group imitation behavior is positively correlated with the perception radius of individuals. This research provides a new modeling framework and analytical perspective for understanding the emergence mechanism of swarm intelligence.

The primary objective of this research paper is to conduct a bibliometric analysis of the prevailing research landscape pertaining to agent-based modeling (ABM). This analysis encompasses an examination of key contributors, affiliated academic institutions, influential publications, and prominent journals within the domain. To achieve this, a dataset consisting of 11,477 scholarly papers retrieved from the ISI Web of Science database has been curated, using keywords specifically related to ABM, spanning the period from 1996 to 2024. Employing n-gram analysis techniques on titles, keywords, abstracts, and keyword-plus fields has unearthed a multitude of domains wherein ABM has been applied with notable success. Our findings, as delineated in this paper, underscore a sustained and robust growth in scholarly interest in the realm of ABM during the specified temporal span, characterized by an impressive annual growth rate of 25.29%. Furthermore, our study contributes to the identification and analysis of salient keywords and emerging trends, thereby elucidating key research trajectories within this domain. The identification of collaborative networks among authors, their respective academic affiliations, and the geographical distribution across various countries and territories offers valuable insights into the global proliferation of ABM as a research methodology. The findings offer valuable insights into the widespread applications of ABM across various domains, including climate change, social networks, supply chain dynamics, public health studies, financial market analysis, and population dynamics. The results of the study can help in guiding future research and practical applications of ABM in these and other multifaceted areas.

Higher-order networks (HON) provide a suitable frame to model connections that involve groups of nodes—representing interacting individuals or other types of agents—of different sizes. They allow us to take into account not only pairwise interactions but also connections binding three or four or any other natural number of nodes together. Motivated by the consideration that the existence of higher-order interactions may impact, among others, the process of diffusion of new products, the spreading of ideas, and the adoption of practices, we propose and study here a version of the celebrated Bass model on top of HON. We define a mean-field equation that contains terms up to the order at which interactions might make a significant contribution. The impact of the paper is twofold. By considering and comparing different maximal orders of interaction and analyzing how they influence certain times that are important in the diffusion process, we show that HON indeed has an impact and yields a greater accuracy in modeling results. The second contribution of the paper, also of interest for future works, consists of a novel procedure we develop for the construction of HON with assigned generalized mean degrees. We also show that the behavior of the take-off time with the size of the orders contribution undergoes a phase transition where the link density of the network and the related higher-order structures act as the characterizing condition for one phase or the other.

This paper investigates the input delay analysis and H_∞ control problem for networked control systems with finite-time stochastic boundedness (FTSB). First, a novel control scheme is used to handle the Markovian jump parameters, and an event-triggered rule is introduced to a networked control system with FTSB, which can ensure the control performance of the system and effectively improve the resource utilization of the networked control system. Simultaneously, a more accurate expression for input delay compared to traditional methods is obtained. Then, the sufficient condition for the networked control system to have FTSB is derived. Additionally, an H_∞ state feedback controller for the stochastic networked control system with FTSB performance is obtained. Finally, an illustrative example is provided to verify the effectiveness of the method proposed in this paper, especially the good control effect of the H_∞ state feedback controller.

While deep learning–based layered feature extraction methods have achieved remarkable success, their reliance on large-scale annotated datasets limits their applicability in small-sample scenarios. To address this challenge, a novel feature extraction method has been proposed within the traditional image processing framework. This technique is specifically designed for scenarios with limited training data, aiming to enhance performance and efficiency in such conditions. Inspired by image separation algorithms and multifeature fusion strategies, the proposed approach employs guided filtering combined with the Sobel gradient operator to decompose the original finger vein image into a foreground layer and a background layer. Texture features are extracted from the foreground layer, while structural features are derived from the background layer, resulting in two complementary feature maps that capture multidimensional information. These maps are then encoded into a unified one-dimensional feature vector using block-wise histogram descriptors, which enhances feature representation and ensures translation invariance. By separately extracting and effectively fusing multilevel features, the method significantly alleviates the impact of noise on feature extraction and discriminative performance. Without relying on large-scale data, it improves the robustness and practicality of finger vein recognition. Extensive experiments on public datasets validate the effectiveness and generalization capability of the proposed approach.

RETRACTION: Y. Xu and X. Liu, “Interactive Algorithms in Complex Image Processing Systems Based on Big Data,” Complexity 2020 (2020): 5929584, https://doi.org/10.1155/2020/5929584.

The above article, published online on 05 May 2020 in Wiley Online Library (https://wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal’s Chief Editor, Hiroki Sayama; and John Wiley & Sons Ltd.

The retraction has been agreed due to the authors finding that the content of the article is considered unreliable.

Real-parameter single-objective optimization has become a prominent focus within artificial intelligence in recent years. Among population-based metaheuristics, differential evolution (DE) and covariance matrix adaptation evolution strategy (CMA-ES) have consistently demonstrated strong performance. However, the difficulty of solving optimization problems increases exponentially with the dimensionality of the objective function, resulting in a corresponding rise in the number of required function evaluations. To address this challenge, a novel algorithm—the Gaining-Sharing Knowledge (GSK)–based algorithm—has emerged as a promising solution. GSK’s development trajectory currently resembles the early stages of DE. Nevertheless, further enhancements are necessary to unlock its full potential. In this paper, we propose an evolutionary external archive (EEA) for GSK and its variants, inspired by the external archive mechanism used in DE. The proposed EEA integrates individuals from both the current population and the archive into the evolutionary process. To promote diversity, we apply an evolutionary procedure based on CMA-ES within the archive and exclude individuals from the archive if identical counterparts exist in the current generation. We evaluate our approach using three benchmark test suites from the Congress on Evolutionary Computation (CEC) and real-world optimization problems from CEC 2011. Our experimental analysis compares GSK and its variants with and without the EEA. Results show that the EEA significantly improves the performance of GSK and its variants. Consequently, the GSK variant, AGSK, with the EEA is selected for further comparison against benchmark algorithms. Experimental results confirm that our proposed method is highly competitive.

RETRACTION: C. Jia, J. Ma, Q. Liu, Y. Zhang, and H. Han, “Linkboost: A Link Prediction Algorithm to Solve the Problem of Network Vulnerability in Cases Involving Incomplete Information,” Complexity (2020): 7348281, https://doi.org/10.1155/2020/7348281.

The above article, published online on 08 April 2020 in Wiley Online Library (https://wileyonlinelibrary.com), has been retracted by agreement between the authors; the journal Editor-in-Chief, Dr. Gonzalo Farias; and John Wiley & Sons Ltd.

The retraction has been agreed due to errors noted by the authors in the network attack experiments performed. Specifically, the proportion of attacked/removed nodes was miscalculated, leading to errors in the results and conclusions presented in the article.

The authors apologise and agree to the retraction.

Organizational systems are inherently complex, with decision-making processes influenced by interactions between individual perceptions, social norms, and systemic structures. In project management, unconscious gender biases represent a hidden layer of complexity, subtly shaping evaluations of competences and leadership potential. This study explores how unconscious gender biases emerge as part of the complex dynamics within organizational decision-making systems. It investigates the interplay between individual cognitive biases and systemic factors in defining what constitutes a “good project manager” and how these biases influence hiring and promotion decisions. Using a sample of project management professionals, we applied noise-based reverse correlation (NBRC) to reveal participants’ unconscious mental representations of an ideal project manager by generating faces that best represented project managers. The study then compared these representations with conscious competence evaluations based on the International Project Management Association (IPMA) Competence Baseline, incorporating statistical methods to identify patterns of bias and preference. The findings reveal that unconscious gender biases align with entrenched stereotypes, favoring traits associated with masculinity in leadership roles. However, when consciously evaluating specific competences, participants displayed preferences that challenged these biases, suggesting a misaligned relationship between unconscious perceptions and explicit decisions. Unconscious gender bias operates as a hidden variable within the complex system of organizational decision-making, creating feedback loops that reinforce traditional stereotypes. Understanding these dynamics requires a system-level approach that integrates cognitive and organizational perspectives. Our findings highlight the need for interventions that address both individual biases and structural factors to foster equitable decision-making in complex organizational environments.