Wei-neng Chen

• PhD
• Professor at South China University of Technology

Infrared-based detection of small targets on ships is crucial for ensuring navigation safety and effective maritime traffic management. However, existing ship target detection models often encounter missed detections and struggle to achieve both high accuracy and real-time performance at the same time. Addressing these challenges, this study presen...

In recent years, black-box distributed optimization (DBO) has been widely studied to solve complex optimization problems in multi-agent systems, such as hyperparameter optimization of distributed machine learning. However, most existing methods use a fixed or diminishing step size to sample and search in the black box optimization space, which make...

The influence maximization (IM) problem in large-scale social networks has attracted great attention. Considering the interactions among multiple online social platforms, the multilayer IMproblem poses further challenges (i.e., high-simulation burden and low-optimization quality). To solve these problems, this article proposes a susceptible-exposed...

The widespread adoption of online social networks (OSNs) has facilitated social interaction and knowledge dissemination while raising concerns about extensive negative information propagation. Competitive propagation of positive and negative information can mitigate negative impacts. Influence blocking maximization (IBM) identifies a set of nodes i...

Crowd management plays a crucial role in improving travel efficiency and reducing potential risks caused by overcrowding in large public places. Crowd control at entrances is a common way in our daily life to avoid overcrowding, but nowadays the control of crowd inflow at the entrances of public places mainly relies on manual operation. In this art...

Multipoint dynamic aggregation (MPDA) is a multirobot task allocation problem, which requires the collaborative scheduling of multiple robots to complete time-varying tasks distributed on a map. Most existing studies consider the scenarios with homogeneous robots and tasks. To model the application scenarios where different types of robots are requ...

The development of multiagent systems (MASs) has given rise to a new type of traveling salesman problem (TSP), namely the multiagent TSP (MATSP). MATSP aims to find multiple routes with the minimum total cost through the cooperation of intelligent agents. Since the distributed nature of MATSP, it is challenging to solve MATSP effectively in a distr...

In expensive multi/many-objective optimization problems (EMOPs), the expensive objectives are generally accessed through different simulation tools, leading to different evaluation latencies and unbearable computational time for serial optimization. One promising approach to improve efficiency is to perform simulation and build surrogates separatel...

Crowdsourcing utilizes the crowd intelligence for pervasive data sensing and processing. When the processing task is a decision-making and optimization problem, the objective is evaluated based on sensed data, which is defined as crowdsourcing-based distributed optimization (CrowdDO). As evolutionary computation (EC) is a powerful technique for bla...

Many real-world combinatorial optimization problems are defined on networks, such as road networks and social networks, etc. Due to the connectivity nature of networks, decision variables in such problems are usually coupled with each other, and the variables are also closely related to the characteristics of the local subnetwork to which they belo...

The emergence of networked systems in various fields brings many complex distributed optimization problems, where multiple agents in the system need to optimize a global objective cooperatively when they only have local information. In this work, we take advantage of the intrinsic parallelism of evolutionary computation to address network-based dis...

Pandemic propagation, a highly nonlinear and complicated process, is difficult to understand, predict, and prevent in reality. The explosive growth of mass data and intelligent technologies poses new insights for solving this challenge. From a systematic perspective, this article proposes an organizational framework for pandemic crisis control. As...

Modeling crowds has many important applications in games and computer animation. Inspired by the emergent following effect in real-life crowd scenarios, in this work, we develop a method for implicitly grouping moving agents. We achieve this by analyzing local information around each agent and rotating its preferred velocity accordingly. Each agent...

The formation of information cocoons, driven by limited disclosure and individual preferences, has resulted in the polarization of society. However, the underlying mechanisms and pathways to escape these cocoons remain unresolved. This article aims to solve it by developing an adaptive imitation process. In this process, the measurement of informat...

EA, such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement evolutionary algorithms (EAs) for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, how...

Surrogate-assisted evolutionary algorithms (SAEAs) have achieved effective performance in solving complex data-driven optimization problems. In the Internet of Things environment, the data of many problems are collected and processed in distributed network nodes and cannot be transmitted. As each local node can only access and build surrogate model...

Distributed optimization has attracted lots of attention in recent years. Thanks to the intrinsic parallelism and great search capacity, evolutionary computation (EC) has the potential for black-box and non-convex distributed optimization. However, due to the decentralization of local objective functions, it is challenging to optimize the global ob...

Genetic programming hyper-heuristic (GPHH) is recently a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lacks a global systemic view sometimes may be problematic...

Constraint handling is a core part when using surrogate-assisted evolutionary algorithms (SAEAs) to solve expensive constrained optimization problems (ECOPs). However, most existing SAEAs for ECOPs train a surrogate for each constraint. With the number of constraints increasing, the training burden of surrogates becomes heavy and the efficiency of...

Estimating the time of arrival is a crucial task in intelligent transportation systems. The task poses challenges due to the dynamic nature and complex spatio-temporal dependencies of traffic networks. Existing studies have primarily focused on learning the dependencies between adjacent links on a route, often overlooking a deeper understanding of...

The ceramic industry is a representative traditional industry in Guangdong Province, where its degree of informatization is low, and the design of ceramic formula mainly depends on human experience. To intelligently generate ceramic formulas, two main challenges are raised, i.e., the evaluation of a ceramic formula by actual firing is expensive, an...

The increasing population density in public places necessitates urgent attention to address safety concerns via effective crowd management. In many congested scenarios such as peak-hour subway stations, the utilization of fences to guide crowd movement has become a widely adopted approach to alleviate congestion. This work presents a method that co...

Surrogate-assisted evolutionary algorithms (SAEAs) have become a popular tool to solve expensive optimization problems and have been gradually used to deal with expensive constraints. To handle inequality expensive constraints, existing SAEAs need both the information of constraint violation and satisfaction to construct surrogate models for constr...

Epidemics like COVID-19 seriously threaten public health. How to control the spread of epidemics has long been an important topic that attracts a large amount of research effort. During the epidemic prevention, it is crucial to effectively reduce the number of infected people, and it is also important to make good use of epidemic prevention resourc...

Binary hashing is an effective approach for content-based image retrieval, and learning binary codes with neural networks has attracted increasing attention in recent years. However, the training of hashing neural networks is difficult due to the binary constraint on hash codes. In addition, neural networks are easily affected by input data with sm...

With the continuous development of urbanization, traffic congestion has become a key problem that plagues many large cities around the world. As new information technologies like the Internet of Things and the mobile Internet develop, the interconnection between vehicles and road facilities provides a new mechanism to improve transportation efficie...

The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and distributed computing systems. On the one hand, the computing power provided by parallel computing systems can sign...

Crowdsourcing is an emerging computing paradigm that takes advantage of the intelligence of a crowd to solve complex problems effectively. Besides collecting and processing data, it is also a great demand for the crowd to conduct optimization. Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to prop...

With the development of Internet of things and distributed computing techniques, distributed and expensive constrained optimization problems (DECOPs) have emerged in the industry. DECOPs refer to optimization problems with objective and constraint functions that are computationally expensive and can only be evaluated on multiple agents of distribut...

Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like distributed EC and surrogate-assisted EC. These advances have significantly improved the performance and the application scope of EC, but also trigger privacy leakages, such...

Collective decision-making problems consisting of individual decisions are commonly seen in social applications. In this article, the vehicle energy station distribution problem (VESDP) is considered, which is modeled as a network-based collective decision-making problem fulfilling consumers’ requirements by arranging the distribution of energy sta...

Traffic assignment problem (TAP) is of great significance for promoting the development of smart city and society. It usually focuses on the deterministic or predictable traffic demand and the vehicle traffic assignment. However, in the real world, traffic demand is usually unpredictable, especially the foot traffic assignment inside buildings such...

In high dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this paper designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-P...

Traffic assignment is of great importance in real life from foot traffic assignment of a building to vehicle traffic assignment of a city. With the rapid increase of the number of agents and the size of the traffic network, the problem becomes more and more challenging nowadays. To solve large-scale efficient dynamic traffic assignment, this articl...

Emerging infectious diseases pose a growing threat to human society and have sparked extensive public discussions on social media. Although numerous efforts have been made in health data mining on social media, there is a lack of focus on quantitative comparisons across multiple platforms, despite their crucial role in the holistic social communica...

Density clustering has shown advantages over other types of clustering methods for processing arbitrarily shaped datasets. In recent years, extensive research efforts has been made on the improvements of DBSCAN or the algorithms incorporating the concept of density peaks. However, these previous studies remain the problems of being sensitive to the...

The rapid development of online social networks (OSNs) has facilitated people to express opinions and share information. To optimize the utility of information dissemination in OSNs, problems such as influence maximization have received increasing attention in recent years. However, not only positive information but also negative information is spr...

Document clustering has long been an important research direction in intelligent system. When being applied to process Chinese documents, new challenges were posted since it is infeasible to directly split the Chinese documents using the whitespace character. Moreover, many Chinese document clustering algorithms require prior knowledge of the clust...

Centralized particle swarm optimization (PSO) does not fully exploit the potential of distributed or parallel computing and suffers from single-point-of-failure. Particularly, each particle in PSO comprises a potential solution (e.g., traveling route and neural network model parameters) which is essentially viewed as private data. Unfortunately, pr...

Surrogate-assisted evolutionary algorithms (EAs) have been proposed in recent years to solve data-driven optimization problems. Most existing surrogate-assisted EAs are for centralized optimization and do not take into account the challenges brought by the distribution of data at the edge of networks in the era of the Internet of Things. To this en...

Mechanism-driven models based on transmission dynamics and statistic models driven by public health data are two main methods for simulating and predicting emerging infectious diseases. In this paper, we intend to combine these two methods to develop a more comprehensive model for the simulation and prediction of emerging infectious diseases. First...

Location selection is an important part of running a business. A good business location can greatly increase customer flow, reduce operating costs, and increase business revenue. Therefore, it is necessary to select an appropriate location for business premises. In this research, we propose a method combining machine learning and particle swarm opt...

Vaccine hesitancy plays a key role in vaccine delay and refusal, but its measurement is still a challenge due to multiple intricacies and uncertainties in factors. This paper attempts to tackle this problem through fuzzy cognitive inference techniques. Firstly, we formulate a vaccine hesitancy determinants matrix containing multi-level factors. Rel...

COVID-19 crisis has been accompanied by copious hate speeches widespread on social media. It reinforces the fragmentation of the world, resulting in more significant racial discrimination and distrust between people, leading to crimes, and injuring individuals spiritually or physically. Hate speech is hard to crack for a global recovery in the post...

Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough capability to implement EAs to solve COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud ser...

As an emerging sensing data collection paradigm, mobile crowdsensing (MCS) enjoys good scalability and low deployment cost but raises privacy concerns. In this paper, we propose a privacy-preserving MCS system called \textsc{CrowdFL} by seamlessly integrating federated learning (FL) into MCS. At a high level, in order to protect participants' priva...

One important problem in financial optimization is to search for robust investment plans that can maximize return while minimizing risk. The market environment, namely the scenario of the problem in optimization, always affects the return and risk of an investment plan. Those financial optimization problems that the performance of the investment pl...

Expensive optimization problems (EOPs) are common in industry and surrogate-assisted evolutionary algorithms (SAEAs) have been developed for solving them. However, many EOPs have not only expensive objective, but also expensive constraints, which are evaluated through distributed ways. We define this kind of EOPs as distributed expensive constraine...

Symbolic regression is an important method of data-driven modeling, which can provide explicit mathematical expressions for data analysis. However, the existing genetic programming algorithms for symbolic regression require centralized storage of all data, which is unrealistic in many practical applications that involve data privacy. If the data co...

Crowd navigation path planning is important in public scenes. Existing strategies are mainly based on manual design, which is not flexible or effective enough. This article proposes an evolutionary framework for automatic crowd navigation path planning in public scenes. The proposed framework contains a new fitness evaluation mechanism that can qua...

Multimodal optimization, which aims to discover multiple satisfactory solutions simultaneously, has attracted increasing attention from researchers in the evolutionary computation community. With the aid of niching methods, evolutionary algorithms could simultaneously locate multiple satisfactory solutions in a single run. Although many multimodal...

With the rapid development of the insurance industry, more diverse insurance products are produced for consumers. Insurance portfolio problems have received increasing attention. While most studies focus on insurance portfolio problem for a single insured, insurance portfolio problems for a specific group of insured are even more intricate but litt...

Query weight optimization, which aims to find an optimal combination of the weights of query terms for sorting relevant documents, is an important topic in the information retrieval system. Due to the huge search space, the query optimization problem is intractable, and evolutionary algorithms have become one popular approach. But as the size of th...

Social propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, social innovation, viral marketing, etc. Simulation and optimization are two major themes in social propagation, where network-based simul...

Crowdsensing is regarded as a critical component of the Internet of Things (IoT) and has been widely applied in smart city services. Incentive mechanism design, data reliability evaluation, and privacy preservation are the research focuses of crowdsensing. However, most existing incentive mechanisms fail to protect data privacy and evaluate data cr...

Abstract Traffic Signal Control (TSC) is a fundamental task in modern intelligent transport systems. TSC is often formulated as a bi‐level optimization problem, comprised by the signal timing at the upper level and the Dynamic User Equilibrium (DUE) traffic assignment at the lower level. Since DUE is non‐convex, existing methods either formulate ap...

Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with scalability, low deployment cost, and distributed characteristics. Traditional MCS systems suffer from privacy concerns and fair reward distribution. Moreover, existing privacy-preserving MCS solutions usually focus on the privacy protection of data collection rather than...

In multi-view subspace clustering, the low-rankness of the stacked self-representation tensor is widely accepted to capture the high-order cross-view correlation. However, using the nuclear norm as a convex surrogate of the rank function, the self-representation tensor exhibits strong connectivity with dense coefficients. When noise exists in the d...

High-dimensional problems are ubiquitous in many fields, yet still remain challenging to be solved. To tackle such problems with high effectiveness and efficiency, this article proposes a simple yet efficient stochastic dominant learning swarm optimizer. Particularly, this optimizer not only compromises swarm diversity and convergence speed properl...

Cloud computing is a powerful and scalable computing platform that enables the virtualization, share and on-demand use of computing resources. Scientific workflows on clouds are promising for handling computational-intensive and complex scientific computing tasks. The scientific workflow scheduling problem has been regarded as an intractable optimi...