SARS-CoV-2 spike S1 protein induces microglial NLRP3-dependent neuroinflammation and cognitive impairment in mice.

Cognitive impairment is often found at the acute stages and sequelae of coronavirus disease 2019 (COVID-19), and the underlying mechanisms remain unclear. The S1 protein from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) might be a cause of cognitive impairment associated with COVID-19. The nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3 (NLRP3) inflammasome and neuroinflammation play important roles in Alzheimer's disease (AD) with cognitive impairment. However, their roles remain unknown in COVID-19 with cognitive impairment. We stimulated BV2 cells with S1 protein in vitro and injected the hippocampi of wild-type (WT) mice, NLRP3 knockout (KO), and microglia NLRP3 KO mice in vivo with S1 protein to induce cognitive impairment. We assessed exploratory behavior as associative memory using novel object recognition and Morris water maze tests. Neuroinflammation was analyzed using immunofluorescence and western blotting to detect inflammatory markers. Co-localized NLRP3 and S1 proteins were investigated using confocal microscopy. We found that S1 protein injection led to cognitive impairment, neuronal loss, and neuroinflammation by activating NLRP3 inflammation, and this was reduced by global NLRP3 KO and microglia NLRP3 KO. Furthermore, TAK 242, a specific inhibitor of Toll-like receptor-4, resulted in a significant reduction in NLRP3 and pro-IL-1ß in BV2 cells with S1 protein stimulation. These results reveal a distinct mechanism through which the SARS-CoV-2 spike S1 protein promotes NLRP3 inflammasome activation and induces excessive inflammatory responses.

The MemTrax memory test for detecting and assessing cognitive impairment in Parkinson's disease.

INTRODUCTION: A valid, reliable, accessible measurement for the early detection of cognitive decline in patients with Parkinson's disease (PD) is in urgent demand. The objective of the study is to assess the clinical utility of the MemTrax Memory Test in detecting cognitive impairment in patients with PD. METHODS: The MemTrax, a fast on-line cognitive screening tool based on continuous recognition task, and Montreal Cognitive Assessment (MoCA) were administered to 61 healthy controls (HC), 102 PD patients with normal cognition (PD-N), 74 PD patients with mild cognitive impairment (PD-MCI) and 52 PD patients with dementia (PD-D). The total percent correct (MTx- %C), average response time (MTx-RT), composite score (MTx-Cp) of MemTrax and the MoCA scores were comparatively analyzed. RESULTS: The MoCA scores were similar between HC and PD-N, however, MTx- %C and MTx-Cp were lower in PD-N than HC(p < 0.05). MTx- %C, MTx-Cp and the MoCA scores were significantly lower in PD-MCI versus PD-N and in PD-D versus PD-MCI (p ≤ 0.001), while MTx-RT was statistically longer in PD-D versus PD-MCI (p ≤ 0.001). For PD groups, the MemTrax performance correlated with the MoCA scores. To detect PD-MCI, the optimal MTx- %C and MTx-Cp cutoff were 75 % and 50.0, respectively. To detect PD-D, the optimal MTx- %C, MTx-RT and MTx-Cp cutoff were 69 %, 1.341s and 40.6, respectively. CONCLUSION: The MemTrax provides rapid, valid and reliable metrics for assessing cognition in PD patients which could be useful for identifying PD-MCI at early stage and monitoring cognitive function decline during the progression of disease.

PEGA: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization.

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 lack the capability to implement EAs for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solution.

MSC-Derived Extracellular Vesicles Alleviate NLRP3/GSDMD-Mediated Neuroinflammation in Mouse Model of Sporadic Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease, with sporadic form being the predominant type. Neuroinflammation plays a critical role in accelerating pathogenic processes in AD. Mesenchymal stem cell (MSC)-derived small extracellular vesicles (MSC-sEVs) regulate inflammatory responses and show great promise for treating AD. Induced pluripotent stem cell (iPSC)-derived MSCs are similar to MSCs and exhibit low immunogenicity and heterogeneity, making them promising cell sources for clinical applications. This study examined the anti-inflammatory effects of MSC-sEVs in a streptozotocin-induced sporadic mouse model of AD (sAD). The intracisternal administration of iPSC-MSC-sEVs alleviated NLRP3/GSDMD-mediated neuroinflammation, decreased amyloid deposition and neuronal apoptosis, and mitigated cognitive dysfunction. Furthermore, it explored the role of miR-223-3p in the iPSC-MSC-sEVs-mediated anti-inflammatory effects in vitro. miR-223-3p directly targeted NLRP3, whereas inhibiting miR-223-3p almost completely reversed the suppression of NLRP3 by MSC-sEVs, suggesting that miR-223-3p may, at least partially, account for MSC-sEVs-mediated anti-inflammation. Results obtained suggest that intracisternal administration of iPSC-MSC-sEVs can reduce cognitive impairment by inhibiting NLRP3/GSDMD neuroinflammation in a sAD mouse model. Therefore, the present study provides a proof-of-principle for applying iPSC-MSC-sEVs to target neuroinflammation in sAD.

Phosphorylation of AQP4 by LRRK2 R1441G impairs glymphatic clearance of IFNγ and aggravates dopaminergic neurodegeneration.

Aquaporin-4 (AQP4) is essential for normal functioning of the brain's glymphatic system. Impaired glymphatic function is associated with neuroinflammation. Recent clinical evidence suggests the involvement of glymphatic dysfunction in LRRK2-associated Parkinson's disease (PD); however, the precise mechanism remains unclear. The pro-inflammatory cytokine interferon (IFN) γ interacts with LRRK2 to induce neuroinflammation. Therefore, we examined the AQP4-dependent glymphatic system's role in IFNγ-mediated neuroinflammation in LRRK2-associated PD. We found that LRRK2 interacts with and phosphorylates AQP4 in vitro and in vivo. AQP4 phosphorylation by LRRK2 R1441G induced AQP4 depolarization and disrupted glymphatic IFNγ clearance. Exogeneous IFNγ significantly increased astrocyte expression of IFNγ receptor, amplified AQP4 depolarization, and exacerbated neuroinflammation in R1441G transgenic mice. Conversely, inhibiting LRRK2 restored AQP4 polarity, improved glymphatic function, and reduced IFNγ-mediated neuroinflammation and dopaminergic neurodegeneration. Our findings establish a link between LRRK2-mediated AQP4 phosphorylation and IFNγ-mediated neuroinflammation in LRRK2-associated PD, guiding the development of LRRK2 targeting therapy.

Early Diagnosis of Mild Cognitive Impairment due to Alzheimer's Disease Using a Composite of MemTrax and Blood Biomarkers.

BACKGROUND: Accessible measurements for the early detection of mild cognitive impairment (MCI) due to Alzheimer's disease (AD) are urgently needed to address the increasing prevalence of AD. OBJECTIVE: To determine the benefits of a composite MemTrax Memory Test and AD-related blood biomarker assessment for the early detection of MCI-AD in non-specialty clinics. METHODS: The MemTrax Memory Test and Montreal Cognitive Assessment were administered to 99 healthy seniors with normal cognitive function and 101 patients with MCI-AD; clinical manifestation and peripheral blood samples were collected. We evaluated correlations between the MemTrax Memory Test and blood biomarkers using Spearman's rank correlation analyses and then built discrimination models using various machine learning approaches that combined the MemTrax Memory Test and blood biomarker results. The models' performances were assessed according to the areas under the receiver operating characteristic curve. RESULTS: The MemTrax Memory Test and Montreal Cognitive Assessment areas under the curve for differentiating patients with MCI-AD from the healthy controls were similar. The MemTrax Memory Test strongly correlated with phosphorylated tau 181 and amyloid-ß42/40. The area under the curve for the best composite MemTrax Memory Test and blood biomarker model was 0.975 (95% confidence interval: 0.950-0.999). CONCLUSION: Combining MemTrax Memory Test and blood biomarker results is a promising new technique for the early detection of MCI-AD.

SBHA: Sensitive Binary Hashing Autoencoder for Image Retrieval.

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 small perturbations. Therefore, a sensitive binary hashing autoencoder (SBHA) is proposed to handle these challenges by introducing stochastic sensitivity for image retrieval. SBHA extracts meaningful features from original inputs and maps them onto a binary space to obtain binary hash codes directly. Different from ordinary autoencoders, SBHA is trained by minimizing the reconstruction error, the stochastic sensitive error, and the binary constraint error simultaneously. SBHA reduces output sensitivity to unseen samples with small perturbations from training samples by minimizing the stochastic sensitive error, which helps to learn more robust features. Moreover, SBHA is trained with a binary constraint and outputs binary codes directly. To tackle the difficulty of optimization with the binary constraint, we train the SBHA with alternating optimization. Experimental results on three benchmark datasets show that SBHA is competitive and significantly outperforms state-of-the-art methods for binary hashing.

PriMPSO: A Privacy-Preserving Multiagent Particle Swarm Optimization Algorithm.

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, previously neither centralized nor distributed PSO algorithms fail to protect privacy effectively. Inspired by secure multiparty computation and multiagent system, this article proposes a privacy-preserving multiagent PSO algorithm (called PriMPSO) to protect each particle's data and enable private data sharing in a privacy-preserving manner. The goal of PriMPSO is to protect each particle's data in a distributed computing paradigm via existing PSO algorithms with competitive performance. Specifically, each particle is executed by an independent agent with its own data, and all agents jointly perform global optimization without sacrificing any particle's data. Thorough investigations show that selecting an exemplar from all particles and updating particles through the exemplar are critical operations for PSO algorithms. To this end, this article designs a privacy-preserving exemplar selection algorithm and a privacy-preserving triple computation protocol to select exemplars and update particles, respectively. Strict privacy analyses and extensive experiments on a benchmark and a realistic task confirm that PriMPSO not only protects particles' privacy but also has uniform convergence performance with the existing PSO algorithm in approximating an optimal solution.

A swarm-optimizer-assisted simulation and prediction model for emerging infectious diseases based on SEIR.

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, we combine a standard epidemic dynamic, the susceptible-exposed-infected-recovered (SEIR) model with population migration. This model can provide a biological spread process for emerging infectious diseases. Second, to determine suitable parameters for the model, we propose a data-driven approach, in which the public health data and population migration data are assembled. Moreover, an objective function is defined to minimize the error based on these data. Third, based on the proposed model, we further develop a swarm-optimizer-assisted simulation and prediction method, which contains two modules. In the first module, we use a level-based learning swarm optimizer to optimize the parameters required in the epidemic mechanism. In the second module, the optimized parameters are used to predicate the spread of emerging infectious diseases. Finally, various experiments are conducted to validate the effectiveness of the proposed model and method.

Edge-Cloud Co-Evolutionary Algorithms for Distributed Data-Driven Optimization Problems.

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 end, we propose edge-cloud co-EAs (ECCoEAs) to solve distributed data-driven optimization problems, where data are collected by edge servers. Specifically, we first propose a distributed framework of ECCoEAs, which consists of a communication mechanism, edge model management, and cloud model management. This communication mechanism is to avoid deadlock during the collaboration of edge servers and the cloud server. In edge model management, the edge models are trained based on local historical data and data composed of new solutions generated by co-evolutionary and their real evaluation values. In cloud model management, the black-box prediction functions received from edge models are used to find promising solutions to guide the edge model management. Moreover, two ECCoEAs are implemented, which proves the generality of the framework. To verify the performance of algorithms for distributed data-driven optimization problems, we design a novel benchmark test suite. The performance on the benchmarks and practical distributed clustering problems shows the effectiveness of ECCoEAs.

Analysing the influencing factors on caregivers' burden among amyotrophic lateral sclerosis patients in China: a cross-sectional study based on data mining.

OBJECTIVES: There is significant burden on caregivers of patients with amyotrophic lateral sclerosis (ALS). However, only a few studies have focused on caregivers, and traditional research methods have obvious shortcomings in dealing with multiple influencing factors. This study was designed to explore influencing factors on caregiver burden among ALS patients and their caregivers from a new perspective. DESIGN: Cross-sectional study. SETTING: The data were collected at an affiliated hospital in Guangzhou, Guangdong, China. PARTICIPANTS: Fifty-seven pairs of patients with ALS and their caregivers were investigated by standardised questionnaires. MAIN OUTCOME MEASURES: This study primarily assessed the influencing factor of caregiver burden including age, gender, education level, economic status, anxiety, depression, social support, fatigue, sleep quality and stage of disease through data mining. Statistical analysis was performed using SPSS 24.0, and least absolute shrinkage and selection operator (LASSO) regression model was established by Python 3.8.1 to minimise the effect of multicollinearity. RESULTS: According to LASSO regression model, we found 10 variables had weights. Among them, Milano-Torinos (MITOS) stage (0-1) had the highest weight (-12.235), followed by younger age group (-3.198), lower-educated group (2.136), fatigue (1.687) and social support (-0.455). Variables including sleep quality, anxiety, depression and sex (male) had moderate weights in this model. Economic status (common), economic status (better), household (city), household (village), educational level (high), sex (female), age (older) and MITOS stage (2-4) had a weight of zero. CONCLUSIONS: Our study demonstrates that the severity of ALS patients is the most influencing factor in caregiver burden. Caregivers of ALS patients may suffer less from caregiver burden when the patients are less severe, and the caregivers are younger. Low educational status could increase caregiver burden. Caregiver burden is positively correlated with the degree of fatigue and negatively correlated with social support. Hopefully, more attention should be paid to caregivers of ALS, and effective interventions can be developed to relieve this burden.

An estimation of distribution algorithm with clustering for scenario-based robust financial optimization.

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 plans largely depends on the scenarios are defined as scenario-based optimization problems. This kind of uncertainty is called scenario-based uncertainty. The consideration of scenario-based uncertainty in multi-objective optimization problem is a largely under explored domain. In this paper, a nondominated sorting estimation of distribution algorithm with clustering (NSEDA-C) is proposed to deal with scenario-based robust financial problems. A robust group insurance portfolio problem is taken as an instance to study the features of scenario-based robust financial problems. A simplified simulation method is applied to measure the return while an estimation model is devised to measure the risk. Applications of the NSEDA-C on the group insurance portfolio problem for real-world insurance products have validated the effectiveness of the proposed algorithm.

Natural history and remarkable psychiatric state of late-onset amyotrophic lateral sclerosis in China.

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motor neurons. The proportion of late-onset ALS in China were low and may have distinct clinical and genetic manifestations. We aimed to investigate the natural history and remarkable psychiatric state of ALS with age at onset over 60 years in China. MATERIALS AND METHODS: We collected all ALS cases from 2017 to 2020 in our center and focused on late-onset ALS patients particularly, by analyzing the clinical data, including the ALS onset and disease progression. Anxiety, depression, cognitive function, and sleep quality were assessed to reflect the psychiatric state. RESULTS: A total of 193 late-onset ALS patients were included in this study. The median age at onset of late-onset ALS was 65 years with the quartile from 62 to 68 years. When compared with 446 non-late-onset ALS, late-onset ALS showed distinct clinical presentation, with lower ALS Functional Rating Scale-Revised at diagnosis and faster rate of progression. Remarkably, late-onset ALS were suffering from worse psychiatric state, including serious anxiety and depression, as well as worse cognitive function with sleep quality. The abnormal psychiatric state was more pronounced in female patients of late-onset. CONCLUSIONS: In the current study, ALS patients with late-onset showed unique clinical features. Severe psychiatric conditions and faster progression in the early stage of the disease of late-onset ALS indicated the need for more social and psychiatric support in this population.

A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space Partitioning.

Multimodal optimization problems have multiple satisfactory solutions to identify. Most of the existing works conduct the search based on the information of the current population, which can be inefficient. This article proposes a probabilistic niching evolutionary computation framework that guides the future search based on more sufficient historical information, in order to locate diverse and high-quality solutions. A binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline niching algorithms. In this article, we integrate the proposed framework with two niching algorithms: 1) a distance-based differential evolution algorithm and 2) a topology-based particle swarm optimization algorithm. The two new algorithms are evaluated on 20 multimodal optimization test functions. The experimental results show that the proposed framework helps the algorithms obtain competitive performance. They outperform a number of state-of-the-art niching algorithms on most of the test functions.

An Adaptive Stochastic Dominant Learning Swarm Optimizer for High-Dimensional Optimization.

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 properly, but also consumes as little computing time and space as possible to locate the optima. In this optimizer, a particle is updated only when its two exemplars randomly selected from the current swarm are its dominators. In this way, each particle has an implicit probability to directly enter the next generation, making it possible to maintain high swarm diversity. Since each updated particle only learns from its dominators, good convergence is likely to be achieved. To alleviate the sensitivity of this optimizer to newly introduced parameters, an adaptive parameter adjustment strategy is further designed based on the evolutionary information of particles at the individual level. Finally, extensive experiments on two high dimensional benchmark sets substantiate that the devised optimizer achieves competitive or even better performance in terms of solution quality, convergence speed, scalability, and computational cost, compared to several state-of-the-art methods. In particular, experimental results show that the proposed optimizer performs excellently on partially separable problems, especially partially separable multimodal problems, which are very common in real-world applications. In addition, the application to feature selection problems further demonstrates the effectiveness of this optimizer in tackling real-world problems.

Large-Scale Evolution Strategy Based on Search Direction Adaptation.

The covariance matrix adaptation evolution strategy (CMA-ES) is a powerful evolutionary algorithm for single-objective real-valued optimization. However, the time and space complexity may preclude its use in high-dimensional decision space. Recent studies suggest that putting sparse or low-rank constraints on the structure of the covariance matrix can improve the efficiency of CMA-ES in handling large-scale problems. Following this idea, this paper proposes a search direction adaptation evolution strategy (SDA-ES) which achieves linear time and space complexity. SDA-ES models the covariance matrix with an identity matrix and multiple search directions, and uses a heuristic to update the search directions in a way similar to the principal component analysis. We also generalize the traditional 1/5th success rule to adapt the mutation strength which exhibits the derandomization property. Numerical comparisons with nine state-of-the-art algorithms are carried out on 31 test problems. The experimental results have shown that SDA-ES is invariant under search-space rotational transformations, and is scalable with respect to the number of variables. It also achieves competitive performance on generic black-box problems, demonstrating its effectiveness in keeping a good tradeoff between solution quality and computational efficiency.

Elastic Differential Evolution for Automatic Data Clustering.

In many practical applications, it is crucial to perform automatic data clustering without knowing the number of clusters in advance. The evolutionary computation paradigm is good at dealing with this task, but the existing algorithms encounter several deficiencies, such as the encoding redundancy and the cross-dimension learning error. In this article, we propose a novel elastic differential evolution algorithm to solve automatic data clustering. Unlike traditional methods, the proposed algorithm considers each clustering layout as a whole and adapts the cluster number and cluster centroids inherently through the variable-length encoding and the evolution operators. The encoding scheme contains no redundancy. To enable the individuals of different lengths to exchange information properly, we develop a subspace crossover and a two-phase mutation operator. The operators employ the basic method of differential evolution and, in addition, they consider the spatial information of cluster layouts to generate offspring solutions. Particularly, each dimension of the parameter vector interacts with its correlated dimensions, which not only adapts the cluster number but also avoids the cross-dimension learning error. The experimental results show that our algorithm outperforms the state-of-the-art algorithms that it is able to identify the correct number of clusters and obtain a good cluster validation value.

A Preference Biobjective Evolutionary Algorithm for the Payment Scheduling Negotiation Problem.

The resource-constrained project scheduling problem (RCPSP) is a basic problem in project management. The net present value (NPV) of discounted cash flow is used as a criterion to evaluate the financial aspects of RCPSP in many studies. But while most existing studies focused on only the contractor's NPV, this article addresses a practical extension of RCPSP, called the payment scheduling negotiation problem (PSNP), which considers both the interests of the contractor and the client. To maximize NPVs of both sides and achieve a win-win solution, these two participants negotiate together to determine an activity schedule and a payment plan for the project. The challenges arise in three aspects: 1) the client's NPV and the contractor's NPV are two conflicting objectives; 2) both participants have special preferences in decision making; and 3) the RCPSP is nondeterministic polynomial-time hard (NP-Hard). To overcome these challenges, this article proposes a new approach with the following features. First, the problem is reformulated as a biobjective optimization problem with preferences. Second, to address the different preferences of the client and the contractor, a strategy of multilevel region interest is presented. Third, this strategy is integrated in the nondominated sorting genetic algorithm II (NSGA-II) to solve the PSNP efficiently. In the experiment, the proposed algorithm is compared with both the double-level optimization approach and the multiobjective optimization approach. The experimental results validate that the proposed method can focus on searching in the region of interest (ROI) and provide more satisfactory solutions.

Evolutionary Divide-and-Conquer Algorithm for Virus Spreading Control Over Networks.

The control of virus spreading over complex networks with a limited budget has attracted much attention but remains challenging. This article aims at addressing the combinatorial, discrete resource allocation problems (RAPs) in virus spreading control. To meet the challenges of increasing network scales and improve the solving efficiency, an evolutionary divide-and-conquer algorithm is proposed, namely, a coevolutionary algorithm with network-community-based decomposition (NCD-CEA). It is characterized by the community-based dividing technique and cooperative coevolution conquering thought. First, to reduce the time complexity, NCD-CEA divides a network into multiple communities by a modified community detection method such that the most relevant variables in the solution space are clustered together. The problem and the global swarm are subsequently decomposed into subproblems and subswarms with low-dimensional embeddings. Second, to obtain high-quality solutions, an alternative evolutionary approach is designed by promoting the evolution of subswarms and the global swarm, in turn, with subsolutions evaluated by local fitness functions and global solutions evaluated by a global fitness function. Extensive experiments on different networks show that NCD-CEA has a competitive performance in solving RAPs. This article advances toward controlling virus spreading over large-scale networks.

Ant Colony Evacuation Planner: An Ant Colony System With Incremental Flow Assignment for Multipath Crowd Evacuation.

Evacuation path optimization (EPO) is a crucial problem in crowd and disaster management. With the consideration of dynamic evacuee velocity, the EPO problem becomes nondeterministic polynomial-time hard (NP-Hard). Furthermore, since not only one single evacuation path but multiple mutually restricted paths should be found, the crowd evacuation problem becomes even challenging in both solution spatial encoding and optimal solution searching. To address the above challenges, this article puts forward an ant colony evacuation planner (ACEP) with a novel solution construction strategy and an incremental flow assignment (IFA) method. First, different from the traditional ant algorithms, where each ant builds a complete solution independently, ACEP uses the entire colony of ants to simulate the behavior of the crowd during evacuation. In this way, the colony of ants works cooperatively to find a set of evacuation paths simultaneously and thus multiple evacuation paths can be found effectively. Second, in order to reduce the execution time of ACEP, an IFA method is introduced, in which fractions of evacuees are assigned step by step, to imitate the group-based evacuation process in the real world so that the efficiency of ACEP can be further improved. Numerical experiments are conducted on a set of networks with different sizes. The experimental results demonstrate that ACEP is promising.