Traditional artificial neural networks often face stability challenges in noisy Internet of Things (IoT) environments, owing to their dependence on static weights and deterministic computational frameworks. Spiking neural networks (SNNs), which mimic the spike-based coding mechanisms of biological neurons, present a promising alternative for edge devices due to their inherent temporal dynamics and energy efficiency. However, most existing Spiking neural network (SNN) models utilize fixed neuronal parameters, restricting their ability to adapt to diverse noise conditions commonly encountered in IoT applications. To address this issue, we propose a fractional-order plasticity approach integrated with spatio-temporal backpropagation algorithm. It enables simultaneous updating of synaptic weights and fractional-order parameters during training, allowing neurons to adjust their intrinsic dynamics. By incorporating fractional-order calculus, neurons can effectively integrate historical input information, enhancing their ability to filter out noise and extract relevant features effectively. Experimental results demonstrate that our proposed approach significantly improves classification performance, achieving accuracies of 99.67% on MNIST, 94.50% on Fashion-MNIST, and 93.43% on CIFAR-10, outperforming existing leading methods on all evaluated static datasets, and reaching a competitive accuracy of 97.25% on the DVS-Gesture dataset. The framework also exhibits superior resistance to noise compared to weight-training-only methods. This adaptive mechanism empowers neurons across different network layers to develop optimal fractional-order configurations, thereby enhancing networks’ temporal information processing ability and overall robustness in noisy environments. The proposed framework considerably advances the state of the art in efficient and robust spiking neural network learning for IoT applications.

This work proposes a Dimension-Reduced Koopman Model Predictive Control (Dr-KMPC) framework for discrete-time nonlinear systems. The main challenges in existing Koopman-based control approaches arise from the curse of dimensionality associated with dictionary-based lifting and the computational burden of deep learning-based alternatives. Particularly, standard Extended Dynamic Mode Decomposition (EDMD) often requires a high-dimensional lifted space to ensure accuracy, which renders the subsequent controller design computationally expensive. To address these issues, we introduce a trainable linear encoding map to project high-dimensional observables into a lower-dimensional latent space. The resulting non-convex joint parameter learning problem is solved by using a novel Block Coordinate Descent (BCD) algorithm, which decomposes the optimization into sequentially solvable, strictly convex linear least-squares sub-problems. The resulting Dr- KMPC formulation allows standard Quadratic Programming (QP) solvers to determine optimal control inputs efficiently. Rigorous theoretical analysis establishes recursive feasibility and asymptotic stability of the proposed MPC method. Simulation results show that the proposed method can effectively handle nonlinear systems and achieve high control performance.

Internet of Things (IoT) devices are widely distributed with large and scattered data, risk of privacy leakage and high bandwidth cost make data transmission and aggregation impractical. Federated Learning (FL) is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating based on private data. Data heterogeneity is one of the main challenges in FL scenarios, which can greatly limit FLs applicability and performance. Most existing approaches tackle the heterogeneity challenge during local training or model aggregation while ignoring the performance drop caused by direct model aggregation and catastrophic forgetting in the global model. This work proposes a novel FL approach, called Federated Learning with Dataset Condensation (FedDC). It aims to relieve the issue of knowledge discrepancy among local models and catastrophic forgetting in the global model. Specifically, this work proposes a differential distribution matching method to summarize local data for each client without compromising on data privacy. It introduces collaborative knowledge distillation to encourage local models to learn from others. To mitigate interround forgetting, this work stabilizes the averaged global models training by leveraging auxiliary information from its immediately past one. Extensive experiments are conducted. The results show that FedDC significantly outperforms the state-ofthe-art methods on various vision tasks.

Selective maintenance has a significant impact on the sustainable management of maintenance operations. The collaboration of multiple maintenance teams/operators is helpful to achieve sustainability for selective maintenance sequence planning. For products with a large number of components, a single maintenance team/operator is inefficient due to a long completion time which is not acceptable for emergency planning. Providing specific and efficient maintenance sequence planning is critical to effectively handle different types of emergencies (e.g., wartime) while avoiding vague task assignments to multiple maintenance teams/operators. For scheduling many maintenance jobs while improving the efficiency and quality of maintenance operations, this study proposes a collaborative maintenance planning based on the concept of imperfect maintenance. In this regard, this study develops a multi-objective optimization model to optimize parallel maintenance sequences considering maintenance profit, maintenance cost, maintenance team, and resource limitations. We show the feasibility of the proposed multi-objective optimization model through a real case of maintenance practice for the components of an assistor device. For analyzing the complexity of the proposed maintenance sequence planning problem, this study introduces a new multi-objective metaheuristic algorithm which is an enhanced multi-objective gravitational search algorithm (EMOGSA) to find high-quality Pareto solutions for the proposed problem. Different multi-objective evaluation metrics are used to study the performance of the proposed algorithm. From the results, the proposed model and developed solution algorithm can help maintenance decision-makers to determine complex maintenance planning.Note to Practitioners—This paper deals product with a maintenance and proposes gravitational search algorithm based on only maintenance task, which maintenance task. The goal of this paper is to analyze the maintenance problem from the perspective of collaboration of multiple maintenance teams/operators.

The escalating consumption and disposal of electronic products have spurred a pressing demand for environmental conservation. Traditional disassembly factories encounter challenges when handling discarded products from various locations, including high costs and limited flexibility. This study addresses a multifactory disassembly process optimization problem, taking into account worker posture and the selection of disassembly line types. Subsequently, a mathematical model to maximize profit is built. The reinforcement learning algorithm, Categorical deep Q network (DQN), is utilized to find optimal solutions. Experimental results are compared with those from CPLEX to validate the precision and viability of the proposed model. Furthermore, we compare the proposed solution with various reinforcement learning algorithms, including DQN, proximal policy optimization, and Advantage Actor–Critic. The effectiveness of the proposed model and algorithm is verified by experiments on several cases with different complexity scales.

Treating each intersection as basic agent, multi-agent reinforcement learning (MARL) methods have emerged as the predominant approach for distributed adaptive traffic signal control (ATSC) in multi-intersection scenarios, such as arterial coordination. MARL-based ATSC currently faces two challenges: disturbances from the control policies of other intersections may impair the learning and control stability of the agents; and the heterogeneous features across intersections may complicate coordination efforts. To address these challenges, this study proposes a novel MARL method for distributed ATSC in arterials, termed the Distributed Controller for Heterogeneous Intersections (DCHI). The DCHI method introduces a Neighborhood Experience Sharing (NES) framework, wherein each agent utilizes both local data and shared experiences from adjacent intersections to improve its control policy. Within this framework, the neural networks of each agent are partitioned into two parts following the Knowledge Homogenizing Encapsulation (KHE) mechanism. The first part manages heterogeneous intersection features and transforms the control experiences, while the second part optimizes homogeneous control logic. Experimental results demonstrate that the proposed DCHI achieves efficiency improvements in average travel time of over 30% compared to traditional methods and yields similar performance to the centralized sharing method. Furthermore, vehicle trajectories reveal that DCHI can adaptively establish green wave bands in a distributed manner. Given its superior control performance, accommodation of heterogeneous intersections, and low reliance on information networks, DCHI could significantly advance the application of MARL-based ATSC methods in practice.

Traffic flow prediction is essential for intelligent transportation systems, yet privacy concerns and limited cross-regional data sharing hinder accurate modeling of global traffic patterns. This paper proposes a Federated Graph Neural Network with Equivalent Hypergraph (FGNNEH) framework to address these challenges by preserving privacy and enhancing cross-client collaboration. FGNNEH consists of two key stages. First, local traffic networks are transformed into high-dimensional hypernodes through an integrated process of backbone network extraction, kernel matrix analysis, and multilayer perceptrons. The backbone network extraction simplifies graph structures by isolating critical nodes and edges based on topological centrality, ensuring computational efficiency while retaining key spatial dependencies. Kernel matrix analysis captures complex nonlinear correlations among traffic flow features, including spatial-temporal dependencies and region-specific dynamics, enabling more effective feature representation. The multilayer perceptrons further fuse these features into robust hypernode embeddings that encapsulate both structural and traffic flow characteristics. Second, a global hypergraph construction mechanism is introduced to optimize inter-client collaboration. This mechanism employs an iterative performance feedback loop to dynamically add or remove edges between hypernodes, addressing the issue of lost inter-client connections and enabling effective cross-regional information exchange. Together, these components reconstruct a global traffic model that balances local privacy with holistic accuracy. Experiments on real-world traffic datasets, including PeMSD4, METR-LA and Guangzhou, demonstrate that FGNNEH outperforms existing methods in prediction accuracy, computational efficiency, and scalability.

Large-scale many-objective optimization problems (LSMaOPs) pose great difficulties for traditional evolutionary algorithms due to their slow search for Pareto-optimal solutions in huge decision space and struggle to balance diversity and convergence among numerous locally optimal solutions. An objective space linear inverse mapping method has successfully achieved great saving in execution time in solving LSMaOPs. Linear mapping is a fast and straightforward way, but fails to characterize a complex functional relationship. If we can enhance the expressive capacity of a mapping model, and further obtain a more general function approximator, can the evolutionary search based on objective space mapping be more efficient? To answer this interesting question, this work proposes to employ nonlinear activation functions widely used in neural networks so as to enhance the efficiency of objective space inverse mapping, thus efficiently generating excellent offspring population. A new evolutionary optimization framework based on decision variable analysis is proposed to solve LSMaOPs. In order to demonstrate its performance, this work carries out empirical experiments involving massive decision variables and many objectives. Experimental results prove its superiority over some representative and updated ones.

The benefit of low latency has led to growing interests in Space-Air-Ground (SAG) integrated networks. To provide better service, we need to consider how to schedule paths for many communication tasks concurrently, thus resulting in a complex optimization problem. Considering the potential benefits of evolutionary multitasking, a new routing framework is designed to draw upon excellent evolutionary experience and enhance the routing performance. In the proposed framework, the population of each task is divided into better-performing and worse-performing sub-populations. A random exchange strategy is designed for the former, while an inter-task knowledge transfer strategy is proposed for the latter with the aim for improving the population diversity. A series of results demonstrate that the proposed framework is an effective routing solution strategy. Moreover, it significantly outperforms baseline solvers and the shortest path strategy.

Soft sensing technique has been extensively used to predict key quality variables in industrial systems. However, due to the difficulty of quality variable acquisition, only limited labeled data samples are available, and a large number of unlabeled ones are discarded. This raises a big challenge to build a high-quality soft sensor model. In order to furthest exploit information contained in both the labeled and unlabeled data, this paper proposes a principal component-based semi-supervised extreme learning machine (referred to as PCSELM) model. Through this model, extracting latent features and learning nonlinear input-output relationship can be simultaneously performed. In this way, unlabeled samples are utilized efficiently for feature representation and model accuracy improvement. Moreover, mixed regularizations are employed to work in conjunction with the PCSELM to obtain high generality and flexibility. We also derive an efficient parameter learning algorithm with theoretically guaranteed convergence. Comprehensive experiments are conducted via an industrial process. Comparison results illustrate that the proposed PCSELM outperforms other representative semi-supervised algorithms.Note to Practitioners—Industrial processes in general incorporate unlabeled samples which are ubiquitous in real world applications. The focus of this paper is to develop a semi-supervised soft sensor model (PCSELM) that is capable to learn the nonlinear features and regression relationship efficiently with both the labeled and unlabeled samples. The proposed model can automatically implement the feature representation and the input-output relationship description. In addition, we introduce mixed norms for the model objective function to improve the final prediction performance and generalization. A feasible model optimization technique with proved convergence is also derived. Experimental results based on a real industrial dataset manifest that PCSELM achieves better prediction accuracy than its peers.

When we recognize images with the help of Artificial Neural Networks (ANNs), we often wonder how they make decisions. A widely accepted solution is to point out local features as decisive evidence. A question then arises: Can local features in the latent space of an ANN explain the model output to some extent? In this work, we propose a modularized framework named MemeNet that can construct a reliable surrogate from a Convolutional Neural Network (CNN) without changing its perception. Inspired by the idea of time series classification, this framework recognizes images in two steps. First, local representations named memes are extracted from the activation map of a CNN model. Then an image is transformed into a series of understandable features. Experimental results show that MemeNet can achieve accuracy comparable to most models' through a set of reliable features and a simple classifier. Thus, it is a promising interface to use the internal dynamics of CNN, which represents a novel approach to constructing reliable models.

In most interactive image generation tasks, given regions of interest (ROI) by users, the generated results are expected to have adequate diversities in appearance while maintaining correct and reasonable structures in original images. Such tasks become more challenging if only limited data is available. Recently proposed generative models complete training based on only one image. They pay much attention to the monolithic feature of the sample while ignoring the actual semantic information of different objects inside the sample. As a result, for ROI-based generation tasks, they may produce inappropriate samples with excessive randomicity and without maintaining the related objects’ correct structures. To address this issue, this work introduces a morphologic-structure-aware generative adversarial network named MOGAN that produces random samples with diverse appearances and reliable structures based on only one image. For training for ROI, we propose to utilize the data coming from the original image being augmented and bring in a novel module to transform such augmented data into knowledge containing both structures and appearances, thus enhancing the model’s comprehension of the sample. To learn the rest areas other than ROI, we employ binary masks to ensure the generation isolated from ROI. Finally, we set parallel and hierarchical branches of the mentioned learning process. Compared with other single image generative adversarial network schemes, our approach focuses on internal features, including the maintenance of rational structures and variation on appearance. Experiments confirm a better capacity of our model on ROI-based image generation tasks than its competitive peers.

Social networks are an essential component of the Internet of People (IoP) and play an important role in stimulating interactive communication among people. Graph convolutional networks provide methods for social network analysis with its impressive performance in semi-supervised node classification. However, the existing methods are based on the assumption of balanced data distribution and ignore the imbalanced problem of social networks. In order to extract the valuable information from imbalanced data for decision making, a novel method named minority-weighted graph neural network (mGNN) is presented in this article. It extends imbalanced classification ideas in the traditional machine learning field to graph-structured data to improve the classification performance of graph neural networks. In a node feature aggregation stage, the node membership values among nodes are calculated for minority nodes’ feature aggregation enhancement. In an oversampling stage, the cost-sensitive learning is used to improve edge prediction results of synthetic minority nodes, and further raise their importance. In addition, a Gumbel distribution is adopted as an activation function. The proposed mGNN is evaluated on six social network data sets. Experimental results show that it yields promising results for imbalanced node classification.

Multitask optimization (MTO) is a new optimization paradigm that leverages useful information contained in multiple tasks to help solve each other. It attracts increasing attention in recent years and gains significant performance improvements. However, the solutions of distinct tasks usually obey different distributions. To avoid that individuals after intertask learning are not suitable for the original task due to the distribution differences and even impede overall solution efficiency, we propose a novel multitask evolutionary framework that enables knowledge aggregation and online learning among distinct tasks to solve MTO problems. Our proposal designs a domain adaptation-based mapping strategy to reduce the difference across solution domains and find more genetic traits to improve the effectiveness of information interactions. To further improve the algorithm performance, we propose a smart way to divide initial population into different subpopulations and choose suitable individuals to learn. By ranking individuals in target subpopulation, worse-performing individuals can learn from other tasks. The significant advantage of our proposed paradigm over the state of the art is verified via a series of MTO benchmark studies.

The generation and updating of solutions, e.g., crossover and mutation, of many existing evolutionary algorithms directly operate on decision variables. The operators are very time consuming for large-scale and many-objective optimization problems. Different from them, this work proposes an objective space-based population generation method to obtain new individuals in the objective space and then map them to decision variable space and synthesize new solutions. It introduces three new objective vector generation methods and uses a linear mapping method to tightly connect objective space and decision one to jointly determine new-generation solutions. A loop can be formed directly between two spaces, which can generate new solutions faster and use more feedback information in the objective space. In order to demonstrate the performance of the proposed algorithm, this work performs a series of empirical experiments involving both large-scale decision variables and many objectives. Compared with the state-of-the-art traditional and large-scale algorithms, the proposed method exceeds or at least reaches its peers’ best level in overall performance while achieving great saving in execution time.

The airline crew pairing (ACP) problem is one of the most challenging problems in airline operations. It aims to decide the optimal connections among pairs of flights assigned to flight crews. However, the number of connections grows exponentially with the increasing number of flights. Conventional approaches usually follow a two-stage method, i.e., divide-and-conquer. The flight sequences (pairings) spanning multiple days are firstly generated for each crew. Then, the best pairing set is chosen based on minimum operational costs. When the number of flights is large, it becomes too difficult to generate all feasible pairings and find the optimum. In order to solve large-scale ACP problems efficiently, we propose a novel iterative optimization framework based on monte carlo tree search (MCTS). Thus, the speed of pairing generation and solution accuracy can be improved. To evaluate the performance of the proposed method, we conduct experiments on different scales of real-world instances provided by airline companies. The empirical results show that the proposed approach is capable of producing high-quality solutions, especially in large-scale instances.

Domain adaptation (DA) aims to accomplish tasks on unlabeled target data by learning and transferring knowledge from related source domains. In order to learn a discriminative and domain-invariant model, a critical step is to align source and target data well and thus reduce their distribution divergence. But existing DA methods mainly align the global feature distributions in distorted original space, which neglects their fine-grained local information and intrinsic geometrical structures. Moreover, some methods rely heavily on pseudo-labels to align features, which may undermine adaptation performance and lead to negative transfer. We propose an efficient discriminative manifold distribution alignment (DMDA) approach, which improves feature transferability by aligning both global and local distributions and refines a discriminative model by learning geometrical structures in manifold space. In addition, when learning geometrical structures, DMDA is exempt from the uncertainty and error brought by pseudo-labels of a target domain. It is very concise and efficient to be implemented by integrating learning steps and obtaining solutions directly. Extensive experiments on 68 DA tasks from seven benchmarks and subsequent analyses show that DMDA outperforms the compared methods in both classification accuracy and time efficiency, thus representing a significant advance in the DA field.

Extreme learning machine (ELM) is suitable for nonlinear soft sensor development. Yet it faces an overfitting problem. To overcome it, this work integrates bound optimization theory with variational Bayesian (VB) inference to derive novel L1 norm-based ELMs. An L1 term is attached to the squared sum cost of prediction errors to formulate an objective function. Considering the nonconvexity and nonsmoothness of the objective function, this article uses bound optimization theory, and constructs a proper surrogate function to equivalently convert a challenging L1 norm-based optimization problem into easy one. Then, VB inference is adopted for optimizing the converted problem. Thus, an L1 norm-based ELM can be efficiently optimized by an alternating optimization algorithm with a proved convergence. Finally, a soft sensor is developed based on the proposed algorithm. An industrial case study is carried out to demonstrate that the proposed soft sensor is competitive against recent ones.

Text classification is a fundamental and important area of natural language processing for assigning a text into at least one predefined tag or category according to its content. Most of the advanced systems are either too simple to get high accuracy or centered on using complex structures to capture the genuinely required category information, which requires long time to converge during their training stage. In order to address such challenging issues, we propose a dynamic embedding projection-gated convolutional neural network (DEP-CNN) for multi-class and multi-label text classification. Its dynamic embedding projection gate (DEPG) transforms and carries word information by using gating units and shortcut connections to control how much context information is incorporated into each specific position of a word-embedding matrix in a text. To our knowledge, we are the first to apply DEPG over a word-embedding matrix. The experimental results on four known benchmark datasets display that DEP-CNN outperforms its recent peers.

A model can be easily influenced by unseen factors in nonstationary environments and fail to fit dynamic data distribution. In a classification scenario, this is known as a concept drift. For instance, the shopping preference of customers may change after they move from one city to another. Therefore, a shopping website or application should alter recommendations based on its poorer predictions of such user patterns. In this article, we propose a novel approach called the multiscale drift detection test (MDDT) that efficiently localizes abrupt drift points when feature values fluctuate, meaning that the current model needs immediate adaption. MDDT is based on a resampling scheme and a paired student t -test. It applies a detection procedure on two different scales. Initially, the detection is performed on a broad scale to check if recently gathered drift indicators remain stationary. If a drift is claimed, a narrow scale detection is performed to trace the refined change time. This multiscale structure reduces the massive time of constantly checking and filters noises in drift indicators. Experiments are performed to compare the proposed method with several algorithms via synthetic and real-world datasets. The results indicate that it outperforms others when abrupt shift datasets are handled, and achieves the highest recall score in localizing drift points.

In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. However, through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks. Generative adversarial network (GAN) loss is widely used in adversarial adaptation learning methods to reduce an across-domain distribution difference. However, it becomes difficult to decline such distribution difference if generator or discriminator in GAN fails to work as expected and degrades its performance. To solve such cross-domain classification problems, we put forward a novel adaptation framework called generative adversarial distribution matching (GADM). In GADM, we improve the objective function by taking cross-domain discrepancy distance into consideration and further minimize the difference through the competition between a generator and discriminator, thereby greatly decreasing cross-domain distribution difference. Experimental results and comparison with several state-of-the-art methods verify GADM's superiority in image classification across domains.

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human-machine interactions.

Question Generation aims to create various questions from a given passage, which can provide education material, improve the training of question answering, and help chat bots have cold-to-start or continue to talk to people. Chinese Question Generation is a new research area, which mainly uses a rule-based model to convert declarative sentences into questions. We propose a Stroke-Aware Copy Network (SAC-Net), which is a sequence-to-sequence model. It can enhance the performance of Chinese question generation by using various specific components and techniques for Chinese characteristics. In view of the fact that the morphology of Chinese characters is related to its meaning, this research introduces the information of strokes in Chinese and obtain the relationship among strokes in Chinese characters. This model introduces the direction information of the questions, which helps us generate more diverse questions. For the out-of-vocabulary (OOV) word problem that often occurs in Chinese, this research has improved a Self-attention Copy mechanism. Extensive evaluations confirm that this model improves the performance of Chinese problem generation significantly and represents a state-of-the-art neural Chinese problem generation model.

Imbalanced classification using a support vector machine (SVM) is a normal but crucial problem in machine learning. Compared with binary classification, multiclass classification is much more complicated. Most existing studies on imbalanced classification using SVM focus on binary imbalanced classification; while only few of them look into imbalanced classification with multiple classes. Pre-clustering is a useful technique to prepare proper data from an imbalanced dataset for a classifier. It can be used to extract the feature of a dataset first and improve classification performance. Density peak based on Euclidean distance proves its effectiveness and generality in clustering. Motivated by this and the fact that the number of clusters is known in multi-class classification using a one-vs-rest strategy, we combine density peak clustering and SVM to propose a new pre-clustering method to perform effective imbalanced classification with multiple classes. Specifically, we transform a multi-class classification problem into several binary classification tasks. The results on 5 public datasets in terms of F-measure, G-mean and Area Under Curve (AUC) show its superiority over the original SVM and SVM with other methods including random under-sampling, Synthetic Minority Oversampling Technique, pre-clustering using K-Means and EasyEnsemble methods using either a one-vs-rest or one-vs-one strategy.

Decomposition of a multiobjective optimization problem (MOP) into several simple multiobjective subproblems, named multiobjective evolutionary algorithm based on decomposition (MOEA/D)-M2M, is a new version of multiobjective optimization-based decomposition. However, it fails to consider different contributions from each subproblem but treats them equally instead. This paper proposes a collaborative resource allocation (CRA) strategy for MOEA/D-M2M, named MOEA/D-CRA. It allocates computational resources dynamically to subproblems based on their contributions. In addition, an external archive is utilized to obtain the collaborative information about contributions during a search process. Experimental results indicate that MOEA/D-CRA outperforms its peers on 61% of the test cases in terms of three metrics, thereby validating the effectiveness of the proposed CRA strategy in solving MOPs.

Imbalance classification is a common but critical problem in machine learning and artificial intelligence. Derived from structural risk minimization, a Support Vector Machine (SVM) enjoys great reputation in classification. However, the original SVM is not suitable for the imbalance classification and the existing modifications of SVM for this kind of problems fail to take the distribution of datasets into full consideration, thereby leading to some remarkable loss in their classification performance. Recently, an Adaptive Clustering by Fast Search and Find of Density Peaks (ADPclust) is proposed and performs well in finding cluster centroids in a sample space automatically and more reliably by using adaptive density peak detection and silhouette theory. Motivated by this, this work proposes an adaptive pre-clustering SVM (AP-SVM) such that the information of the original dataset distribution is well utilized to yield balanced sub-datasets for accurate and efficient classification. Specifically, AP-SVM clusters the majority into several groups given a dataset and then applies undersampling on every cluster to re-balance the dataset to be used in the SVM classification step. After experiments on 10 binary public datasets and evaluation using Area Under Curve (AUC), F-Measure, G-Mean, we well show the superiority of the proposed method over SVM, Synthetic Minority Over-sampling Technique algorithm (SMOTE), Undersampling-SVM (U-SVM), K-Means, Fuzzy C Means and EasyEnsemble.

The highly strategic nature of terrorist attacks has often frustrated attempts at locating emergency response facilities. To better determine the optimal location of such facilities, we present a leader-follower game between State and Terrorist by considering facility failures. The first stage of the game allows State to make a facility location decision and facility assignment to the attacked city, while the second stage allows Terrorist to select one city to attack after observing the State's strategy. The game is translated into a minmaxmin problem, and a population-based heuristic algorithm is proposed to solve it. We evaluate the performance of both model and heuristic by using an emergency example. Our results indicate that the proposed algorithm is able to generate suitable facility location solutions, allowing us to deploy resources more efficiently during a terrorist attack to where they are needed.

A support vector machine (SVM) plays a prominent role in classic machine learning, especially classification and regression. Through its structural risk minimization, it has enjoyed a good reputation in effectively reducing overfitting, avoiding dimensional disaster, and not falling into local minima. Nevertheless, existing SVMs do not perform well when facing class imbalance and large-scale samples. Undersampling is a plausible alternative to solve imbalanced problems in some way, but suffers from soaring computational complexity and reduced accuracy because of its enormous iterations and random sampling process. To improve their classification performance in dealing with data imbalance problems, this work proposes a weighted undersampling (WU) scheme for SVM based on space geometry distance, and thus produces an improved algorithm named WU-SVM. In WU-SVM, majority samples are grouped into some subregions (SRs) and assigned different weights according to their Euclidean distance to the hyper plane. The samples in an SR with higher weight have more chance to be sampled and put to use in each learning iteration, so as to retain the data distribution information of original data sets as much as possible. Comprehensive experiments are performed to test WU-SVM via 21 binary-class and six multiclass publically available data sets. The results show that it well outperforms the state-of-the-art methods in terms of three popular metrics for imbalanced classification, i.e., area under the curve, F-Measure, and G-Mean.

Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, -measure, and -mean.

In order to protect the environment and slow down global warming trend, many governments and environmentalists are keen at promoting the use of plug-in hybrid electric vehicles (PHEVs). As a result, more and more PHEVs have been put into use. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective load scheduling, they fail to meet the real requirements that need one to conduct multiple objective optimization. This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss. When we apply existing multi-objective evolutionary algorithms (MOEAs), i.e., multi-objective particle swarm optimization (MOPSO), Nondominated Sorting Genetic Algorithm II, MOEA based on decomposition, and multi-objective differential evolutionary algorithm to solve it, because its high dimension and special conditions we find that they fail to reach the Pareto Front or converge into a relatively small area only. Therefore, we propose a weight aggregation (WA) strategy and implement a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches. Furthermore, WA is also combined with other MOEAs to solve the defined scheduling problem.

As the supply potential has declined gradually and the pressure for better environment intensifies, the demand for clean energy arises continuously. The consumptive demand for Plug-in Hybrid Electric Vehicle (PHEV) thus increases. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective optimal load scheduling, they fail to meet the real requirements for efficient multiple objective optimization. This work proposes a novel weight-aggregation based multi-objective particle swarm optimization method to solve the load scheduling problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches.

Motivated by neuroscience discoveries during the last few years, many studies consider pulse-coupled neural networks with spike-timing as an essential component in information processing by the brain. There also exists some technical challenges while simulating the networks of artificial spiking neurons. The existing studies use a Hodgkin-Huxley (H-H) model to describe spiking dynamics and neuro-computational properties of each neuron. But they fail to address the effect of specific non-Gaussian noise on an artificial H-H neuron system. This paper aims to analyze how an artificial H-H neuron responds to add different types of noise using an electrical current and subunit noise model. The spiking and bursting behavior of this neuron is also investigated through numerical simulations. In addition, through statistic analysis, the intensity of different kinds of noise distributions is discussed to obtain their relationship with the mean firing rate, interspike intervals, and stochastic resonance.

Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.

Interacting with a random environment, Learning Automata (LAs) are automata that, generally, have the task of learning the optimal action based on responses from the environment. Distinct from the traditional goal of Learning Automata to select only the optimal action out of a set of actions, this paper considers a multiple-action selection problem and proposes a novel class of Learning Automata for selecting an optimal subset of actions. Their objective is to identify the optimal subset: the top k out of r actions. Based on conventional continuous pursuit and discretized pursuit learning schemes, this paper introduces four pursuit learning schemes for selecting the optimal subset, called continuous equal pursuit, discretized equal pursuit, continuous unequal pursuit and discretized unequal pursuit learning schemes, respectively. In conjunction with a reward-inaction learning paradigm, the above four schemes lead to four versions of pursuit Learning Automata for selecting the optimal subset. The simulation results present a quantitative comparison between them.

Particle Swarm Optimization (PSO) is a brilliant evolutionary algorithm adaptable to various kinds of optimization problems in distinct fields. However, when facing noisy environments, its performance suffers from unexpected noise. To address this issue, one of the widely-used mechanisms is the resampling method that is based on the fact that the true objective value can be achieved by re-evaluations. Such method allocates a fixed number of re-evaluations before running but cannot change allocations according to the current environment adaptively. It may result in the waste of re-evaluations allocated to unpromising candidate particles. This paper proposes a novel hybrid approach by integrating PSO with Learning Automata (LAs) in noisy environments. LAs are well-known for their self-adaption, automatic learning capability as well as low computational complexity. They are able to converge in different situations. The proposed hybrid approach achieves much faster convergence than the existing ones by performing fewer re-evaluations in simple environments than in complex one automatically. This mechanism enables it to find the best particle efficiently. With its self-adaption and automatic learning capability, it leads to a more accurate and faster algorithm. Besides, its distinct selection mechanism helps it achieve a significantly lower computational complexity than that of the-state-of-the-art resampling methods. Through experiments on 20 large-scale benchmark functions subject to different levels of noise, it is validated that, the proposed approach is able to achieve much better performance results in terms of accuracy and convergence rate than the existing ones.

Distributed generation becomes more and more important in modern power systems. However, the increasing use of distributed generators causes the concerns on the increasing system risk due to their likely failure or uncontrollable power outputs based on such renewable energy sources as wind and the sun. This work for the first time formulates an optimal power flow problem by considering controllable and uncontrollable distributed generators in power networks. The problem for the cases of single and multiple generator failures is addressed as an example. The methods are presented to find its power output solution of controllable online generators via particle swarm optimization and group search optimizer for coping with the difficult scenarios in a power network. The proposed methods are tested on an IEEE 14-bus system, and several population initialization strategies are investigated and compared for the algorithms. The simulation results confirm their effectiveness for optimal power management and effective control of a power network.

This paper presents an efficient and reliable approach based on swarm intelligence to solve the optimal power flow problem. The optimal setting of distributed generations (DGs) is addressed if one or more generators broken down in a power distribution system, to achieve minimization of fuel cost and voltage profile stability. The proposed approach employs particle swarm optimization (PSO) and group search optimizer (GSO) for optimal setting of DGs. These algorithms are executed and compared on IEEE 14-bus test system, respectively. The results confirm the effectiveness and potential application of the proposed swarm-based optimization method in power distribution systems.

The optimal control of fuzzy systems with constraints is still an open problem. Our focus concerns the optimal control problem of fuzzy systems derived from receding horizon control (RHC) schemes. We consider methods to numerically compute the value function for general fuzzy systems. The numerical method that is developed using the finite difference with sigmoidal transformation is a stable and convergent algorithm for the Hamilton-Jacobi-Bellman (HJB) equation. An optimization procedure is developed to increase the calculation accuracy with less computation time. A parallel-processing method is employed in the optimization procedure. The optimization results are applied to the controller design of general fuzzy dynamic systems. Employing the principle of conventional RHC schemes, RHC-form controllers are designed for some classes of fuzzy dynamic systems. The basic ideas are as follows. First, the value function is calculated by numerical methods. Then, the value function is used as controller-design parameters to redesign RHC controllers for fuzzy systems, which is motivated by the inverse Lyapunov function design method. It is proven that the closed-loop system is asymptotically stable. An engineering implementation of the controller redesign scheme is discussed. Meanwhile, the parallel-processing framework that can improve the closed-loop performance is also introduced.

Turbo Codes present a new direction for the channel encoding, especially since they were adopted for multiple norms of telecommunications, such as deeper communication, etc. To obtain an excellent performance, it is necessary to design robust turbo code interleaver and decoding algorithms. In this paper, we are investigating particle swarm algorithm as a promising optimization method to find good interleaver for the large frame sizes, as well as design the decoding optimization mode (PSO-Turbo); and apply the proposed PSO-Turbo codes mode to the security radio data transmission; in which, a kind of transport control proposal based on PSO-Turbo optimizer for CBTC wireless channel is designed and simulated to validate our method.