High-accuracy forecasting of short-term electricity load (STEL) is essential for real-time industrial operations, particularly within the context of Internet of Things (IoT), yet it remains challenging due to the complex and nonlinear nature of power system fluctuations. Given that the brain analysis capabilities far surpass those of neural networks in lots of scenarios, this paper proposes a brain-inspired network, namely BI-Net, that integrates distinct brain mechanisms for enhanced STEL forecasting. On one hand, the STEL forecasting is framed as a cognitive process, and the BI-Net’s flowchart is designed to mimic the human brain’s layer-by-layer environmental cognition, thereby improving structural rationality. On the other hand, by leveraging multiple brain-inspired mechanisms, including parallel, cooperative, asynchronous, and gating mechanisms, the BI-Net effectively captures nonlinear and long short-term dependencies, enabling enhanced extraction and integration of diverse features. Specifically, the network architecture incorporates a gating module derived from the gating mechanism, a high-level feature extraction module inspired by parallel and asynchronous mechanisms, and an interactive coordination module based on the cooperative mechanism. Evaluated on three real-world STEL datasets, BI-Net demonstrates superior forecasting performance and serves as an effective tool for power management, combining the analytical strengths of brain science with rationality in its multi-level design.

Constrained multiobjective optimization problems are widespread in practical engineering fields. Scholars have proposed various effective constrained multiobjective evolutionary algorithms (CMOEAs) for such problems. However, most existing algorithms overlook the differences between different decision variables in influencing the degree of constraint violation and still lack an effective handling mechanism for constraint-sensitive variables. To address this issue, a two-stage cooperation multiobjective evolutionary algorithm guided by constraint-sensitive variables (CV-TCMOEA) is proposed. In the first stage, a relatively simple auxiliary problem with only a few dominant constraints is constructed to approximate the original problem. After obtaining a set of approximate Pareto optimal solutions by dealing with the auxiliary problem, in the second stage, a constraint-sensitive variable-guided multistrategy cooperation search method is developed. In this method, decision variables are divided into two types: 1) constraint-sensitive and 2) constraint-insensitive variables, and a variable-type-guided cooperative individual update strategy is proposed to autonomously select appropriate search strategies for different types of variables. Experimental results on 28 benchmark functions and 10 engineering problems demonstrated the superiority of the CV-TCMOEA over seven state-of-the-art CMOEAs.

Design change is an important issue in complex product development projects. In a complex product with numerous parts (also known as components), the change of one key part may spread to other parts associated with it, generating a chain reaction throughout the entire project. Therefore, it is necessary to select a suitable change plan involving only fewer crucial parts in order to enhance the product’s performance, minimize change cost, and reduce change duration/time. Focusing on the case where the correlation strength between parts cannot be accurately obtained, in this paper we study an interval multi-objective evolutionary algorithm for finding excellent design change plans. Firstly, on the basis of the established multi-layer product network with interval correlation weights, an interval multi-objective optimization model of the product design change planning problem is established, where three new objective functions regarding product performance, carbon trading cost and supply risk are defined. Then, a constraint multi-objective evolutionary algorithm based on interval Pareto dominance is proposed to search for optimal change plans. Several novel operators, including the problem characteristic-guided population update strategy, the probability-based interval Pareto dominance, and the interval constraint handling strategy, are developed to enhance the algorithm’s performance. Finally, the proposed algorithm is compared with eight existing algorithms on the two design change cases, experimental results revealed its effectiveness.

Real-world production scenarios often involve multiobjective optimization problems with intricate constraints. Although there has been a growing interest in multiobjective problems with complex constraints, such as the vehicle routing problem with time windows, existing multiobjective evolutionary optimization techniques still face significant challenges, particularly when addressing the fragmented and narrow feasible regions that arise from these constraints. Our research introduces a refined framework tailored for complex constrained multiobjective evolutionary optimization. The methodology conducts an initial strong-weak analysis to categorize constraints and merges each strong constraint with all weak constraints to form subsets. Each subset, combined with the original objective functions, defines a subproblem. Independent optimization of the original problem and subproblems is carried out by utilizing multiple populations. Information acquired from the subproblems’ populations is transferred into the population of the original issue, thereby expediting the detection of the feasible region and simplifying the resolution of the original problem. The efficacy of our innovative algorithm, when benchmarked against traditional constrained multiobjective evolutionary algorithms across 72 test functions, has demonstrated superior convergence, diversity, and competitiveness.

The Internet of Things (IoT) is used in homes and hospitals and deployed outdoors to control and report environmental changes, prevent fires, and perform many more beneficial functions. However, all these benefits come at the tremendous risk of loss of privacy and security issues. To protect the IoT, much research has been carried out to address these risks and find better ways to eliminate them or at least minimize their impact on user privacy and security requirements. This paper expounds various network security risks faced by the IoT, analyzes their impacts, discusses risk assessment methods, shows the causes and hazards of these threats, and proposes an overall framework of privacy security protection. This paper summarizes the typical defect types in the implementation of IoT firmware, analyzes the generation mechanism of typical defects from the perspectives of fuzzy testing, program verification and machine learning, and compares and expounds the progress of security research for several common IoT protocols. This paper analyzes and summarizes the mainstream access control model in the existing IoT and the access control model after using the blockchain and builds a new integrated AIoT architecture for intelligent information processing. Finally, this paper expounds on the current legal development status of the privacy protection of network information in various countries and discusses the future prospects of the IoT.

In practice, some optimization problems require expensive calculation and exhibit multimodal characteristics simultaneously. These problems are called high-dimensional expensive multimodal optimization problems. When addressing such problems, existing surrogate-assisted evolutionary algorithms (SAEAs) encounter the “curse of dimensionality," which severely affects their capability to search optimal solutions. Therefore, this study proposed a surrogate and autoencoder-assisted multitask particle swarm optimization algorithm. First, an autoencoder-embedded multitask evolutionary framework was established to transform a high-dimensional multimodal optimization problem into multiple low-dimensional subproblems or subtasks. Further, a multi-level surrogate model management mechanism combining mirror learning was proposed. An appropriate local surrogate model can be rapidly generated for each modality of the problem. Moreover, a dual-mode local exploitation strategy was developed to improve the capability of swarm to exploit each subtask. The proposed algorithm was compared with seven existing SAEAs on 33 benchmark functions and the aeroengine aerodynamic design optimization problem. Experimental results revealed that the proposed algorithm can obtain multiple highly competitive optimal solutions, including global optimal solutions.

Inspired by the success of Contrastive Learning (CL) in computer vision and natural language processing, Graph Contrastive Learning (GCL) has been developed to learn discriminative node representations on graph datasets. However, the development of GCL on Heterogeneous Information Networks (HINs) is still in the infant stage. For example, it is unclear how to augment the HINs without substantially altering the underlying semantics, and how to design the contrastive objective to fully capture the rich semantics. Moreover, early investigations demonstrate that CL suffers from sampling bias, whereas conventional debias- ing techniques are empirically shown to be inadequate for GCL. How to mitigate the sampling bias for heterogeneous GCL is another important problem. To address the aforementioned challenges, we propose a novel Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. In particular, we use metapaths as the augmentation to generate multiple subgraphs as multi-views, and propose a contrastive objective to maximize the mutual information between any pairs of metapath-induced views. To alleviate the sampling bias, we further propose a positive sampling strategy to explicitly select positives for each node via jointly considering semantic and structural information preserved on each metapath view. Extensive experiments demonstrate HGCML consistently outperforms state-of-the-art baselines on five real-world benchmark datasets. To enhance the repro- ducibility of our work, we make all the code publicly available at https://github.com/Zehong-Wang/HGCML.

Task allocation is a crucial issue of mobile crowdsensing. The existing crowdsensing systems normally select the optimal participants giving no consideration to the sudden departure of mobile users, which significantly affects the sensing quality of tasks with a long sensing period. Furthermore, the ability of a mobile user to collect high-precision data is commonly treated as the same for different types of tasks, causing the unqualified data for some tasks provided by a competitive user. To address the issue, a dynamic task allocation model of crowdsensing is constructed by considering mobile user availability and tasks changing over time. Moreover, a novel indicator for comprehensively evaluating the sensing ability of mobile users collecting high-quality data for different types of tasks at the target area is proposed. A new Q -learning-based hyperheuristic evolutionary algorithm is suggested to deal with the problem in a self-learning way. Specifically, a memory-based initialization strategy is developed to seed a promising population by reusing participants who are capable of completing a particular task with high quality in the historical optima. In addition, taking both sensing ability and cost of a mobile user into account, a novel comprehensive strength-based neighborhood search is introduced as a low-level heuristic (LLH) to select a substitute for a costly participant. Finally, based on a new definition of the state, a Q -learning-based high-level strategy is designed to find a suitable LLH for each state. Empirical results of 30 static and 20 dynamic experiments expose that this hyperheuristic achieves superior performance compared to other state-of-the-art algorithms.

Expensive constraint multimodal optimization problems (ECMMOPs) have such characteristics as expensive objectives and constraints, and multiple optimal modalities simultaneously, which pose severe challenges to evolutionary optimization methods. This article studies an objective-constraint mutual-guided surrogate-assisted particle swarm optimization algorithm for the kind of problem, aiming to discover multiple competing feasible optimal solutions at a lower calculation cost. The algorithm designs first a new two-layer cooperative surrogate model framework based on heterogeneous database to effectively adjust the prediction accuracies of objective surrogates and constraint surrogates on different search regions. An objective-constraint mutual-guided partial evaluation strategy (O-C-PES) is developed to generate high-quality infilling samples for objective and constraint surrogates, respectively, based on which the number of unnecessary real evaluations can be significantly reduced. Moreover, a position feature-guided hybrid update mechanism (PF-HUM) is proposed to find more optimal solutions by searching excellent infeasible and feasible areas at the same time, and a feasible ratio-driven local search (FR-LS) strategy is proposed to improve the algorithm’s exploitation. Compared with four existing surrogate-assisted evolutionary algorithms (EAs) and one constraint multimodal EAs on 21 benchmark problems and three engineering instances, experiment results show that the proposed algorithm can simultaneously obtain multiple highly-competitive feasible optimal solutions with less computational cost.

Abstract The novel‐corona‐virus is presently accountable for 547,782 deaths worldwide. It was first observed in China in late 2019 and, the increase in number of its affected cases seriously disturbed almost every nation in terms of its economical, structural, educational growth. Furthermore, with the advancement of data‐analytics and machine learning towards enhanced diagnostic tools for the infection, the growth rate in the affected patients has reduced considerably, thereby making it critical for AI researchers and experts from medical radiology to put more efforts in this side. In this regard, we present a controlled study which provides analysis of various potential possibilities in terms of detection models/algorithms for COVID‐19 detection from radiology‐based images like chest x‐rays. We provide a rigorous comparison between the VGG16, VGG19, Residual Network, Dark‐Net as the foundational network with the Single Shot MultiBox Detector (SSD) for predictions. With some preprocessing techniques specific to the task like CLAHE, this study shows the potential of the methodology relative to the existing techniques. The highest of all precision and recall were achieved with DenseNet201 + SSD512 as 93.01 and 94.98 respectively.

The "curse of dimensionality" and the high computational cost have still limited the application of the evolutionary algorithm in high-dimensional feature selection (FS) problems. This article proposes a new three-phase hybrid FS algorithm based on correlation-guided clustering and particle swarm optimization (PSO) (HFS-C-P) to tackle the above two problems at the same time. To this end, three kinds of FS methods are effectively integrated into the proposed algorithm based on their respective advantages. In the first and second phases, a filter FS method and a feature clustering-based method with low computational cost are designed to reduce the search space used by the third phase. After that, the third phase applies oneself to finding an optimal feature subset by using an evolutionary algorithm with the global searchability. Moreover, a symmetric uncertainty-based feature deletion method, a fast correlation-guided feature clustering strategy, and an improved integer PSO are developed to improve the performance of the three phases, respectively. Finally, the proposed algorithm is validated on 18 publicly available real-world datasets in comparison with nine FS algorithms. Experimental results show that the proposed algorithm can obtain a good feature subset with the lowest computational cost.

The current critical global concerns regarding fossil fuel exhaustion and environmental pollution have been driving advancements in transportation electrification and related battery technologies. In turn, the resultant growing popularity of electric vehicles (EVs) calls for the development of a well-designed charging infrastructure. However, an inappropriate placement of charging stations might hamper smooth operation of the power grid and be inconvenient to EV drivers. Thus, the present work proposes a novel two-stage planning model for charging station placement. The candidate locations for the placement of charging stations are first determined by fuzzy inference considering distance, road traffic, and grid stability. The randomness in road traffic is modelled by applying a Bayesian network (BN). Then, the charging station placement problem is represented in a multi-objective framework with cost, voltage stability reliability power loss (VRP) index, accessibility index, and waiting time as objective functions. A hybrid algorithm combining chicken swarm optimization and the teaching-learning-based optimization (CSO TLBO) algorithm is used to obtain the Pareto front. Further, fuzzy decision making is used to compare the Pareto optimal solutions. The proposed planning model is validated on a superimposed IEEE 33-bus and 25-node test network and on a practical network in Tianjin, China. Simulation results validate the efficacy of the proposed model.

Obstacles in front of a train pose a significant threat to traffic safety, and many accidents happen under shunting mode when the speed of a train is below 45 km/h. The existing track object–detection algorithms encounter difficulty in balancing the detection precision and speed in shunting mode. Additionally, their accuracy is insufficient, particularly for small objects in complex environments. To address these problems, we propose a stable lightweight feature extraction and adaptive feature fusion network for real-time detection of obstacles in railway traffic scenarios to ensure driving safety. The proposed network consists of three modules. The stable bottom feature extraction module reduces the computational load and extracts more image information stably. The lightweight feature extraction module improves feature extraction using a simple and effective network. The enhanced adaptive feature fusion module fuses the image and original features, improving the multiscale detection accuracy under complex environments, particularly in the case of small objects. With a default input size of 416$\times 416$pixels (px), the proposed method achieves a detection speed of 81 FPS and a mean average precision of 94.75% for the railway traffic dataset as well as a detection speed of 78 FPS (26 FPS faster and 0.47% higher than those of YOLOv4, respectively) and a mean average precision of 42.5% for MS COCO. This indicates its potential for real-world railway object detection and other multi-target detection tasks. Additionally, the experimental results based on PASCAL VOC2007 and VOC2012 indicate that the proposed approach is considerably better than the state-of-the-art models.

Quantum-inspired differential evolution (QDE) is an evolutionary algorithm, which can effectively solve complex optimization problems. However, sometimes, it easily leads to premature convergence and low search ability and falls to local optima. To overcome these problems, based on the MSIQDE (improved QDE with multistrategies) algorithm, an enhanced MSIQDE algorithm based on mixing multiple strategies, namely, EMMSIQDE is proposed in this article. In the EMMSIQDE, a new differential mutation strategy of a difference vector is proposed to enhance the search ability and descent ability. Then, a new multipopulation mutation evolution mechanism is designed to ensure the relative independence of each subpopulation and the population diversity. The feasible solution space transformation strategy is used to achieve the optimal solution by mapping the quantum chromosome from a unit space to solution space. Finally, some multidimensional unimodal and multimodal functions are selected to demonstrate the optimization performance of EMMSIQDE. The results demonstrate that the EMMSIQDE is significantly better than the DE, QDE, QGA, and MSIQDE, and has better optimization ability, scalability, efficiency, and stability.

Clustering by jointly exploiting information from multiple views can yield better performance than clustering on one single view. Some existing multi-view clustering methods aim at learning a weight for each view to determine its contribution to the final solution. However, the view-weighted scheme can only indicate the overall importance of a view, which fails to recognize the importance of each inner cluster of a view. A view with higher weight cannot guarantee all clusters in this view have higher importance than them in other views. In this paper, we propose a cluster-weighted kernel k-means method for multi-view clustering. Each inner cluster of each view is assigned a weight, which is learned based on the intra-cluster similarity of the cluster compared with all its corresponding clusters in different views, to make the cluster with higher intra-cluster similarity have a higher weight among the corresponding clusters. The cluster labels are learned simultaneously with the cluster weights in an alternative updating way, by minimizing the weighted sum-of-squared errors of the kernel k-means. Compared with the view-weighted scheme, the cluster-weighted scheme enhances the interpretability for the clustering results. Experimental results on both synthetic and real data sets demonstrate the effectiveness of the proposed method.

Automatic disease diagnosis is, in essence, a classification problem where the classifier has to be trained based on patients’ datasets and not entirely on doctors’ expert knowledge. In this paper, we present the design of such data-driven disease classifiers and fine-tuning classifier performance by a multi-objective evolutionary algorithm. We have used sequential minimal optimization (SMO) classifier as the base classifier and three evolutionary algorithms namely Cat Swarm Optimization (CSO), Invasive Weed Optimization (IWO) and Eagle Search based Invasive Weed Optimization (ESIWO) to diagnose disease from datasets available. In that sense, our approach is an offline data-driven approach with 18 benchmark medical datasets, and the obtained results demonstrate the superiority of the proposed diagnoses in terms of multiple objectives such as classification Prediction accuracy, Sensitivity, and Specificity. Relevant statistical tests have been carried out to substantiate the cogence of the obtained results.

Intrusion is one of the biggest problems in wireless sensor networks. Because of the evolution in wired and wireless mechanization, various archetypes are used for communication. But security is the major concern as networks are more prone to intrusions. An intrusion can be dealt in two ways: either by detecting an intrusion in a wireless sensor network or by preventing an intrusion in a wireless sensor network. Many researchers are working on detecting intrusions and less emphasis is given on intrusion prevention. One of the modern techniques for averting intrusions is through fuzzy logic. In this paper, we have defined a fuzzy rule-based system to avert intrusions in wireless sensor network. The proposed system works in three phases: feature extraction, membership value computation and fuzzified rule applicator. The proposed method revolves around predicting nodes in three categories as “red”, “orange” and “green”. “Red” represents that the node is malicious and prevents it from entering the network. “Orange” represents that the node “might be malicious” and marks it suspicious. “Green” represents that the node is not malicious and it is safe to enter the network. The parameters for the proposed FzMAI are packet send to base station, energy consumption, signal strength, a packet received and PDR. Evaluation results show an accuracy of 98.29% for the proposed system. A detailed comparative analysis concludes that the proposed system outperforms all the other considered fuzzy rule-based systems. The advantage of the proposed system is that it prevents a malicious node from entering the system, thus averting intrusion.

During the past decade, the permanent magnet synchronous machines have been becoming more and more prevailing in industrial applications. Substantial optimization approaches are employed to improve the performances of the machine drives. It is well known that artificial intelligence techniques bring competitive solutions to machine design and control problems. In this paper, we provide a survey on the state-of-the-art control and optimization methods used in the permanent magnet synchronous machine drives. A large variety of control schemes and up-to-date optimization methods are reviewed here.

The paper aims at establishing the output-to-input relationship of the real-life adult depression data using a neural network (NN) model. The said model has been developed to diagnose and detect the associated severity (grade) of the illness. An intelligent NN-based reverse model has been trained through batch mode and put to test on another set of real-life data. Reverse mapping of this model has been developed to isolate significantly contributing input components (factors) for any given case to expedite the preventive procedure for further deterioration as well as the start of treatment.

Electricity consumption prediction is an important but demanding issue in the study of power systems. It is difficult for the conventional prediction methods, such as linear models, to utilize relevant domain knowledge in the forecasting of power peaks. In this paper, we propose an approach merging a regression predictor and a peak compensator together. The latter is designed to compensate for the prediction errors related to power peaks caused by the former. The proposed hybrid short-term prediction scheme has been demonstrated in a real-world case study to efficiently yield performances moderately better than the standalone regression predictors.

The Global-best Harmony Search (GHS) is a stochastic optimisation algorithm recently developed, which hybridises the Harmony Search (HS) method with the concept of swarm intelligence in the particle swarm optimisation (PSO) to enhance its performance. In this article, a new optimisation algorithm called GHSACO is developed by incorporating the GHS with the Ant Colony Optimisation algorithm (ACO). Our method introduces a novel improvisation process, which is different from that of the GHS in the following aspects. (i) A modified harmony memory (HM) representation and conception. (ii) The use of a global random switching mechanism to monitor the choice between the ACO and GHS. (iii) An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The proposed GHSACO algorithm has been applied to various benchmark functions and constrained optimisation problems. Simulation results demonstrate that it can find significantly better solutions when compared with the original HS and some of its variants.

A new bio-inspired algorithm, namely Bird Swarm Algorithm (BSA), is proposed for solving optimisation applications. BSA is based on the swarm intelligence extracted from the social behaviours and social interactions in bird swarms. Birds mainly have three kinds of behaviours: foraging behaviour, vigilance behaviour and flight behaviour. Birds may forage for food and escape from the predators by the social interactions to obtain a high chance of survival. By modelling these social behaviours, social interactions and the related swarm intelligence, four search strategies associated with five simplified rules are formulated in BSA. Simulations and comparisons based on eighteen benchmark problems demonstrate the effectiveness, superiority and stability of BSA. Some proposals for future research about BSA are also discussed.

Under mild conditions, the Pareto front (Pareto set) of a continuous m-objective optimization problem forms an (m - 1)-dimensional piecewise continuous manifold. Based on this property, this paper proposes a self-organizing multiobjective evolutionary algorithm. At each generation, a self-organizing mapping method with (m - 1) latent variables is applied to establish the neighborhood relationship among current solutions. A solution is only allowed to mate with its neighboring solutions to generate a new solution. To reduce the computational overhead, the self-organizing training step and the evolution step are conducted in an alternative manner. In other words, the self-organizing training is performed only one single step at each generation. The proposed algorithm has been applied to a number of test instances and compared with some state-of-the-art multiobjective evolutionary methods. The results have demonstrated its advantages over other approaches.

Segmentation of an image is the partition or separation of the image into disjoint regions of related features. In clinical practice, magnetic resonance imaging (MRI) is used to differentiate pathologic tissues from normal tissues, especially for brain tumors. The main objective of this paper is to develop a system that can follow a medical technician way of work, considering his experience and knowledge. In this paper, a step by step methodology for the automatic MRI brain tumor segmentation and classification is presented. Initially acquired MRI brain images are preprocessed by the Gaussian filter. After preprocessing, initial segmentation is done by hierarchical topology preserving map (HTPM). From the resultant images, the features are extracted using gray level co-occurrence matrix (GLCM) method, and the same are given as inputs to adaptive neuro fuzzy inference systems (ANFIS) for final segmentation and the classification of brain images into normal or abnormal. In case of abnormal, the MRI brain images are classified as benign subject (tumor without cancerous tissues) or malignant subject (tumor with cancerous tissues). Based on the analysis, it has been discovered that the overall accuracy of classification of our method is above 94%, and F1-score is about 1. The simulation results also show that the proposed approach is a valuable diagnosing technique for the physicians and radiologists to detect the brain tumors.

In this paper, we propose a clustering based multiobjective evolutionary algorithm (CLUMOEA) to deal with the multiobjective optimization problems with irregular Pareto front shapes. CLUMOEA uses a k-means clustering method to discover the population structure by partitioning the solutions into several clusters, and it only allows the solutions in the same cluster to do the reproduction. To reduce the computational cost and balance the exploration and exploitation, the clustering process and evolutionary process are integrated together and they are performed simultaneously. In addition to the clustering, CLUMOEA also uses a distance tournament selection to choose the more similar mating solutions to accelerate the convergence. Besides, a cosine nondominated selection method considering the location and distance information of the solutions are further presented to construct the final population with good diversity. The experimental results show that, compared with some state-of-the-art algorithms, CLUMOEA has significant advantages on dealing with the given test problems with irregular Pareto front shapes.

The differential evolution (DE) and harmony search (HS) are two well-known nature-inspired computing techniques. Both of them can be applied to effectively cope with nonlinear optimization problems. In this paper, we propose and study a new DE method, DE–HS, by utilizing the fresh individual generation mechanism of the HS. The HS-based approach can enhance the local search capability of the original DE. Optimization of some unconstrained and constrained benchmark problems and a real-world wind generator demonstrate that our DE–HS has an improved convergence property.

The Harmony Search (HS) method is an emerging meta- heuristic optimization algorithm. However, it is generally not so efficient in handling multi-modal optimization problems, in which instead of only a single optimum, multiple optima need to be found. In our paper, a novel HS method based on the niching technique (deterministic crowding), n-HS, is presented to overcome this shortcoming. The n-HS is further applied to deal with the multi-modal optimal design of a wind generator.

The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm, which has been widely employed to deal with various optimization problems during the past decade. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and even fails to find the global optima in an efficient way. In this paper, a new HS method with dual memory, namely DUAL-HS, is proposed and studied. The secondary memory in the DUAL-HS takes advantage of the Opposition-Based Learning (OBL) to evolve so that the quality of all the harmony memory members can be significantly improved. Optimization of 25 typical benchmark functions demonstrate that compared with the regular HS method, our DUAL-HS has an enhanced convergence property.

The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm, which has been extensively applied to handle numerous optimization problems during the past decade. However, it usually lacks of an efficient local search capability, and may sometimes suffer from weak convergence. In this paper, a memetic HS method, m-HS, with local search function is proposed and studied. The local search in the m-HS is inspired by the principle of bee foraging, and performs only at selected harmony memory members, which can significantly improve the efficiency of the overall search procedure. Compared with the original HS method, our m-HS has been demonstrated in numerical simulations of 16 typical benchmark functions to yield a superior optimization performance.

In Epilepsy EEG signal classification, the main time-frequency features can be extracted by using sparse representation with marching pursuit (MP) algorithm. However, the computational burden is so heavy that it is almost impossible to apply MP to real time signal processing. To reduce complexity of sparse representation, we propose to adopt harmony search method in searching the best atoms. Because harmony search method can find the best atoms in continuous time-frequency dictionary, the performance of epilepsy EEG signal classification is enhanced. The validity of this method is proved by experimental results.

Memetic algorithms (MAs) with multiple Local Search Strategies (LSSs) for the mixed H∞/H2robust control design is proposed and investigated in this paper. Multiple LSSs are introduced into a given evolutionary computation leading to a new memetic algorithm. The correcting memes, directed memes, and stochastic memes are used to form the meme pool for iterative search, by which the multiple LSSs can be combined with Multi-Objective Evolutionary Algorithms (MOEAs) together. The new algorithm is applied in Multi-objective Robust Control Design (MRCD), which is capable of both moving toward and along the Pareto can yield a better performance for these two norms. Finally, the result of the proposed memetic algorithm is compared with the numerical solution of the convex approximations in terms of the Linear Matrix Inequalities (LMIs).

In this paper, a novel type of fuzzy input-output model (FIOM) is presented. Using this kind of fuzzy model, it is convenient for us to focus on the input-output properties of the nonlinear systems to be controlled. The FIOM is compared with the general T-S fuzzy model. Moreover, the L∞gain of the nonlinear operator achieved by this kind of fuzzy input-output model is investigated, and the L∞stability of the feedback system constructed by two such systems is examined based on the well known small gain theorem.

In this paper, on the basis of the off-axis circle criterion, a sufficient condition with a simple graphical explanation is derived to guarantee the global asymptotic stability for a type of multi-rule proportional Takagi-Sugeno (T-S) fuzzy control systems. Three numerical examples are given to demonstrate how to use the proposed method in analyzing the T-S fuzzy control systems.

The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. In this paper, we propose two modified HS methods to deal with the uni-modal and multi-modal optimization problems. The first modified HS method is based on the fusion of the HS and differential evolution (DE) technique, namely, HS-DE. The DE is employed here to optimize the members of the HS memory. The second modified HS method utilizes a novel HS memory management approach, and it targets at handling the multi-modal problems. Several nonlinear functions are used to demonstrate and verify the effectiveness of our two new HS methods.

In this paper, based on the fusion of the clonal selection algorithm (CSA) and differential evolution (DE) method, we propose a novel optimization scheme: CSA-DE. The DE is employed here to increase the affinities of the clones of the antibodies (Abs) in the CSA. Several nonlinear functions are used to verify and demonstrate the effectiveness of this hybrid optimization approach. It is further applied for the construction of the cascade-correlation (C-C) neural network, in which the optimal hidden nodes can be obtained.

This paper presents a hybrid optimization method based on the fusion of the clonal selection algorithm (CSA) and harmony search (HS) technique. The CSA is employed to improve the members of the harmony memory in the HS method. The hybrid optimization algorithm is further used to optimize a fuzzy classification system for the Fisher Iris data classification. Computer simulations results demonstrate the effectiveness of our new approach.

This paper proposes a particle swarm optimization (PSO)-based detector optimization scheme in the negative selection algorithm (NSA). The NSA is a natural immune response inspired pattern discrimination method. In the new scheme, the NSA detectors are optimized by the PSO to collectively occupy the maximal coverage of the nonself space so that they can achieve the best anomaly detection performance. Two numerical examples including the discriminant analysis of Fisher's iris data are demonstrated to verify the effectiveness of our approach.

The performance of evolutionary algorithms in optimization is tightly coupled to the computational effort required by the evaluation of the objective function. If the objective function is too expensive to evaluate, then, the elaboration of the procedures of the search algorithm alone may not result in the required improvement in algorithm’s performance. However, if there is a way to speed up or decrease the number of objective function evaluations, even a basic algorithms can potentially achieve better results due to the increased number of generation run in given time. This paper considers a probabilistic objective function evaluation scheme in which the candidate solutions are evaluated and evolved based on their objective function value.

This paper proposes a hybrid optimization method based on the ant colony and clonal selection algorithms, in which the cloning and mutation operations are embedded in the ant colony to enhance its search capability. The novel algorithm is employed to deal with a few benchmark optimization problems under both static and dynamic environments. Simulation results demonstrate the remarkable advantages of our approach in diverse optimal solutions, closely tracking varying optimum, as well as improved convergence speed.

In this paper, a sufficient condition is derived to guarantee the globally asymptotic stability of the simplest Takagi-Sugeno (T-S) fuzzy control system based on the circle criterion. Two numerical examples are given to demonstrate how to use this condition in analyzing the T-S fuzzy control systems. Performance comparisons are also made between the simplest T-S fuzzy controller and linear compensators.

In this paper, we employ the clonal optimization method to optimize the detectors in the negative selection algorithm (NSA). Taking advantage of the clonal optimization strategy, the NSA detectors can be optimized for anomaly detection. A new motor fault detection scheme using our NSA is also discussed. We demonstrate the efficiency of the proposed approach with an example of bearings fault detection.

This paper proposes a hybrid particle swarm optimization (PSO) method, which is based on the fusion of the PSO, clonal selection algorithm (CSA), and mind evolutionary computation (MEC). The clone function borrowed from the CSA and MEC-characterized similartaxis and dissimilation operations are embedded in the original PSO. Simulations of nonlinear function optimization are made to compare this hybrid PSO with the regular PSO. It has been demonstrated that our hybrid algorithm can achieve a better convergence performance, and provide diverse solutions to multi-model optimization problems.

In this paper, a new evolutionary optimization algorithm named Scored Pareto Mind Evolutionary Computation (SP-MEC) is proposed, which embeds the theory of Pareto and information of density into the Mind Evolutionary Computation (MEC) in order to deal with multi-objective optimization problems. Taking advantage of two unique operations, similartaxis and dissimilation, the MEC is an efficient optimization algorithm combining the global search with local search. Thus SP-MEC can further effectively converge to the Pareto front, and achieve the high-quality trade-off front for multi-objective optimization. The features of the proposed SP-MEC are the employments of the relation of Pareto and density information of individuals. Therefore, the optimal solutions acquired by our SP-MEC distribute uniformly on the Pareto front. The feasibility and efficiency of this SP-MEC are demonstrated using numerical examples. The convergence of the sequence of populations generated from the similartaxis operation is also analyzed under certain conditions.

In this paper, we first propose a linguistic information feedforward-based dynamical fuzzy system (LIFFDFS) in which the past fuzzy inference output represented by a membership function is fed forward locally with trainable parameters. Our LIFFDFS can overcome the common static mapping drawback of conventional fuzzy systems. We also give a detailed description of its underlying principle and general structure. Next, based on the gradient descent method, an adaptive learning algorithm for the feedforward parameters is derived. The proposed LIFFDFS is further employed in the prediction of time series. The well-known Box-Jenkins gas furnace data are used here as an evaluation example. Simulation results demonstrate that this new dynamical fuzzy system has the advantage of inherent dynamics and is, therefore, well suited for handling temporal problems, such as process modeling and control.

In our paper, the analytical structures and properties of the one and 2D fuzzy controllers are first investigated in details. The nature of these two kinds of fuzzy controllers is next probed from the viewpoint of control engineering. For the one dimensional fuzzy controller, it is concluded that this controller is a combination of a saturation element and a nonlinear proportional controller, and the system that employs the one dimensional fuzzy controller is the combination of an open-loop control system and a closed-loop control system. On the other hand, the 2D controller is a hybrid controller, which comprises the saturation part, zero-output part, nonlinear derivative part, nonlinear proportional part, as well as nonlinear proportional-derivative part, and the 2D fuzzy controller-based control system is a loop-varying system with varying number of control loops

We explore the feasibility of using both feedforward and Elman neural networks to detect assembly faults in ink jet printers. The method is an extension of the motor fault detection scheme proposed by Gao and Ovaska (2002). Two types of cartridge faults are studied here: encoder belt misalignment and encoder strip error. These two faults are detected from the characteristics variants in the neural networks-based prediction of cartridge velocity signals. Results of experiments with real-world data demonstrate that neural networks can be trained to effectively detect the inherent encoder faults. Some discussions on the selection of appropriate fault detection criteria are also given.

Prediction of sinusoidal signals with time. varying frequencies has been an important research topic in power electronics systems. To solve this problem, we propose a new fuzzy predictive filtering scheme, which is based on a Finite Impulse Response (FIR) filter bank. Fuzzy logic is introduced here to provide appropriate interpolation of individual filter outputs. Therefore, instead of regular 'hard' switching, our method has the advantageous 'soft' switching among different filters. Simulation comparisons between the fuzzy predictive filtering and conventional filter bank-based approach are made to demonstrate that the new scheme can achieve an enhanced prediction performance for slowly changing sinusoidal input signals.

This paper compares the performance of nonlinear radial basis function network-based (RBFN) and linear autoregressive (AR) model-based general parameter (GP) methods in a fault detection application. We use the efficient GP approach for initializing the weights of the RBFN model in the beginning of the off-line system identification phase, as well as for fine-tuning the modeling accuracy of RBFN and AR models online. Our fault detection scheme is based on monitoring the expectation value of the scalar general parameter. This provides improved robustness and detection sensitivity over such methods where the online prediction error is used directly in the decision-making process. In order to illustrate the performance of the efficient nonlinear and linear schemes, they are applied to fault detection of automobile transmission gears. As the acoustic sound level time-series, providing the necessary basis information for fault detection, is slightly nonlinear, the GP-RBFN outperformed the linear methods: the GP-AR method and conventional AR inverse filtering. Both of the GP-based methods provide competitive alternatives for real-world fault detection and diagnosis.

Soft computing (SC) is an evolving collection of methodologies, which aims to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low total cost. It differs from conventional hard computing in that it is strongly based on intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent the ambiguity in human thinking with real life uncertainty. Fuzzy logic (FL), neural networks (NN), and evolutionary computation (EC) are the core methodologies of soft computing. However, FL, NN, and EC should not be viewed as competing with each other, but synergistic and complementary instead. In this paper, we discuss the role of soft computing in developing innovative features for home appliances. Such development work is particularly active in Japan and South Korea. In those Asian countries even ordinary consumers do appreciate the capabilities of SC in developing user-friendly products with high machine IQ. Although home appliance technology is not a popular academic research area, it is an important application area for soft computing methods.

In this paper, we propose a fuzzy logic-based power command enhancement scheme for the mobile station in the cellular communications systems. By defining necessary linguistic terms of the power commands and corresponding fuzzy inference rules, an Embedded Fuzzy Unit (EFU) is constructed. The role of our EFU is to produce appropriate digital power commands instead of the one-bit discrete commands directly obtained from the base station. Simulations show that the EFU-based mobile station can generate considerably smoother received power at the base station. The transient overshoot and steady channel tracking error are small. Moreover, our method has the advantage of simplicity in both structure and algorithm, and is thus easy for hardware implementation.

During recent years, fuzzy neural networks have found extensive applications in numerous engineering areas. It is known that the fusion of neural networks and fuzzy logic can overcome their individual drawbacks and benefit from each other's merits. However, current fuzzy neural networks often have complex structures and training algorithms. In addition, some of them cannot deal with fuzzy knowledge directly. Inspired by the alpha-level cut representation of fuzzy numbers, we propose a simple neural network-based approach for processing fuzzy information. By numerical simulations, our scheme is illustrated to be capable of coping with fuzzy input and output without a need for new network topology or learning algorithm

Fault detection methods are crucial in acquiring safe and reliable operation in motor drive systems. Remarkable maintenance costs can also be saved by applying advanced detection techniques to find potential failures. However, conventional motor fault detection approaches often have to work with explicit motor models. In addition, most of them are deterministic or non-adaptive, and therefore cannot be used in time-varying cases. We propose an Elman neural network-based motor fault detection scheme to overcome these difficulties. The Elman neural network has the unique time series prediction capability because of its memory nodes as well as local recurrent connections. Motor faults are detected from changes in the expectation of the feature signal prediction error. A genetic algorithm (GA)-aided training strategy for the Elman neural network is further introduced to improve the approximation accuracy and achieve better detection performance. Computer simulations of a practical automobile transmission gear with an artificial fault are carried out to verify the effectiveness of our method. Encouraging fault detection results have been obtained without any prior information of the gear model.

We give a comparison between the conventional power control scheme and a soft computing-based approach in a mobile communications application. At the base station, the 'bang-bang' control strategy and a neural network-based prediction control method are employed respectively. In addition, full power command, single-bit command transmission mode, and fuzzy logic-based power command enhancement unit, are considered. Based on numerical simulation experiments, we quantitatively evaluate the performance of various combinations of these control methods and command transmission modes. Finally, conclusions on the optimal configuration are given.

Dealing with impulsive noise remains one of the great engineering challenges due to the difficulties in modeling and filtering impulsive noise. Since impulsive noise is, in general, a non-stationary stochastic process, neural networks and fuzzy systems have been applied to deal with it. In many cases, however, the underlying primary signal is smoothly changing. Therefore, a more simple approach, namely polynomial predictive filtering, can successfully be applied instead of soft computing methods. In this paper, we present a filtering system which consists of a cascade of a median filter and a polynomial predictive filter. The proposed system is capable of removing the strong impulses that are present in the polynomial-like input signal without introducing a disturbing delay. The simulation results are compared with the corresponding results obtained by applying fuzzy filtering. Finally, we consider an interesting concept called smoothness of a signal; we will show that the choice of feasible filtering approach and its parameters is dependent on the degree of smoothness of the signal to be filtered.

Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.