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Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing. Specifically, correct and deep understanding in test cases of manufactured devices is key to enable post-silicon tuning and debugging. This analysis is typically performed by experienced human experts. However, with the fast development in semiconductor industry, test cases can contain hundreds of variables. The resulting high-dimensionality poses enormous challenges to experts. Thereby, some recent prior works have introduced data-driven variable selection algorithms to tackle these problems and achieved notable success. Nevertheless, for these methods, experts are not involved in training and inference phases, which may lead to bias and inaccuracy due to the lack of prior knowledge. Hence, this letter for the first time aims to design a novel conditional variable selection approach while keeping experts in the loop. In this way, we expect that our algorithm can be more efficiently and effectively trained to identify the most critical variables under certain expert knowledge. Extensive experiments on both synthetic and real-world datasets from industry have been conducted and shown the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The module with multijunction solar cells (MJSC) is an excellent solution for converting solar radiation into electrical energy. Several methods are applied to extract the parameters of the multijunction solar cell. Most of them are analytical and numerical. Metaheuristic algorithms have lately been used for the parameters’ extraction. In specialized literature, only a few multijunction solar cells have been performed to extract the parameters. Therefore, two metaheuristic algorithms are proposed in this paper: the Chameleon Swarm Algorithm (CSA) and the Black Widow Optimization Algorithm (BWOA). The first algorithm (CSA) is applied for the first time to estimate the solar cell and panel parameters, and both algorithms (CSA & BWOA) are employed for the first time for the multijunction solar cell. They are applied considering two models: the single diode model (SDM) and the double diode model (DDM) for the multijunction solar cell, and three work temperatures of the multijunction solar cell. Four statistical tests are used to analyze the performance of the algorithms, the main being root mean square error (RMSE). A comparative study was performed using the other analytical and metaheuristic algorithm. The obtained RMSE is 8.9120260701E-5 for BWOA and 8.9123932518E-5 for CSA model, respectively, in the case of SDM model and 41.5°C, 1.0186359636E-4 for BWOA and 1.0088314434E-4 for CSA model, respectively, in the case of DDM model and 41.5°C. In the case of the solar panel the RMSE is 3.62E-3 for BWOA, 3.6250156794E-3 for CSA model, respectively, in the case of SDM model and 25°C. The best root mean square error results are obtained using the Black Widow Optimization Algorithm for the single-diode model. The lowest value for root mean square error is 8.9120260701E-5. The special feature and merits of the proposed algorithms are that they have better exploration and exploitation ability; thus, they provide the optimal results with reduced computational time. Further, the performance of the two algorithms (BWOA andCSA) is validated using the dataset of the CTJ 30 panel. The BWOA algorithm has a root mean square error that is two times lower than the one in the research literature. The computational time is also calculated. It is around 2 s, which is very competitive for all considered cases. CSA has the lowest computing time for all four cases considered, varying from 1.882829 s to 2.277469 s. Furthermore, variation in the temperature function is studied using the extracted parameters.

Machine learning (ML) has been instrumental in solving complex problems and significantly advancing different areas of our lives. Decision tree-based methods have gained significant popularity among the diverse range of ML algorithms due to their simplicity and interpretability. This paper presents a comprehensive overview of decision trees, including the core concepts, algorithms, applications, their early development to the recent high-performing ensemble algorithms and their mathematical and algorithmic representations, which are lacking in the literature and will be beneficial to ML researchers and industry experts. Some of the algorithms include classification and regression tree (CART), Iterative Dichotomiser 3 (ID3), C4.5, C5.0, Chi-squared Automatic Interaction Detection (CHAID), conditional inference trees, and other tree-based ensemble algorithms, such as random forest, gradient-boosted decision trees, and rotation forest. Their utilisation in recent literature is also discussed, focusing on applications in medical diagnosis and fraud detection.

The selection of an optimal investment sector is of critical importance not only for individual financial success but also to drive economic development. The allocation of capital into sectors with high potential for growth, innovation, and job creation is key. In addressing the complexity of decision-making scenarios associated with investment sector exploration, we introduce a novel data structure known as Picture fuzzy hypersoft set ( $\mathbb {PF}_{\mathbb {HSS}s}$ ). This specialized approach within computational intelligence and decision-making aims to categorize data into various attributes and sub-attributes, considering the significant role of neutrality. The study stems from the need for a comprehensive framework ( $\mathbb {PF}{\mathbb {HSS}s}$ ) that can effectively handle intricate decision-making scenarios involving attributes, subattributes, and nuanced factors such as neutrality. Traditional tools such as TOPSIS and its extensions of fuzzy sets, while robust in Multiple Criteria Decision Making (MCDM), may face challenges in modeling and analyzing decision-making information within a $\mathbb {PF}{\mathbb {HSS}s}$ environment. The rationale behind this study lies in enhancing the accuracy and efficiency of decision-making processes when dealing with complex, fuzzy, and multi-criteria data. By introducing newly proposed distances and similarity measures tailored to $\mathbb {PF}{\mathbb {HSS}s}$ , and constructing a $\mathbb {PF}{\mathbb {HSS}s}$ -TOPSIS method, we aim to address the limitations faced by existing models in the $\mathbb {PF}_{\mathbb {HSS}s}$ environment. The application of Hamming distance-based similarity measures further distinguishes our method by determining the weights assigned to each decision maker. The proposed $\mathbb {PF}_{\mathbb {HSS}s}$ -TOPSIS method is practically applied in designing an optimal investment sector exploration tool for investors. This method has the potential to establish a crucial connection between alternatives and attributes, providing value across various fields and industries. The research emphasizes bridging the gap in decision-making scenarios where alternatives and attributes need to be effectively connected and analyzed, thereby contributing to the advancement of decision-making processes in complex domains.

Due to the potential for high energy harvesting capacity, subwavelength scale semiconductor nanostructured arrays are used to address the issue of single-junction thin-film solar cells' limited solar energy harvesting. Along with numerical simulations, an effective and efficient algorithm is crucial to maximizing the optical field modulation and energy trapping capacity of nanostructures. Based on the effective medium theory and the leaky mode resonance, an analytical feedback algorithm is suggested in this study to determine the precise the dimensions of vertically aligned InP stepped nanocylinders (SNCs) for maximum solar energy absorption. For both square and hexagonally arranged two-segment or three-segment InP SNC arrays, the ideal geometrical dimensions were quantitatively estimated for maximum energy harvesting. Densities of short-circuit current Jscs under the AM 1.5G spectrum's illumination as the measurement standard, they were computed for each SNC array. The maximal Jsc of 32.85 mA/cm2 was obtained with square three-segment InP SNC arrays. The optimized SNC arrays for the maximum light absorption are also validated and examined using thorough finite-difference time-domain computational simulations. The algorithm estimated maximum Jsc had tolerances of under 1.8% for all scenarios, which, when compared to simulations, shows that this analytical method offers a practical and efficient means to direct the design of high-performance InP SNC arrays solar cells.

Single-image super-resolution (SISR) techniques have found wide applications in semiconductor defect inspection. Enhancing image resolution to improve inspection sensitivity and accuracy holds great significance. A novel SISR algorithm, called cross-convolutional residual network (CCRN), is proposed in this study. CCRN comprises a cross-convolutional module (CCM), which incorporates a cross-sharing mechanism that facilitates the fusion of features from different stages, enabling the extraction of more information from the image. Moreover, a global residual aggregation structure (GRA) is introduced. GRA captures and transfers different levels of residual features acquired from learning each CCM to the reconstruction layer. Experimental results demonstrate that the proposed SR algorithm outperforms existing state-of-the-art SR algorithms in terms of both visual and quantitative metrics when applied to optical, SEM, and TEM images of microfluidic chips, CMOS image sensors, and quantum dots, respectively. Additionally, CCRN significantly improves the accuracy of defect classification and inspection of unpatterned wafers, as evaluated using the WM-811K dataset. Notably, an increase in local defection testing accuracy from 79.00% to 89.00% and an improvement in classification accuracy from 93.69% to 96.06% are achieved. These findings underscore the potential applications of the proposed algorithm in improving semiconductor defect inspection and classification accuracies. [ABSTRACT FROM AUTHOR]

Nowadays, the growth of multimedia content over the web is exponential. The fingerprints are inconspicuously embedded in multimedia content. The fingerprints can be exploited to trace divergent information from multimedia resources. Sampling fingerprints, particularly from multimedia resources, is challenging since they are complex, heterogeneous, and diverse. This research proposed an approach to sample fingerprints from multimedia resources. Our approach partitions the multimedia content space into converged clusters using variations of Canberra distance and identifies the most diverged samples using Kullback-Leibler (KL) divergence. The resultant clusters represent the information belonging to particular concepts and the diverged samples within the clusters represent multimedia fingerprints. The fingerprint sampling process is leveraged using unsupervised learning algorithms, instantiated across various multimedia descriptors, and tested over standard multimedia datasets. The average results obtained over various standard visual and acoustic datasets reveal 80%, 77%, and 78% accuracy, precision, and recall, respectively, surpassing most of the existing baseline clustering methods such as K-Means, Mean-Shift, and DBSCAN. Furthermore, the rigorousness of the proposed algorithm clustering is evaluated using the internal clustering stability silhouette coefficient and the fingerprint diversity scores. The results unveil a maximum of 94% diversity score. The proposed variation of Canberra distance and KL divergence provides the most stable performance (SD=0.02) and creates promising implications in future multimedia retrieval, summarization, and exploration activities.

Flux switching machines play a crucial role in emerging sectors such as renewable energy, electric vehicles, and the aerospace industry. Recent advancements have underscored their potential for achieving high torque density, effective heat management, and flux-weakening capabilities. This paper provides a comprehensive review of the design optimization of these machines, offering a unique contribution, as no previous attempts have delved into a detailed exploration of various optimization techniques specific to this technology. Special attention is given to state-of-the-art technologies related to multi-objective optimization and the performance analysis of the three variants: permanent magnet, wound-field, and hybrid excited. The review highlights and compares several optimization strategies from recent research, revealing research gaps in integrating advanced control algorithms, thermal, vibroacoustic, and structural models into the optimization design process of these machines. Furthermore, this review identifies essential trends in optimization development, offering valuable insights and future perspectives for these machines across diverse applications.

The integration of advanced technologies such as artificial intelligence, the Internet of Things, and location-tracking technologies has revolutionized our daily lives by enhancing the functionality of logistical services. However, these advancements have also placed significant pressure on the current supply networks and have impeded further growth within the logistics industry. This article presents an inventive logistic supply chain solution utilizing drone-based delivery to tackle the logistic supply chain issues. The proposed solution goal is to minimize the maximum required time to finish all assigned delivery tasks. A set of sophisticated algorithms was developed based on the combination of dispatching rule, randomization, and iterative methods to optimize the performance of the supply chain processes and effectively accomplish the aim of time savings. The deployed system enables the management of a larger volume of shipments and increases the efficiency of logistic supply chain management. The experimental results demonstrate that the proposed algorithms can decrease the maximum duration required to complete the delivery operations assigned to the drones in 1520 cases. The algorithm that exhibited the highest level of performance was the $MID$ algorithm, which attained a staggering percentage of 87.7%, an average deviation from the optimal solution of 0.001, and an execution time of 0.0215 s. Moreover, after comparing their outputs, the $MID$ algorithm was determined to outperform the top two algorithms in the literature.

Satellite images play a crucial role in ecology as they provide rich information about the Earth’s surface. The deep analysis of satellite images presents a vast challenge due to the sheer size of the data that needs to be managed. Sophisticated storage solutions are required to handle the ever-increasing velocity of incoming data and to deal with potential latency or data loss. Storage balancing ensures efficient allocation and distribution of storage capacity across a system, which involves monitoring, analyzing, and adjusting how data is stored to optimize performance, minimize downtime, and maximize cost savings. Additionally, storage balancing helps avoid data bottlenecks by automatically redistributing data across multiple resources. While many solutions have been proposed to balance storage, no polynomial solution is available. This paper addresses the issue of transmitting a considerable amount of satellite images across the network to various storage supports. The challenge is to find an effective way to schedule these satellite images to the storage supports that lead to equitable results in distribution. Many heuristics and enhancement methods are proposed to solve this problem. The effectiveness of the algorithms presented in this paper was tested and analyzed through extensive testing. The experimental study shows that the proposed heuristics outperform those developed in the literature. Indeed, in 73.8% of cases, the best-proposed algorithm, the best iterative-selection satellite images algorithm ( $BIS$ ), reached the best solution compared to the best algorithm in the literature and the other proposed algorithms. The $BIS$ algorithm obtained an average gap of 0.147 in an average running time of 1.0654 s.

Apache Kafka has become a popular solution for managing networked data streaming in a variety of applications, from industrial to general purpose. This paper systematically surveys the research literature in this field by carefully classifying it into key macro areas, namely algorithms, networks, data, cyber-physical systems, and security. Through this meticulous classification, the paper aims to identify and analyze the optimization aspects relevant to each area, drawing upon practical applications as the basis for analysis. In this respect, the paper synthesizes and consolidates existing knowledge, saving researchers valuable time and effort in searching for relevant information across multiple sources. The tangible benefits of this survey paper include providing a consolidated knowledge base about research-intensive Apache Kafka topics, highlighting practical insights and novel approaches, pointing up cross-domain applications, identifying related research challenges, and serving as a trusted reference for the Apache Kafka community.

This study analyzed three main sources of error in bladder volume calculation by traditional ultrasound bladder volume formula, which is most commonly used in clinic and built into most ultrasonic diagnostic apparatus. In order to solve these errors, we changed the traditional bladder volume formula to calculate bladder volume based on bladder axis to calculate volume based on bladder Section area, and corrected this formula according to actual clinical data to obtain a new formula, which is called the modified ellipsoidal trend formula. This formula can easily calculate the bladder volume manually during ultrasound examination of the bladder. Based on this formula, OpenCV function is applied to build a program for calculating bladder volume through two ultrasound images of bladder. The method is to calculate the total number of pixels in the contour of the bladder image by the program, and convert the number of pixels to area, and calculate the bladder volume using the modified ellipsoidal trend formula. This algorithm can be implanted into Ultrasonic diagnostic apparatus or ultrasound workstation to replace the traditional bladder volume formula. In this study, 122 ultrasound cases were used to test the accuracy of the program, and the results showed that the program was more accurate than the traditional bladder volume formula.

In this work, we introduce an open-source suite of quantum application-oriented performance benchmarks that is designed to measure the effectiveness of quantum computing hardware at executing quantum applications. These benchmarks probe a quantum computer's performance on various algorithms and small applications as the problem size is varied, by mapping out the fidelity of the results as a function of circuit width and depth using the framework of volumetric benchmarking. In addition to estimating the fidelity of results generated by quantum execution, the suite is designed to benchmark certain aspects of the execution pipeline in order to provide end users with a practical measure of both the quality of and the time to solution. Our methodology is constructed to anticipate advances in quantum computing hardware that are likely to emerge in the next five years. This benchmarking suite is designed to be readily accessible to a broad audience of users and provides benchmarks that correspond to many well-known quantum computing algorithms.

The main purpose of logic optimization lies in architecting integrated switching circuits. It is thus a topic well covered in electrical engineering. Some subfields of the social sciences have also employed algorithms for logic optimization since the mid-1980s to infer about cause-effect relations. Most notably, political scientists and sociologists have developed Qualitative Comparative Analysis (QCA), a configurational comparative method (CCM) that relies on the well-known Quine-McCluskey algorithm. However, while electrical engineering has progressed considerably since the advent of logic optimization in the 1950s, these advancements have not been monitored by social scientists. Nor have social scientists sought to establish interdisciplinary collaboration. The objective of our article is twofold. First, and more generally, we seek to build a bridge between electrical engineering and configurational causal inference. Secondly, and more specifically, we present Combinational Regularity Analysis (CORA), a new CCM that has been inspired by electrical engineering. In particular, we introduce one of CORA’s algorithms for optimizing highly unspecified multi-value logic functions with multiple outputs. The availability of such algorithms in CORA pushes the boundaries of configurational causal inference and attests to the extent to which configurational comparative methodology could benefit from more interdisciplinary collaboration with electrical engineering.

One crucial step in several image processing and computer vision applications is Image Contrast Enhancement (ICE), whose main objective is to improve the quality of the information contained in the processed images. Most of the proposed schemes attack the problem by redistributing the pixel intensities in a histogram, leading to undesirable effects such as noise amplification, over-saturation, and lousy human perception. On the other hand, Agent-Based Models (ABM) are computational models that allow describing the behavior and interactions of autonomous agents when they operate cooperatively. These agents follow behavioral rules rather than mathematical formulations. This mechanism allows the implementation of complex behavioral patterns in agents through their interactions. This paper proposes a two-step method where pixels in the processed image are considered agents whose behavioral rules permit to enhance significatively the contrast. In our approach, the interactions among the agents are characterized by the differences in intensity values among the pixels or agents. In the first step, pixels or agents that present enough high differences in their intensity are modified to increase even more their differences. In the second step, pixels or agents that maintain a very small difference are altered to assume a homogeneous intensity value. The proposed approach has been tested considering different public datasets commonly used in the literature. Its results are also compared with those produced by other well-known ICE techniques. Evaluation of the experimental results demonstrates that the proposed approach highlights the important details of the image taking a lower computational execution time.

It is extremely common in engineering to design algorithms to perform various tasks. In data-driven decision making in any field one needs to ascertain the quality of an algorithm. Therefore, a robust assessment of algorithms is essential in deciding the best algorithm as well as in improving algorithms. To perform such an assessment objectively is obvious in the case of a single performance metric, but it is unclear in the case of multiple metrics. Nonetheless, $F_{1}$ measure is widely used in cases with two metrics; $F_{1}$ measure represents the harmonic mean (HM) of two metrics. Of course, there are other means, e.g., the arithmetic mean (AM) and the geometric mean (GM). As motivations for using them are intuitive and none of them are based on any objective function, it is difficult to judge objectively which is the best one. In this paper, the single metric case is examined to develop two objective functions that are applicable for any number of metrics. These two objective functions lead to two different performance measures - the distance from the origin (DO) and the distance from the ideal position (DIP). It introduces a new concept of the remaining phase space for the evaluation of the quality of a performance measure. On further and closer examinations of the original goal and the phase space of the metrics, amongst these five measures, either HM or DIP is found to be the best. Specifically, it is found that HM is the best measure at the lower performance end, while DIP is clearly the best measure at the higher performance end and is of much practical interest. Rules for deciding the best algorithm and the order of a set of algorithms are presented. These results are derived in the context of multiple independent and bounded metrics. Furthermore, several properties and detailed discussions are provided, following which some published results are reviewed in the present context to elucidate some points.

Redundant manipulators have been investigated and employed in various fields, and its trajectory tracking is of much importance in the field of robotic control. In this article, a model predictive control (MPC) scheme for the trajectory tracking of redundant manipulators is constructed, which minimizes the tracking error, velocity norm, and acceleration norm simultaneously. The commonly used trajectory tracking schemes for redundant manipulators, such as the minimum-velocity-norm scheme and minimum-acceleration-norm scheme, handle joint limits at different levels by introducing additional parameters, which reduces the feasible region of decision variables. In contrast, the proposed scheme directly considers these limits at three different levels, without reducing the feasible region of decision variables. In addition, to compensate for the deficiencies of most existing algorithms in noise environments, an error-summation enhanced Newton (ESEN) algorithm is proposed for solving the MPC scheme. Through theoretical analysis, it is determined that the proposed ESEN algorithm has a small steady-state error under noise conditions. Finally, in contrast with the comparative trajectory tracking schemes, as determined through computer simulations and experiments, the proposed MPC scheme solved by the ESEN algorithm not only enables the redundant manipulator to perform the trajectory tracking task in excellent fashion, but also offers advantages of high efficiency, fast responsiveness, and strong noise tolerance. [ABSTRACT FROM AUTHOR]

Sensors provide insights into the industrial processes, while misleading sensor outputs may result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian estimation algorithms are developed to estimate unforeseen signals in sensor outputs without tuning. The optimal Bayesian estimation method is first derived by incorporating a Gaussian distribution specifying potential unmodeled dynamics into the measurement equation. Since its performance depends on tuning parameters, an iterative Bayesian estimation algorithm is developed using the variational inference technique. Specifically, an inverse Wishart distribution is introduced to describe the predicted covariance of abnormal signals. We then estimate it together with the other independent Gaussian distributions to conditionally approximate the joint posterior distribution, by which the effects of tuning parameters can be replaced adaptively. Testing the proposed algorithms through a simulated electromechanical brake model and a real experimental system shows that the proposed algorithm can satisfactorily estimate additive sensor faults online and services as a sensor monitor that simultaneously provides the locations and magnitudes of faulty signals without tuning. [ABSTRACT FROM AUTHOR]

With the arrival of the current digital era and the advancement of information transmission technologies, there has been an unprecedented rise in data. Efficient extraction of useful information from the volumes of data has garnered growing interest from academics and the industry. Data mining research focuses on finding utility patterns in large datasets. But the inherent complications like frequent scans, creation of substantial candidate sets, etc. plague the mining process for large datasets. Distributive architecture-based approaches also prove inefficacious due to high communication overhead over iterations. High communication cost over data exchange both locally and remotely further aggravates the situation. We propose a Communication Cost Effective Utility-based Pattern Mining (CEUPM) algorithm based on the Spark framework to address this issue. Spark accelerates iterative scanning by storing scanned datasets in a memory abstraction called resilient distributed datasets (RDD). RDD operations need a redistribution of data among cluster nodes during processing. To minimize the communication cost incurred during the shuffle process, we adopt a search space division strategy based on data parallelism for a fair and effective task allocation across cluster nodes. Communication overhead is incurred during this redistribution or shuffle process while minimizing costs. Experimental results in four real datasets demonstrate that CEUPM considerably reduces shuffling overhead and outperforms other existing methods in terms of memory usage, communication cost, execution time, and scalability. [ABSTRACT FROM AUTHOR]

In this article, we consider a class of decentralized resource allocation problems over directed networks, where each node only communicates with its in-neighbors and attempts to minimize its own cost when network-wide resource constraints as well as local capacity limits are satisfied. Decentralized optimization to solve this problem has been a significant focus within engineering research due to its advantages in scalability, robustness, and flexibility. Most existing methods are synchronous while few works are devoted to asynchronously solving the problem. The problem becomes even more challenging when the networks are directed. To address the resource allocation problem when the above issues are considered, we propose a novel decentralized asynchronous algorithm based on the gossip-based communication protocol and epigraph strategy. An important feature of the algorithm is that it is implemented in a completely decentralized manner in the case of asynchronous communication and directed networks. We provide theoretical proof to guarantee the convergence of the proposed algorithm, which indicates that it can successfully allocate the optimal resource. When solving the resource allocation problem over time-varying directed networks, we further discuss a related decentralized asynchronous algorithm according to the random sleep protocol. Numerical examples are given to demonstrate the viability and performance of the algorithms. [ABSTRACT FROM AUTHOR]

The estimation of nested functions (i.e., functions of functions) is one of the central reasons for the success and popularity of machine learning. Today, artificial neural networks are the predominant class of algorithms in this area, known as representational learning. Here, we introduce Representational Gradient Boosting (RGB), a nonparametric algorithm that estimates functions with multi-layer architectures obtained using backpropagation in the space of functions. RGB does not need to assume a functional form in the nodes or output (e.g., linear models or rectified linear units), but rather estimates these transformations. RGB can be seen as an optimized stacking procedure where a meta algorithm learns how to combine different classes of functions (e.g., Neural Networks (NN) and Gradient Boosting (GB)), while building and optimizing them jointly in an attempt to compensate each other’s weaknesses. This highlights a stark difference with current approaches to meta-learning that combine models only after they have been built independently. We showed that providing optimized stacking is one of the main advantages of RGB over current approaches. Additionally, due to the nested nature of RGB we also showed how it improves over GB in problems that have several high-order interactions. Finally, we investigate both theoretically and in practice the problem of recovering nested functions and the value of prior knowledge. [ABSTRACT FROM AUTHOR]

Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Previous methods attempted to approximate the true gradient either by using the transfer gradient of a surrogate white-box model or based on the feedback of model queries. However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information. To address these problems and improve black-box attacks, we propose two prior-guided random gradient-free (PRGF) algorithms based on biased sampling and gradient averaging, respectively. Our methods can take the advantage of a transfer-based prior given by the gradient of a surrogate model and the query information simultaneously. Through theoretical analyses, the transfer-based prior is appropriately integrated with model queries by an optimal coefficient in each method. Extensive experiments demonstrate that, in comparison with the alternative state-of-the-arts, both of our methods require much fewer queries to attack black-box models with higher success rates. [ABSTRACT FROM AUTHOR]

The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This article presents a basic machine-learning (ML) algorithm, called Dendrite Net or DD, just like the support vector machine (SVM) or multilayer perceptron (MLP). DD’s main concept is that the algorithm can recognize this class after learning, if the output’s logical expression contains the corresponding class’s logical relationship among inputs (and $\backslash $ or $\backslash $ not). Experiments and main results: DD, a white-box ML algorithm, showed excellent system identification performance for the black-box system. Second, it was verified by nine real-world applications that DD brought better generalization capability relative to the MLP architecture that imitated neurons’ cell body (Cell body Net) for regression. Third, by MNIST and FASHION-MNIST datasets, it was verified that DD showed higher testing accuracy under greater training loss than the cell body net for classification. The number of modules can effectively adjust DD’s logical expression capacity, which avoids overfitting and makes it easy to get a model with outstanding generalization capability. Finally, repeated experiments in MATLAB and PyTorch (Python) demonstrated that DD was faster than Cell body Net both in epoch and forwardpropagation. The main contribution of this article is the basic ML algorithm (DD) with a white-box attribute, controllable precision for better generalization capability, and lower computational complexity. Not only can DD be used for generalized engineering, but DD has vast development potential as a module for deep learning. DD code is available at https://github.com/liugang1234567/Gang-neuron. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM) is a type of powerful deep neural network that has been widely used in many sequence analysis and modeling applications. However, the large model size problem of LSTM networks make their practical deployment still very challenging, especially for the video recognition tasks that require high-dimensional input data. Aiming to overcome this limitation and fully unlock the potentials of LSTM models, in this paper we propose to perform algorithm and hardware co-design towards high-performance energy-efficient LSTM networks. At algorithm level, we propose to develop fully decomposed hierarchical Tucker (FDHT) structure-based LSTM, namely FDHT-LSTM, which enjoys ultra-low model complexity while still achieving high accuracy. In order to fully reap such attractive algorithmic benefit, we further develop the corresponding customized hardware architecture to support the efficient execution of the proposed FDHT-LSTM model. With the delicate design of memory access scheme, the complicated matrix transformation can be efficiently supported by the underlying hardware without any access conflict in an on-the-fly way. Our evaluation results show that both the proposed ultra-compact FDHT-LSTM models and the corresponding hardware accelerator achieve very high performance. Compared with the state-of-the-art compressed LSTM models, FDHT-LSTM enjoys both order-of-magnitude reduction (more than $1000 \times$ 1000 × ) in model size and significant accuracy improvement (0.6% to 12.7%) across different video recognition datasets. Meanwhile, compared with the state-of-the-art tensor decomposed model-oriented hardware TIE, our proposed FDHT-LSTM architecture achieve $2.5\times$ 2. 5 × , $1.46\times$ 1. 46 × and $2.41\times$ 2. 41 × increase in throughput, area efficiency and energy efficiency, respectively on LSTM-Youtube workload. For LSTM-UCF workload, our proposed design also outperforms TIE with $1.9\times$ 1. 9 × higher throughput, $1.83\times$ 1. 83 × higher energy efficiency and comparable area efficiency. [ABSTRACT FROM AUTHOR]

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain – CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain. [ABSTRACT FROM AUTHOR]

A flat-field correction method is proposed for multiple measured hyperspectral microscopy imaging in this paper. As the most crucial preprocessing process in quantitative microscopic analysis, flat-field correction solves the uneven illumination caused by vignetting in microscopic images, and guarantees the precision of spatial and spectral information in hyperspectral microscopic imaging. In order to carry out flat-field correction and extract uneven illumination among groups of hyperspectral microscopic data containing hundreds of bands simultaneously, two properties of vignetting have been exploited: i) low-rank property is reflected by little information contained in vignetting; ii) local smoothness can be observed as a gradual change in brightness of vignetting, which is typically equivalent to the sparseness in spatial frequency domain. Combining the two properties above, a novel Low Rank Smooth Flat-field Correction (LRSFC) model modified from common orthogonal basis extraction is proposed, while an optimization is solved based on alternating direction multiplier method (ADMM), obtaining a unique flat-field term with low-rank and smooth properties. Qualitative and quantitative experimental assessments indicate that LRSFC does not add extra cell texture to the extracted flat-field term, whose performance appears prior to other state-of-the-art flat-field correction methods. [ABSTRACT FROM AUTHOR]

Owing to its good compression ability, discrete cosine transform (DCT) is widely used in signal processing, including high-efficiency video coding (HEVC). For efficient implementation, approximate DCTs and integer DCTs were proposed, but the challenge in the tradeoff between coding performance and implementation cost always exists. To solve this problem, this article proposes a new algorithm to derive Int-DCT with good coding performance and low hardware cost. To address the multiobjective optimization problem, we extract information from the initially shortlisted transform matrices to derive the weighting factors for the different matrices properties. Those properties having a higher impact to the performance are assigned with higher weights and vice versa. Subsequently, an efficient search algorithm is proposed to shortlist those candidates which can lead to better coding performance evaluated by the proposed measure. Last but not the least, a splitting method is proposed to search the efficient extended double-base number system (EDBNS) representation of the coefficients for hardware implementation and select the solution which encounters the lowest cost. The experimental results in ASIC demonstrate that the implementation area and power cost of designs by the new algorithm are reduced by at least 11.1% and 17.5% for 32-point transform, respectively, over other competing algorithms. Meanwhile, the transforms generated by the proposed algorithm causes negligible impact to coding performance compared to the original transforms in HEVC. [ABSTRACT FROM AUTHOR]

Topological quantum error correction (TQEC) is promising for scalable fault-tolerant quantum computation. The required resource of a TQEC circuit can be modeled as its space-time volume of a three-dimensional geometric description. Implementing a quantum algorithm with a reasonable physical qubit number and computation time is challenging for large-scale complex problems. Therefore, it is desirable to minimize the space-time volume for large-scale TQEC circuits. Previous work proposed bridge compression, which can significantly compress a TQEC circuit, but it was performed manually. This article presents the first automated tool that can perform bridge compression on a large-scale TQEC circuit. Our proposed algorithm applies the bridge compression technique to compactify TQEC circuits with modularization. Besides, we offer a time-ordering-aware 2.5-D placement for compacting TQEC circuits and satisfying time-ordered measurement constraints. On the other hand, we suggest friend net-aware routing to effectively reduce the required routing resource under topological deformation. Compared with the state-of-the-art work, experimental results show that our proposed algorithm can averagely reduce space-time volumes by 84%. [ABSTRACT FROM AUTHOR]

This work presents an automated design methodology for time-mode proportional and integral (PI) controllers aimed for an on-chip switched-capacitor (SC) dc/dc converter system. The basis of this design is the use of evolutionary optimization algorithms to find the near-optimal set of sizings for the time-mode PI controller. It is motivated due to the difficulty faced when tuning the controller parameters at a circuit level, which arise as a result of the presence of modeling inaccuracies and the small region for the linearized model where it is defined. Moreover, this design proposes the required modifications for the original design presented for the inductor-based dc/dc converter. These modifications are necessary to operate the SC dc/dc converter in slow switching limit (SSL). The addition of a pulse-width-modulated (PWM)-to-pulse frequency-modulated (PFM) conversion block is presented and elaborated in this article. The controller is codesigned using the differential evolution algorithm for the circuit level implementation to mitigate the issues prior mentioned. The optimized controller is then tested in a simulation environment using TSMC $0.18~\mu \text{m}$ technology. The results of the optimized controller were superior to those of a conventional controller. The optimized system achieved an overall efficiency of 79.1%. [ABSTRACT FROM AUTHOR]

Soft starters are a well-known solution for starting induction motors in industrial applications. With a predictive algorithm approach the losses in motor and soft starter can be lowered significantly. This article presents an algorithm which can be implemented in industrial devices with a concomitant speed control option. This speed control is necessary to fit the users requirements for start-up. Besides the speed control, the characteristic behavior of the predictive algorithm is analyzed in detail. The suitability of the novel predictive closed loop approach for different representative industrial applications is proven by measurements and discussed in detail. [ABSTRACT FROM AUTHOR]

To achieve levitation in active magnetic bearing (AMB) systems, levitation coils must be driven by a switch power amplifier (SPA). Therefore, SPA performance is important to stable levitation. In previous studies, five-phase six-leg (5P6L) SPA topology was proposed to reduce hardware costs. However, current tracking performance degrades due to the coupling problem of the five-phase current and freewheeling loss. Then, the rotor may deviate markedly from the balance position, affecting rotor stability. The keys to solving these problems lie in the duty cycle control of the common leg and the compensation of freewheeling loss. This article proposes an improved one-cycle control (OCC) algorithm in which the duty cycle of the common leg is optimized by finite control set model predictive control (FCS-MPC), and the freewheeling loss is compensated for by a high-precision mathematical model. In addition, the parameter uncertainty of the FCS-MPC model is analyzed, and the convergence of the optimization algorithm is investigated. Simulations and experiments show that the proposed algorithm is effective, and the high dynamic response and low steady-state error performance of SPA are achieved. [ABSTRACT FROM AUTHOR]

When partial discharge (PD) measurement is performed on power transformers under the IEC60270 standard, a large error will occur in apparent charge estimation due to the winding distributive longitudinal capacitances and unknown location of the PD source. A frequency-spectrum-based PD localization and an apparent charge error correction method are proposed based on winding RLC ladder network gray-box modeling with FRA results and intelligent algorithm. An experiment on a 220 kV inhomogeneous model winding is carried out and the proposed method is applied. The results show that the maximum error of apparent charge has reduced from 37.7 dB to less than 3 dB which shows an error decrease of 70%, verifying the feasibility of this method on real windings. The proposed method requires only single-end PD measurement and follows strictly the IEC60270 standard, which is hopefully to be extended to more complex transformer windings in future research. [ABSTRACT FROM AUTHOR]

Auditing the energy consumption in urban rail is vital for energy consumption evaluation and system parameter design. There are multiple ways to audit energy consumption, but a universal and global approach is missing. The system-level traction energy consumption (STEC) is proposed. Compared to the main substation energy consumption (MSEC), STEC is more accurate by eliminating the influence of step-down loads based on field test studies. An optimization parameter designing model is built, which takes the system cost as the optimal object considering the life span of energy feedback system (EFS)s. The modified salp swarm algorithm (MSSA) is proposed as the optimization algorithm. The numerical tests show that MSSA has better converge performance than salp swarm algorithm (SSA) and particle swarm optimization (PSO). The impact factors of STEC are analyzed. Compared with SSA and PSO, the initial value of the MSSA is improved and it evolves faster. Compared with the case that does not take the no-load voltage of rectifiers and start voltage of EFSs as optimal parameters, the composite cost of the case that takes the abovementioned parameters as optimal parameters is 3.49% less. Compared to the system without EFSs, the optimal system with EFSs can save costs by 29.47%. [ABSTRACT FROM AUTHOR]

This study presents a maximum torque per ampere (MTPA) control method for a mono inverter dual parallel (MIDP) surface-mounted permanent-magnet synchronous motor (SPMSM) drive system. The multimotor drive system has been extensively studied, because it can drive moreover two motors using only one inverter, reducing the costly power conversion circuit. However, existing studies concerning MIDP permanent-magnet synchronous motor drive systems focus on stabilizing the system, and studies regarding efficiency optimization are difficult to find. Some studies have shown the efficiency-optimizing method, but implementing is challenging because of the complicated calculations and algorithm. The proposed control method can achieve efficiency-optimizing operation using the angle difference controller. The angle difference controller controls the angle difference of two motors to an optimal value. Furthermore, the optimal angle difference of two motors can be easily obtained regardless of parameter errors, using the perturbation and observation algorithm. The proposed MTPA strategy is verified by performing simulations using matlab Simulink and applying the method to an actual MIDP SPMSM drive system. [ABSTRACT FROM AUTHOR]

Real-world graphs exhibit many interesting properties that differentiate them from random graphs, which have been extensively studied for the past decades. For various proposed generative models, a majority of them build the graph by sequentially adding each node and the attached edges. However, the growth of many real-world graphs, such as social networks, is naturally modeled by the sequential insertion of edges. Unfortunately, to the best of our knowledge, no generative model has been proposed to reveal this process. We propose the first sequence-of-edges model, denoted as temporal preferential attachment (TPA). It relies on preferential attachment (PA), one of the most influential mechanisms to generate scale-free graphs, and takes time-decay effect and node fitness into consideration. Empirical analysis demonstrates that our model preserves several key properties of the real-world graphs, including both the properties observed from the snapshot graphs (e.g., power-law distribution) and temporal properties observed from the graph generation process (e.g., shrinking diameter). Meanwhile, our model is sufficiently general to accommodate several forms of time decay and fitness distributions. Then, we design two efficient algorithms that generate TPA graphs with billions of edges in several minutes. [ABSTRACT FROM AUTHOR]

One-class collaborative filtering (OCCF) problems are ubiquitous in real-world recommendation systems, such as news recommendation, but suffer from data sparsity and lack of negative items. To address the challenge, the state-of-the-art algorithm assigns uninteracted items with smaller weights of being negative and performs low-rank approximation over the user-item interaction matrix. However, the prior ratings are usually suggested to be zero but may not be well-defined. To avert the direct utilization of prior ratings for uninteracted items, we propose a novel ranking-based implicit regularizer by hypothesizing that users’ preference scores for uninteracted items should not deviate a lot from each other. The regularizer is then used in a ranking-based OCCF framework to penalize large differences of preference scores between uninteracted items. To efficiently optimize model parameters in this framework, we develop the scalable alternating least square algorithm and coordinate descent algorithm, whose time complexity is linearly proportional to the data size. Finally, we extensively evaluate the proposed algorithms on six public real-world datasets. The results show that the proposed regularizer significantly improves the recommendation quality of ranking-based OCCF algorithms, such as BPRMF and RankALS. Moreover, the ranking-based framework with the proposed regularizer outperforms the state-of-the-art recommendation algorithms for implicit feedback. [ABSTRACT FROM AUTHOR]

Tensor computations are important mathematical operations for applications that rely on multidimensional data. The tensor–vector multiplication (TVM) is the most memory-bound tensor contraction in this class of operations. This article proposes an open-source TVM algorithm which is much simpler and efficient than previous approaches, making it suitable for integration in the most popular BLAS libraries available today. Our algorithm has been written from scratch and features unit-stride memory accesses, cache awareness, mode obliviousness, full vectorization and multi-threading as well as NUMA awareness for non-hierarchically stored dense tensors. Numerical experiments are carried out on tensors up to order 10 and various compilers and hardware architectures equipped with traditional DDR and high bandwidth memory (HBM). For large tensors the average performance of the TVM ranges between 62% and 76% of the theoretical bandwidth for NUMA systems with DDR memory and remains independent of the contraction mode. On NUMA systems with HBM the TVM exhibits some mode dependency but manages to reach performance figures close to peak values. Finally, the higher-order power method is benchmarked with the proposed TVM kernel and delivers on average between 58% and 69% of the theoretical bandwidth for large tensors. [ABSTRACT FROM AUTHOR]

Sharing data across various Internet of Things (IoT) devices has been a common challenge due to efficiency, security, and stability issues. Blockchain, with security features, is considered to be a potential solution for data sharing in IoT settings. However, traditional blockchain-based solutions cannot satisfy the efficiency requirement of high-frequency data sharing among IoT devices. In this paper, we propose an efficient IoT data sharing approach by adopting the Payment Channel Network (PCN)-extended blockchain. Besides, we develop a homomorphic hashing-based transaction segmentation scheme to solve the issue of low transaction success ratio caused by channel deposit restrictions in PCN. In addition, a Multi-point Relay (MPR)-based multi-path routing scheme has been developed to ensure high-frequency transaction forwarding. The communication overhead of maintaining the routing table is reduced by our proposed Multi-point Relay Selection algorithm, and multiple alternate paths generated by Multiple Routing Path algorithm can improve the transaction success rate. Experiment evaluations have demonstrated that that our proposed approach outperforms the baseline approaches in terms of the transaction efficiency and success ratio. [ABSTRACT FROM AUTHOR]

Recently, large-scale multiobjective optimization has received increasing attention from the evolutionary multiobjective optimization (EMO) community. This has led to the emergence of a specialized research area called evolutionary large-scale multiobjective optimization (ELMO). In general, it is believed that multiobjective optimization problems become more difficult as the number of decision variables increases. However, the following two counterintuitive observations are obtained from careful examinations of recent ELMO studies. One is that experimental results on some large-scale multiobjective test problems were improved by increasing the number of decision variables. The other is that better results were obtained for some other large-scale multiobjective test problems by conventional EMO algorithms (EMOAs) than state-of-the-art ELMO algorithms (ELMOAs). These observations suggest that ELMOAs have not always been evaluated on appropriate test problems. Moreover, their performance is not always better than the performance of conventional EMOAs. In this letter, we first re-examine the performance of ELMOAs and conventional EMOAs on a wide variety of scalable multiobjective test problems. Then, counterintuitive experimental results are analyzed using the anytime performance evaluation scheme and distributions of randomly generated initial solutions. Based on the analysis, suggestions on how to handle large-scale multiobjective test problems with counterintuitive results are proposed. [ABSTRACT FROM AUTHOR]

Traditionally, visualizing benchmark problems is an integral task in the domain of evolutionary algorithms development. Researchers get inspired for new search heuristics by challenges observed in functional landscapes. Moreover, landscape characteristics, features, and even terminology to describe them are derived from visualizations. And most importantly, benchmark designers need visualizations for identifying diverse problems that potentially challenge different aspects of optimization algorithms. As easy as it is to visualize single-objective problems, until recently there were hardly any approaches for gaining similar insights for multi-objective problems. Also, there have been no seamlessly accessible tools to support such visualizations. This article presents a comprehensive overview of the available visualization techniques from literature, including two interactive techniques to visualize 3-D problems, as well as two novel techniques which are suitable to scale some visualization properties to even higher-dimensional spaces. All presented techniques are integrated into a single tool, the moPLOT-dashboard, which enables users to perform landscape analyses in an interactive manner. Finally, the value of the tool and the visualizations is demonstrated in a series of usage scenarios on well-known benchmark problems. [ABSTRACT FROM AUTHOR]

Automated hyperparameter optimization (HPO) has gained great popularity and is an important component of most automated machine learning frameworks. However, the process of designing HPO algorithms is still an unsystematic and manual process: new algorithms are often built on top of prior work, where limitations are identified and improvements are proposed. Even though this approach is guided by expert knowledge, it is still somewhat arbitrary. The process rarely allows for gaining a holistic understanding of which algorithmic components drive performance and carries the risk of overlooking good algorithmic design choices. We present a principled approach to automated benchmark-driven algorithm design applied to multifidelity HPO (MF-HPO). First, we formalize a rich space of MF-HPO candidates that includes, but is not limited to, common existing HPO algorithms and then present a configurable framework covering this space. To find the best candidate automatically and systematically, we follow a programming-by-optimization approach and search over the space of algorithm candidates via Bayesian optimization. We challenge whether the found design choices are necessary or could be replaced by more naive and simpler ones by performing an ablation analysis. We observe that using a relatively simple configuration (in some ways, simpler than established methods) performs very well as long as some critical configuration parameters are set to the right value. [ABSTRACT FROM AUTHOR]

In the field of optimization and machine learning, the statistical assessment of results has played a key role in conducting algorithmic performance comparisons. Classically, null hypothesis statistical tests have been used. However, recently, alternatives based on Bayesian statistics have shown great potential in complex scenarios, especially when quantifying the uncertainty in the comparison. In this work, we delve deep into the Bayesian statistical assessment of experimental results by proposing a framework for the analysis of several algorithms on several problems/instances. To this end, experimental results are transformed to their corresponding rankings of algorithms, assuming that these rankings have been generated by a probability distribution (defined on permutation spaces). From the set of rankings, we estimate the posterior distribution of the parameters of the studied probability models, and several inferences concerning the analysis of the results are examined. Particularly, we study questions related to the probability of having one algorithm in the first position of the ranking or the probability that two algorithms are in the same relative position in the ranking. Not limited to that, the assumptions, strengths, and weaknesses of the models in each case are studied. To help other researchers to make use of this kind of analysis, we provide a Python package and source code implementation at https://zenodo.org/record/6320599. [ABSTRACT FROM AUTHOR]

Benchmarking various metaheuristics and their new enhancements, strategies, and adaptation mechanisms has become standard in computational intelligence research. Recently, many challenges and issues regarding fair comparisons and recommendations toward good practices for benchmarking of metaheuristic algorithms, have been identified. This article is aimed at an important issues in metaheuristics design and benchmarking, which are boundary strategies or boundary control methods (BCMs). This work aims to investigate whether the choice of a BCM could significantly influence the performance of competitive algorithms. The experiments encompass the top three performing algorithms from IEEE CEC competitions 2017 and 2020 with six different BCMs. We provide extensive statistical analysis and rankings resulting in conclusions and recommendations for metaheuristics researchers and possibly also for the future direction of benchmark definitions. We conclude that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics, since the BCM setting could impact the optimization method performance. [ABSTRACT FROM AUTHOR]

Quality-diversity (QD) algorithms refer to a class of evolutionary algorithms designed to find a collection of diverse and high-performing solutions to a given problem. In robotics, such algorithms can be used for generating a collection of controllers covering most of the possible behaviors of a robot. To do so, these algorithms associate a behavioral descriptor (BD) to each of these behaviors. Each BD is used for estimating the novelty of one behavior compared to the others. In most existing algorithms, the BD needs to be hand-coded, thus requiring prior knowledge about the task to solve. In this article, we introduce: autonomous robots realizing their abilities, an algorithm that uses a dimensionality reduction technique to automatically learn BDs based on raw sensory data. The performance of this algorithm is assessed on three robotic tasks in simulation. The experimental results show that it performs similar to traditional hand-coded approaches without the requirement to provide any hand-coded BD. In the collection of diverse and high-performing solutions, it also manages to find behaviors that are novel with respect to more features than its hand-coded baselines. Finally, we introduce a variant of the algorithm which is robust to the dimensionality of the BD space. [ABSTRACT FROM AUTHOR]

Feature-based algorithm selection aims to automatically find the best one from a portfolio of optimization algorithms on an unseen problem based on its landscape features. Feature-based algorithm selection has recently received attention in the research field of black-box numerical optimization. However, there is still room for the analysis of algorithm selection for black-box optimization. Most previous studies have focused only on whether an algorithm selection system can outperform the single-best solver (SBS) in a portfolio. In addition, a benchmarking methodology for algorithm selection systems has not been well investigated in the literature. In this context, this article analyzes algorithm selection systems on the 24 noiseless black-box optimization benchmarking functions. First, we demonstrate that the first successful performance measure is more reliable than the expected runtime measure for benchmarking algorithm selection systems. Then, we examine the influence of randomness on the performance of algorithm selection systems. We also show that the performance of algorithm selection systems can be significantly improved by using sequential least squares programming as a presolver. We point out that the difficulty of outperforming the SBS depends on algorithm portfolios, cross-validation methods, and dimensions. Finally, we demonstrate that the effectiveness of algorithm portfolios depends on various factors. These findings provide fundamental insights for algorithm selection for black-box optimization. [ABSTRACT FROM AUTHOR]

Band selection (BS) is a widely used dimensionality reduction technique for hyperspectral images. However, most of existing evolutionary algorithms focus on searching a globally optimal band subset under a fixed size, and their obtained band subsets may still contain a large number of redundant bands. In order to simultaneously obtain multiple optimal band subsets with different sizes, this article proposes an unsupervised multitask artificial bee colony (ABC) BS algorithm based on variable-size clustering (MBBS-VC). First, a variable-size band clustering method based on worst class decomposition is developed, based on which the BS problem can be modeled as a multitask optimization problem. Next, a multitask multimicrogroup bee colony algorithm with variable coding length is proposed to simultaneously search multiple optimal band subsets with different sizes. Moreover, several new strategies, including the intergroup collaboration strategy based on bidirectional neighborhood learning and the multimeasure integration judgment (MIJ) mechanism, are designed to improve the performance of MBBS-VC. In this article, the hyperspectral BS problem is transformed into a multitask optimization problem for the first time. Finally, compared with 15 classical BS algorithms on several commonly used datasets, experimental results verify the superiority of the proposed BS algorithm. [ABSTRACT FROM AUTHOR]

This work assesses the efficacy of evolutionary algorithms (EAs) using an intuitive multidimensional scaling (MDS) visualization of the evolution of a population. We propose the use of landmark MDS (LMDS) to overcome computational challenges inherent to visualizing many-objective and complex problems with MDS. For the benchmark problems we tested, LMDS is akin to MDS visually, whilst requiring less than 1% of the time and memory necessary to produce an MDS visualization of the same objective space solutions, leading to the possibility of online visualizations for multi- and many-objective optimization evaluation. Using multi- and many-objective problems from the DTLZ and WFG benchmark test suites, we analyze how Landmark MDS visualizations can offer far greater insight into algorithm performance than using traditional algorithm performance metrics such as hypervolume alone, and can be used to complement explicit performance metrics. Ultimately, this visualization allows the visual identification of problem features and assists the decision maker in making intuitive recommendations for algorithm parameters/operators for creating and testing better EAs to solve multi- and many-objective problems. [ABSTRACT FROM AUTHOR]

We propose four frameworks for clustering data with missing values. We first use a very simple method to impute the missing values and generate multiple imputed versions of the data. These views are then clustered together to obtain a common partition matrix and a common set of centroids. As the clustering framework, we use a multiview version of the Fuzzy-c-Means (MVFCM) and a multiview version of Kernelized Fuzzy-c-Means (MVKFCM). To find the importance (weights) of different views, we use an entropic regularization term using the weights. After obtaining the optimal weights, the final imputation is done as a weighted sum (convex combination) of the imputed values used to generate the views. The final clustering is done on this imputed data set. We compare the performance of the proposed algorithms with several algorithms using Normalized Mutual Information, Adjusted Rand Index, and cluster accuracy on 12 benchmark data sets. Of these algorithms, the MVKFCM is found to perform the best. The MVFCM and MVKFCM use $5 \times r$ views, where $r$ is the number of classes (note that the class labels are not used). However, $r$ may not be known, and also for large $r$ , there will be too many views. So we propose two variants of MVKFCM: $MVKFCM_{FV}$ and $MVKFCM_{RFV}$ (FV stands for a fixed number of views and RFV stands for a robust version with fixed views). The $MVKFCM_{RFV}$ generates views in a manner that helps to obtain robust performance. As expected, $MVKFCM_{RFV}$ is found to be the best performing algorithm. [ABSTRACT FROM AUTHOR]

Many inverse problems in machine learning, system identification, and image processing include nuisance parameters, which are important for the recovering of other parameters. Separable nonlinear optimization problems fall into this category. The special separable structure in these problems has inspired several efficient optimization strategies. A well-known method is the variable projection (VP) that projects out a subset of the estimated parameters, resulting in a reduced problem that includes fewer parameters. The expectation maximization (EM) is another separated method that provides a powerful framework for the estimation of nuisance parameters. The relationships between EM and VP were ignored in previous studies, though they deal with a part of parameters in a similar way. In this article, we explore the internal relationships and differences between VP and EM. Unlike the algorithms that separate the parameters directly, the hierarchical identification algorithm decomposes a complex model into several linked submodels and identifies the corresponding parameters. Therefore, this article also studies the difference and connection between the hierarchical algorithm and the parameter-separated algorithms like VP and EM. In the numerical simulation part, Monte Carlo experiments are performed to further compare the performance of different algorithms. The results show that the VP algorithm usually converges faster than the other two algorithms and is more robust to the initial point of the parameters. [ABSTRACT FROM AUTHOR]