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In recent years, NAND flash memory has been widely used in mobile devices, laptops, desktops, and data center storage systems due to its low-power consumption, high performance, high density, lightweight, shock resistance, and high-reliability natures. However, as the stacked layers and the storage density increase, flash memory also suffers from a higher raw bit error rate (RBER) and shorter lifetime. Observing that reliable cell states suffer from less data retention errors and program disturbance, we propose a differential evolution coding scheme to increase the probability of storing data in more reliable cell states, thereby reducing the RBER. We conducted the experiments over a development platform of SSD storage device with 3D-TLC charge trap (CT) NAND flash memory. The experimental results showed that the proposed differential evolution algorithm with asymmetric coding scheme could averagely reduce RBER by 48.88%, 65.45%, 52.61%, 61.99%, 80.19%, and 33.18% compared with baseline, asymmetric coding algorithm, asymmetric coding scheme with stripe-pattern elimination algorithm, UAC- $n$ LC, word-line batch score modulation programming, and SCB schemes. [ABSTRACT FROM AUTHOR]

In-situ tool condition monitoring (in-situ TCM) is vital for metal removal manufacturing which realizes on-machine diagnosis in a real-time manner. The limitation of in-situ TCM based on traditional deep learning lies in several aspects: the requirement of sufficient labeled data of health conditions, the empirically manual designed architecture, and the labor-intensive tuning of hyperparameters. Network-based deep transfer learning (NDTL) partially solves the problem of limited labeled data. However, the time-consuming architecture design and the tuning of hyperparameters also pose a great impact on the schedule of real in-situ TCM projects. In this article, a new NDTL method is proposed, which is automatically designed by the genetic algorithm. A degradation monitoring experiment is conducted employing edge computing devices under multiple working conditions, where the texture of the machined surface is collected during the whole life cycle of milling cutters. The experimental results suggest that the proposed method possesses competitive performance evaluated by both the robustness metrics (e.g., the area under the curve of precision-recall) and the efficiency metrics (e.g., multiply–accumulates), where the trial-and-errors are reduced significantly by the automatic architecture design and the selection of hyperparameters. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are being actively adopted as a solution to sustainable transportation. However, a bottleneck remains with charging, where two of the main problems are the long charging time and the range anxiety of EV drivers. In this research, we investigate the deployment of dynamic charging systems, i.e., electrified roads that wirelessly charge EVs on the go, with a view to accelerating EV adoption rate. We propose a traffic-based deployment strategy, statistically quantify its impact, and apply the strategy to two case studies of real traffic in New York City (USA) and Xi’an (China). We find that our analytical estimates not only closely match the real data, but they also suggest that dynamic charging considerably extends the driving range of popular EV models in urban mobility. For example, when only 5% of the existing roads in New York City are equipped with this technology, an EV model such as the Nissan Leaf will approximately maintain its battery level without stopping to recharge. If the percentage of charging roads is increased to 10%, then the Leaf will gain nearly 10% of its battery after every 40 kilometers of driving. Our framework provides a solution to public and private organizations that support and facilitate vehicle electrification through charging infrastructure. [ABSTRACT FROM AUTHOR]

This article presents a new variant of particle swarm optimization (PSO) algorithm, which was developed for a reliable parameter estimation in thermal analysis of electrical machines. The proposed algorithm uses a varied social network, where both number and size of the network (local neighborhoods) are randomly adjusted during the optimization process. Such approach has been introduced here to assure improved diversity of the PSO and consequently a more reliable and robust search of the solution space. A case study parameter estimation for a reduced-order thermal-equivalent-circuit (TEC) of an electrical machine has been used to demonstrate effectiveness of the proposed method. The analyzed black-box parameter estimation relies on the input and output data (demand data) from a short-transient finite-element-analysis (FEA) of a complete machine assembly. The proposed PSO variant has been benchmarked with a selection of the existing state-of-the-art PSO algorithms, which employ alternative social network schemes with the network parameters dynamically varied. The statistical data gathered from multiple runs of the PSO-based estimation suggests that the proposed new approach offers considerable improvements in terms of accuracy, efficiency, reliability, and robustness as compared with the alternative PSO algorithms. [ABSTRACT FROM AUTHOR]

In this work, a novel general pattern of assisted flux barriers in an asymmetric V-shape interior permanent magnet (AVIPM) machine is presented. The AVIPM machine has a symmetric permanent magnet (PM) structure and an asymmetric rotor core structure to realize the magnetic-field-shifting (MFS) effect. The general pattern can represent four possible types of assisted flux barriers at different positions on the rotor core and the final structure can be automatically determined by using optimization method. The advantage of the proposed optimization pattern is that the optimal design of assisted flux barriers in V-shape interior permanent magnet (VIPM) machines with high torque and low torque ripple can be generated within a short computing time. The proposed optimization method is applied to improve the structure of a conventional 8-pole 48-slot VIPM machine, which is commonly used for driving electric vehicles (EVs). A non-dominated sorting genetic algorithm II (NSGA-II) is used for the global optimization of both a VIPM machine and an AVIPM machine. The electromagnetic performance is computed using a finite element analysis (FEA). Moreover, a frozen permeability (FP) method is applied to perform an accurate separation of PM torque and reluctance torque. The results exhibit that the peak output torque of the AVIPM machine is increased by 8.7% compared to a conventional VIPM machine with the same PM volume due to the notable MFS effect. In the meantime, the AVIPM machine has a better overload capability due to the larger contribution of reluctance torque. Moreover, the proposed machine has high efficiency of over 97%. [ABSTRACT FROM AUTHOR]

Optimization modeling of real-world application problems usually involves noise from various sources. Noisy optimization imposes challenges to optimization methods since the objective values can be different for multiple evaluations. In this article, we propose a novel online population size learning (OPL) technique of evolution strategies for handling noisy optimization problems. By re-evaluating a fraction of the candidates, we measure the strength of noise level of the re-evaluated candidate solutions and adapt the population size according to the noise level. The proposed OPL combines the advantages of both explicit averaging by re-evaluations and the implicit averaging by large population size and overcomes their limitations. We incorporate it with the covariance matrix adaptation evolution strategy (CMA-ES) and obtain OPL-CMA-ES. Compared with the existing noise handling technique, the proposed OPL is much simpler in both concepts and computation. We conduct comprehensive experiments to evaluate the algorithm’s performance on standard problems with Gaussian noise. We further evaluate the performance of OPL-CMA-ES on the black-box optimization benchmarks (BBOBs) noisy testbed, which is a standard platform for comparing black-box optimization algorithms, compared with the state-of-the-art noise-handling algorithms. The experimental results show that OPL-CMA-ES achieves remarkable performance and outperforms the compared variants. [ABSTRACT FROM AUTHOR]

Identifying borehole lithofacies through geop- hysical loggings is a fundamental task in petroleum exploration industry. Recent interdisciplinary studies have demonstrated the feasibility of applying machine learning to lithofacies identification. Most of these studies establish a mapping from the logging values at one depth point to the lithofacies type. However, due to the intrinsic properties of geophysical loggings, the logging shape should be taken into consideration, apart from the absolute values. In this article, we present the attempt to predict the lithofacies by feeding logging segments, and for the first time model the logging lithofacies identification problem as 1-D semantic segmentation. Such a logging segmentation task is challenging due to two reasons, strong spatial heterogeneity of lithofacies subsurface distribution and the explicit physical significance of geophysical loggings. To solve these challenges, we propose a novel geophysical logging segmentation network entitled SegLog. Specifically, we develop a global statistics pooling subnetwork and a statistics fusion subnetwork to generate statistical embeddings of geophysical loggings. Based on these statistical embeddings, we design a pixel-enhanced convolutional subnetwork to learn the microdetailed features, indicated by pixel-level logging values. These features are fused with the macrosemantic features extracted by a backbone U-Net to constitute the representations that can simultaneously describe the logging spatial correlation and pixel specificity. Experimental results on two logging datasets from the Jiyang Depression verify the effectiveness of our modeling strategy and its state-of-the-art performance on the lithofacies identification problem. [ABSTRACT FROM AUTHOR]

Biocomputing systems based on engineered bacteria can lead to novel tools for environmental monitoring and detection of metabolic diseases. In this paper, we propose a Bacterial Molecular Computing on a Chip (BMCoC) using microfluidic and electrochemical sensing technologies. The computing can be flexibly integrated into the chip, but we focus on engineered bacterial AND Boolean logic gate and ON-OFF switch sensors that produces secondary signals to change the pH and dissolved oxygen concentrations. We present a prototype with experimental results that shows the electrochemical sensors can detect small pH and dissolved oxygen concentration changes created by the engineered bacterial populations’ molecular signals. Additionally, we present a theoretical model analysis of the BMCoC computation reliability when subjected to unwanted effects, i.e., molecular signal delays and noise, and electrochemical sensors threshold settings that are based on either standard or blind detectors. Our numerical analysis found that the variations in the production delay and the molecular output signal concentration can impact on the computation reliability for the AND logic gate and ON-OFF switch. The molecular communications of synthetic engineered cells for logic gates integrated with sensing systems can lead to a new breed of biochips that can be used for numerous diagnostic applications. [ABSTRACT FROM AUTHOR]

In distributed computing, Web Service Composition (WSC) leads to the effective reuse of existing services and produces added value. WSC must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. Memetic Algorithms (MAs) are promising for automatically composing numerous Web services to satisfy the above requirements. Data-intensive Web services focus on providing and updating data with a significant volume of data operation and exchange. However, current composition approaches have ignored the impact of data communication and the distribution of services, which significantly affect the performance when applied to the challenging Distributed Data-intensive Web Service Composition (DDWSC) problem. Although recent approaches have revealed the usefulness of local search, they have completely overlooked the question of preferring appropriate composition solutions for local search. To address this research issue, we propose a priority-based selection method for the local search that can be consistently integrated with any MA for DDWSC. This enables us to develop state-of-the-art algorithms for DDWSC by explicitly considering the problem-specific, population and solution-related information for choosing a solution. Extensive experimental evaluation using benchmark datasets shows that our proposed method significantly outperforms several recently proposed methods. [ABSTRACT FROM AUTHOR]

Phylogenetic analyses commonly assume that the species history can be represented as a tree. However, in the presence of hybridization, the species history is more accurately captured as a network. Despite several advances in modeling phylogenetic networks, there is no known polynomial-time algorithm for parsimoniously reconciling gene trees with species networks while accounting for incomplete lineage sorting. To address this issue, we present a polynomial-time algorithm for the case of level-1 networks, in which no hybrid species is the direct ancestor of another hybrid species. This work enables more efficient reconciliation of gene trees with species networks, which in turn, enables more efficient reconstruction of species networks. [ABSTRACT FROM AUTHOR]

The article views the forthcoming virtual societies, decentralized society (DeSoc) or metaverse, as cyber-physical social systems, and discusses the key issue of management for such human-centered hybrid systems—the prescription of human behaviors. We build parallel humans with its aggregation—parallel population—to complete that task, where each individual's mental knowledge is cognitively modeled by heterogeneous learning, analyzed by generative big data with deep evolutionary reasoning, and prescribed by knowledge convergence with an active recommendation. A case study from social security has validated that our parallel population and parallel human are a feasible and effective way to construct DeSoc or metaverse. [ABSTRACT FROM AUTHOR]

This article presents a generic motion model to capture mobile robots’ dynamic behaviors (translation and rotation). The model is based on statistical models driven by white random processes and is formulated into a full state estimation algorithm based on the error-state extended Kalman filtering framework (ESEKF). The major benefits of this method are its versatility, being applicable to different robotic systems without accurately modeling the robots’ specific dynamics, and the ability to estimate the robot’s (angular) acceleration, jerk, or higher order dynamic states with low delay. Mathematical analyses with numerical simulations are presented to show the properties of the statistical model-based estimation framework and reveal its connection to existing low-pass filters. Furthermore, a new paradigm is developed for robotic observability analysis by developing Lie derivatives and associated partial differentiation directly on manifolds. It is shown that this new paradigm is much simpler and more natural than existing methods based on quaternion parameterizations. It is also scalable to high-dimensional systems. A novel thin set concept is introduced to characterize the unobservable subset of the system states, providing the theoretical foundation to observability analysis of robotic systems operating on manifolds and in high dimension. Finally, extensive experiments, including full state estimation and extrinsic calibration (both POS-IMU and IMU-IMU) on a quadrotor unmanned aerial vehicle (UAV), a handheld platform, and a ground vehicle, are conducted. Comparisons with existing methods show that the proposed method can effectively estimate all extrinsic parameters, the robot’s translation/angular acceleration, and other state variables (e.g., position, velocity, and attitude) with high accuracy and low delay. [ABSTRACT FROM AUTHOR]

We present a framework for fast synthesizing indoor scenes, given a room geometry and a list of objects with learnt priors. Unlike existing data-driven solutions, which often learn priors by co-occurrence analysis and statistical model fitting, our method measures the strengths of spatial relations by tests for complete spatial randomness (CSR), and learns discrete priors based on samples with the ability to accurately represent exact layout patterns. With the learnt priors, our method achieves both acceleration and plausibility by partitioning the input objects into disjoint groups, followed by layout optimization using position-based dynamics (PBD) based on the Hausdorff metric. Experiments show that our framework is capable of measuring more reasonable relations among objects and simultaneously generating varied arrangements in seconds compared with the state-of-the-art works. [ABSTRACT FROM AUTHOR]

With the advent of electric vehicles (EVs), issues connected to the electric vehicle charging scheduling (EVCS) problem, which is $\textsf {NP}$ -hard, have become important. In previous studies, EVCS has been mainly formulated as a constrained shortest-path problem; however, such formulations have not involved variables such as charging rates, traffic congestion, scalability, and waiting time at charging station (CS), that need to be considered in practical settings. Earlier research has also tended to focus on the strengths of particular evolutionary optimization algorithms like differential evolution (DE) or particle swarm optimization (PSO) over others or traditional mathematical programming methods, with only a limited study of hybrid approaches. In this paper, fast and slow charging options at a station are considered in the EVCS problem for practical use. In previous studies, EVs have been considered to have fixed speeds; however, in order to mitigate CS congestion and thus waiting times at CSs, dynamic speed control of EVs has been considered in this work. This work also investigates the scalability of different EVCS solutions. A hybrid approach using PSO and the Firefly algorithm (FFA) with a Lévy flights search strategy is designed and implemented to solve the EVCS. Also, different hybrid methods variants of PSO and FFA have been evaluated in this paper to find the best performing hybrid variant. Experimental results validate the effectiveness of our approach on both synthetic and the real-world transportation networks. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computational tasks are offloaded from UEs to the base station (BS) at the edge of the network for processing. However, MEC cannot cope with environments where there are no BS or where communication facilities have been destroyed. In this paper, we study the problem of minimizing the energy consumption of UAV equipped with MEC servers as a mobile base station to serve users. The problem involves user offloading decision, UAV location and allocation with computational resources, and is a hybrid optimization problem with continuous and discrete variables. To address this problem, we propose a hybrid nature-inspired optimization algorithm (HNIO) and its version for discrete optimization, where HNIO incorporates mutation and population diversity detection mechanisms to boost its global optimization capability, and we design a probabilistic selection-based coding strategy for the discrete optimization version. The experimental study is conducted based on ten cases with different numbers of UEs. Comparing HNIO with several other state-of-the-art optimization algorithms, it is concluded from the Friedman and Wilcoxon’s test of the experimental results that HNIO shows better precision and stability in nine out of the ten cases with higher number of UEs. [ABSTRACT FROM AUTHOR]

Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this article proposes a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction. The proposed algorithm can switch between the two stages according to the status of the current population, enabling the population to cross the infeasible region and reach the feasible regions in one stage, and to spread along the feasible boundaries in the other stage. Experimental studies on four benchmark suites and three real-world applications demonstrate the superiority of the proposed algorithm over the state-of-the-art algorithms, especially on problems with complex feasible regions. [ABSTRACT FROM AUTHOR]

The clique partitioning problem (CPP) of an edge-weighted complete graph is to partition the vertex set $V$ into $k$ disjoint subsets such that the sum of the edge weights within all cliques induced by the subsets is as large as possible. The problem has a number of practical applications in areas, such as data mining, engineering, and bioinformatics, and is, however, computationally challenging. To solve this NP-hard problem, we propose the first evolutionary algorithm that combines a dedicated merge–divide crossover operator to generate offspring solutions and an effective simulated annealing-based local optimization procedure to find high-quality local optima. The extensive experiments on three sets of 94 benchmark instances (including two sets of 63 classical benchmark instances and one new set of 31 large benchmark) show a remarkable performance of the proposed approach compared to the state-of-the-art methods. We analyze the key algorithmic ingredients to shed light on their impacts on the performance of the algorithm. The algorithm and its available source code can benefit people working on practical problems related to CPP. [ABSTRACT FROM AUTHOR]

Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of antibody-bound cells, but the manual interpretation of large scale flow cytometry data has long been a time-consuming and complicated task for hematologists and laboratory professionals. Past studies have led to the development of representation learning algorithms to perform sample-level automatic classification. In this work, we propose a chunking-for-pooling strategy to include large-scale FC data into a supervised deep representation learning procedure for automatic hematologic malignancy classification. The use of discriminatively-trained representation learning strategy and the fixed-size chunking and pooling design are key components of this framework. It improves the discriminative power of the FC sample-level embedding and simultaneously addresses the robustness issue due to an inevitable use of down-sampling in conventional distribution based approaches for deriving FC representation. We evaluated our framework on two datasets. Our framework outperformed other baseline methods and achieved 92.3% unweighted average recall (UAR) for four-class recognition on the UPMC dataset and 85.0% UAR for five-class recognition on the hema.to dataset. We further compared the robustness of our proposed framework with that of the traditional downsampling approach. Analysis of the effects of the chunk size and the error cases revealed further insights about different hematologic malignancy characteristics in the FC data. [ABSTRACT FROM AUTHOR]

Low-activity signals, such as voice, electrocardiogram (ECG), and ultrasonic signals, in the Internet-of-Things applications have both posed unique challenges and offered special opportunities for modern analog-to-digital conversion. This article presents a new successive approximation register (SAR) analog-to-digital converter (ADC) search methodology, which is aimed at low-activity signals for reducing comparator activity and switching energy of digital-to-analog converter (DAC). By using statistical histogram information of the low-activity signals, two search solutions are proposed. The first solution is designed for some part of signal that has small difference between two adjacent samples, while the second solution is designed for that with large difference. To engage one suitable solution, the digital interval between two adjacent samples needs to be detected. In addition, a new DAC tactic is proposed to reduce the activity of DAC switches. Our simulated 10-bit SAR ADC for voice signals shows that by using our proposed method, the comparator activity is reduced by 62.09%, and the DAC switching energy is decreased by 85.90% compared to the monotonic method. In addition, the activity of DAC switches is further trimmed by 39.66% compared to the monotonic method. [ABSTRACT FROM AUTHOR]

For high-volume manufacturing, yield estimation is an important design step to determine the effects of uncertainties in the fabrication process. The tolerances associated with the fabrication process are applied to the statistically significant system parameters, and a Monte Carlo (MC) simulation is historically done to accurately estimate the yield. This process becomes computationally very expensive when the number of statistically significant system parameters are either too difficult to intuitively determine or are too high. A nonlinear partial-least-squares-based polynomial chaos expansion (NLPLSs-based PCE) is proposed as a solution for complex antenna yield analysis. NLPLS-based PCE effectively reduces the system dimensionality using NLPLS and simultaneously extracts the statistical information on the same sample set, that is, yield, using PCE. It is also possible to perform a global sensitivity analysis using NLPLS-based PCE surrogates, providing an additional advantage. This method is illustrated using an eight-variable single-frequency patch antenna, an eight-variable dual-band patch antenna, and a 37-variable diplexer requiring 30, 10, and 30 analysis points, respectively, to obtain converged yield estimates. [ABSTRACT FROM AUTHOR]

In Turkey, refugees from Syria have been continuously increasing with rapid urbanization. Concurrently, energy consumption has been growing promptly. Supply and demand equality may be a crucial problem in the electricity market causing further instability and shortages leading to reliable predictions a necessity. For this reason, an informative questionnaire is prepared to take the expert opinions on the main drivers that shape the energy consumption behavior of Syrian refugees. In total, 17 experts are interviewed. The answers are explored by using NETICA software for Bayesian Belief Networks. Factors affecting energy consumption behavior and also strategies, institutions, tools, and financing methods that change the behavior toward efficient consumption are investigated. Based on obtained results, it can be claimed that to be successful, electricity prices and tariffs in the market should be arranged in the favor of energy efficiency. Besides, support mechanisms by not only the government but also by the municipalities should be considered while setting related policies. Also, electric appliance producers should develop and implement strategies and actions to encourage the usage of more efficient appliances. Last but not the least, nongovernmental organizations should raise refugees’ awareness of the efficient electricity consumption to minimize the impacts of refugee inflows on the electricity market. [ABSTRACT FROM AUTHOR]

Fusion energy has the potential to address long-term energy requirements and climate change. However, fusion energy is characterized by a “development divide” between the International Thermonuclear Experimental Reactor (ITER) consortium members and the non-ITER “Global South” states. It is beset with problems, which can be divided into geopolitical, geo-economic, geo-sociocultural, and geo-technological (GEO-PEST). Geopolitical problems include cooperation on fusion energy development between ITER members (of the Global North) and the Global South on fusion development. Geo-economic problems include the cost of funding ITER versus newly emerging private-sector fusion companies. Geo-sociocultural problems include the requirement to maintain the peacebuilding tradition of nuclear energy (embodied by the Cold War-era “Atoms for Peace” initiative), as well as public perceptions of radiation. Geo-technological problems include tokamak lock-in, fuel type, the viability of “compact reactors,” and disruptive technology events. In this article, we outline these problems and discuss how to address them in a timely fashion via an external independent review mechanism, modeled on the International Energy Agency's “Global Commission for Urgent Action on Energy Efficiency,” established in 2019 and due to deliver recommendations in 2020. [ABSTRACT FROM AUTHOR]

We humorously discuss 12 fallacies when focusing on compute performance that we have frequently observed in practice. We follow each with a recommendation to mitigate the danger and hope to contribute to good benchmarking etiquette for data science. [ABSTRACT FROM AUTHOR]

This article addresses the measurement of the power spectrum of red noise processes at the lowest frequencies, where the minimum acquisition time is so long that it is impossible to average on a sequence of data record. Therefore, averaging is possible only on simultaneous observation of multiple instruments. This is the case of radio astronomy, which we take as the paradigm, but examples may be found in other fields such as climatology and geodesy. We compare the Bayesian confidence interval of the red noise parameter using two estimators, the spectrum average and the cross-spectrum. While the spectrum average is widely used, the cross-spectrum using multiple instruments is rather uncommon. With two instruments, the cross-spectrum estimator leads to the Variance–Gamma distribution. A generalization to ${q}$ devices based on the Fourier transform of characteristic functions is provided, with the example of the observation of millisecond pulsars with five radio telescopes (RTs). The simulations show that the spectrum average is by a small amount more efficient than the cross-spectrum, chiefly when the background exceeds the signal. However, some notable differences between their upper limit indicate that it should be wiser to compute both estimators. [ABSTRACT FROM AUTHOR]

The electronic devices working inside the enclosure will affect the internal electromagnetic (EM) environment, and their radiated EM energy will also be coupled out through the apertures, which could affect the surrounding equipment. In this article, the built-in transmission line and printed circuit board are considered, respectively, to analyze their influence on the deterministic and statistical distribution of the internal EM environment at different frequencies and positions. Second, based on the radiation power of the built-in device, combined with the coupling cross-section of the aperture, the statistical method is used to quickly calculate the leaking power outward the perforated enclosure. Finally, a three-dimensional positioning system is developed to measure the statistical characteristics of the internal electric field and the outward radiation power, which are in good agreement with the calculation ones. It is revealed that when the device is working at a high frequency, the field modes excited simultaneously increases, the complexity of the EM environment increases, and the field distribution is complex and chaotic. The change of the frequency or position will cause drastic changes in the deterministic field distribution. However, from the probability density functions of the electric field, the position of the built-in device has little effect on the statistical characteristics. [ABSTRACT FROM AUTHOR]

Secondary craters (i.e., secondaries) formed by the landing of impact ejecta are an important crater population in planetary geology. Typical secondaries on planetary surfaces occur in chains and clusters, and they have irregular-shaped crater rims and/or floors. Background secondaries are a special type of secondaries that were formed by more-dispersed impact ejecta, and they have similarly circular morphology and dispersed spatial distribution with same-sized primary craters (i.e., primaries) that are formed by asteroids and comets. While individual background secondaries have been recognized at different planetary bodies, there is no reliable method to differentiate populations of background secondaries from those of primaries. In this work, we design a novel method to extract background secondaries based on multiscale and multifacies topography characteristics of impact craters. To characterize the complex topography fluctuations across continuous ejecta deposits of secondaries, we design a multiwavelet method to present their full-wavelength topography variations. Selecting three populations of secondaries formed by the Orientale basin and a population of primaries, we investigate and compare their crater interior slopes and roughness of continuous ejecta deposits, determining the threshold ranges that can differentiate background secondaries from same-sized and coeval primaries. The two sets of threshold values are then fused together to create a standard T-score terrain indicator, which are combined with our modeled theoretical spatial distribution of secondaries, and abundant previously unrecognized Orientale background secondaries were discovered. Our results demonstrate that the production of background secondaries is much more efficient than manifested by the observed spatial density of obvious secondaries in chains and clusters. This study establishes a promising method to detect populations of background secondaries, which can be extended further to smaller primary craters on various airless planetary bodies. [ABSTRACT FROM AUTHOR]

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire datasets based on Earth observation satellites, our dataset combines 2-D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, and population density) aligned over 2-D regions, providing a feature-rich dataset for machine learning. To demonstrate the usefulness of this dataset, we implement a neural network that takes advantage of the spatial information of these data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This dataset can be used as a benchmark for developing wildfire propagation models based on remote-sensing data for a lead time of one day. [ABSTRACT FROM AUTHOR]

Since 2010, a lot of progress was done concerning Small Angle X-ray Scattering (SAXS) extraction capabilities for dimensional control of line gratings. In this paper, we summarize the general methodology, including transmission (T-)SAXS experimental setup, and extract the line shape and roughness in line gratings. We describe step by step the full data treatment to extract the pitch, critical dimension (CD), line shape and roughness, focusing on the Line Width Roughness (LWR) extraction through a Power Spectral Density (PSD) analysis. The reliability of the extraction by T-SAXS is evaluated thanks to different extractions, comparison to TEM cross-section and analyses of count statistic in case of line roughness. [ABSTRACT FROM AUTHOR]

This work investigates a random access (RA) game for a time-slotted RA system, where $N$ players choose a set of slots of a frame and each frame consists of $M$ multiple time slots. We obtain the pure strategy Nash equilibria (PNEs) of this RA game, where slots are fully utilized as in the centralized scheduling. As an algorithm to realize a PNE (Pure strategy Nash Equilibrium), we propose an Exponential-weight algorithm for Exploration and Exploitation (EXP3)-based multi-agent (MA) learning algorithm, which has the computational complexity of $O(N N_{\max }^{2} T)$. EXP3 is a bandit algorithm designed to find an optimal strategy in a multi-armed bandit (MAB) problem that users do not know the expected payoff of each strategy. Our simulation results show that the proposed algorithm can achieve PNEs. Moreover, it can adapt to time-varying environments, where the number of players varies over time. [ABSTRACT FROM AUTHOR]

In massive MIMO systems, the high complexity of signal detection comes mainly from computing a Gram matrix and its inversion. In this correspondence, we propose a low-complexity MIMO detection method based on the Neumann series (NS), which does not require explicitly computing the Gram matrix and its inversion. The proposed method leverages the statistical information of the Gram matrix to compute an initialization matrix, which guarantees the convergence of NS expansion. The signal detection is conducted implicitly by transforming matrix inversion and product operations into matrix-vector multiplication operations. By doing so, it reduces the computational complexity of MIMO signal detection from $\mathcal {O}(BU^{2}+U^{3})$ to $\mathcal {O}(BUN)$ , where $B$ , $U$ , and $N$ are the numbers of antennas at the base station, at the user equipment, and NS terms, respectively. Simulation results show that, compared with existing approaches, the proposed method can significantly reduce the signal detection complexity while achieving similar performance. [ABSTRACT FROM AUTHOR]

Contribution: Screening rates for engineering students for several major and moderate mental health issues are reported, including unspecified psychological distress as captured by the Kessler 6 screening instrument; screening rates for depressive, anxiety, and eating disorders as measured by the patient health questionnaire (PHQ); and screening rates for post-traumatic stress disorder (PTSD) as measured by the primary care post-traumatic stress disorder (PC-PTSD) instrument. This work also explores how mental health issues affect different student demographic groups within engineering. Background: Anecdotal evidence has long suggested that stress and certain mental health issues are particularly acute in the field of Engineering, and some recent research has shown elevated rates of mental health issues at different institutions around the country. This article presents the results of a previously validated mental health survey conducted with first- and second-year students at eight universities. Intended Outcomes: A better understanding of which mental health issues affect engineering students as a population, and an understanding of what mental health disparities exist among different demographics in engineering. This information is intended to allow engineering programs, student groups, and other stakeholders to better target mental health resources for all engineering students. Application Design: This work combines several widely used population-scale mental health diagnostic tools into a single comprehensive survey instrument that was deployed to first- and second-year engineering students at eight universities nationwide. Findings: This study finds that 50% of respondents screening positive for a major mental health condition—including depression, anxiety, PTSD, an eating disorder, or major psychological distress—while only 16% of respondents report having ever received a diagnosis for a mental health condition. Women respondents are more likely to screen positive for anxiety disorders ($4.4\times $ for panic disorder, $2.2\times $ for other anxiety, and $1.9\times $ for PTSD) and major depressive disorder ($2.3\times $) relative to men. Respondents reporting physical disabilities have significantly higher likelihoods of suffering from mental health issues than peers with no reported physical disabilities and are $2.9\times $ more likely to screen positive for PTSD. Identifying as Hispanic was also a significant predictor of major depressive disorder ($3.2\times $ more likely) and PTSD ($2.5\times $ more likely). [ABSTRACT FROM AUTHOR]

Contribution: The present research provides new evidence on the predictors of foreign-born (FB) students’ dropout and the situations of discrimination that emerge on engineering campuses. Background: Ethnic minority students remain underrepresented in engineering degrees and have higher dropout rates than their White peers. Previous literature pointed out discrimination as a possible cause of these disparities. However, little is known about the factors that trigger these discriminations and to which extent discrimination can predict students’ dropout. Knowing this information will help institutions and governments establish support plans for underrepresented students to increase their participation in engineering degrees. Research Questions: 1) Do native-born (NB) and FB students enrolled in engineering degrees differ in their levels of academic satisfaction, self-esteem, perceived discrimination, and dropout intentions? 2) What factors predict the likelihood that FB students would drop out of engineering degrees? 3) What main factors make FB students feel discriminated against on engineering campuses? Methodology: A sample of 602 undergraduate engineering students answered a questionnaire, and descriptive and inferential statistics were performed. Findings: FB students reported significantly lower self-esteem than NB students. Contrary to what was expected, FB students did not report significantly higher dropout intentions and perceived discrimination than NB students; possible reasons are discussed. Academic satisfaction, self-esteem, and perceived discrimination were significant predictors of FB students’ dropout intentions, with perceived discrimination being the strongest predictor in students most at risk of dropping out. Finally, the study identifies the most relevant reasons, situations, and people causing discrimination on engineering campuses. [ABSTRACT FROM AUTHOR]

Contribution: This work provides evidence of various approaches to studying longitudinal student unit record data in undergraduate education in the USA and the outcomes that can be realized using a large multi-institutional longitudinal dataset, Multiple-Institution Database for Investigating Longitudinal Development (MIDFIELD). Background: Cross-sectional studies introduce a variety of sources of error in estimating student pathways and outcomes. Longitudinal outcomes that ignore pathways also miss important information, and some populations are systematically excluded (such as transfer students). Intended Outcomes: By providing examples of how longitudinal student unit-record data can be analyzed and the results that can be expected, this work aims to deepen the research toolbox in engineering education. Findings: MIDFIELD is being used to support studies of demographic and financial trends among universities in the southeastern USA, required math and science course grades and disciplinary cultures, time to find graduation major, educational data mining, and applications of selected advanced models. [ABSTRACT FROM AUTHOR]

Contribution: In the research presented in this article, a procedure for determining student workload has been designed, tested, and validated. This procedure also makes it possible to analyze how different teaching variables are affected by workload and to visualize the learning environment generated in the course. Background: When the project-based learning (PBL) methodology is used in a course, if the students’ workload is not properly planned and controlled, important variables in the educational environment, such as the teaching organization and the students’ approach to learning, can be affected. The PBL methodology may even become unfeasible as it affects the other courses with which it shares year and semester. This justifies the need to know the student workload of a subject. The previous research has presented procedures for establishing student workload. However, they use questionnaires with a high number of questions. A questionnaire with many questions affects the reliability of students’ answers. The questionnaire presented in this article reduces the number of questions without affecting the results obtained and it is easily applicable to other courses. Research Questions: The questions to be answered in this research are: 1) is the workload of the students, when using the PBL methodology in a course, in line with the one established in the syllabus? and 2) is the learning environment created in the course as expected? Methodology: The methodology used to obtain the necessary data is based on students completing weekly surveys, two critical incident questionnaires (CIQs), and a final survey. The aim was always to minimize the number of questions to be answered, ensuring that the data collected provided a true picture of the workload and the educational environment. Findings: The analysis of the data obtained by applying the procedure provides mechanisms to control the workload, ensure involvement and motivation, improve students’ academic results and show the learning environment created in the course. The implementation of this method, during three academic years, has allowed to test its effectiveness, validate some of the initial hypotheses, and confirm the conclusions of some previous studies. [ABSTRACT FROM AUTHOR]

Contribution: This study contributes to efforts to diversify the field of engineering by studying the influence of co-curricular activities on African American students’ development of key nontechnical professional skills. Background: The 21st Century workforce requires significant collaboration and communication. For engineering graduates to meet workforce challenges, they must graduate with nontechnical skills. This study operationalized these skills using traits identified in the National Academy of Engineering’s (NAE) “Engineer of 2020” report. The NAE also points to the urgent need for the United States to diversify its workforce; broadening the participation of African American engineers is key to doing so. Co-curricular activities help students develop nontechnical professional skills and are particularly important to African Americans at predominantly White institutions (PWIs). Research Question: How do African American engineering students attending PWIs in the United States develop nontechnical professional skills through participation in ethnic-specific co-curricular activities? Methodology: This qualitative study explored the lived experiences of 16 participants from one of six engineering disciplines. Each participant was a member of at least one of the following organizations: a Black fraternity or sorority (termed Black Greek Letter organizations), their campus student chapter of the National Society of Black Engineers, or an institution-sponsored program for racially underrepresented students such as a minority engineering program. Findings: Data analysis revealed significant evidence that involvement in one or more of the studied ethnic-specific co-curricular activities enhanced African American engineering students’ educational experiences by providing resources and opportunities to help them develop professional skills. [ABSTRACT FROM AUTHOR]

Contributions: The underrepresentation of women in computer science (CS) majors has long been a focus of attention in many academic documents, the majority of them from the United States and Europe. There is, however, a lack of information about educational interventions (EIs) for women in computing in Latin America. The contribution of this article is to cover this gap and describe what researchers in Latin American countries have been publishing about the recruitment and retention of women in the CS field. Background: Many EIs targeting female students at different educational levels—K-12, undergraduate, and graduate—have been undertaken to increase the participation of women in computing in Latin America. However, descriptions of these activities rarely are included in international academic databases. Research Questions: This literature mapping addresses two main research questions (RQ) about the topic of women in computing in academic publications in Latin American countries: RQ1) what are the characteristics of the publications about women in computing in Latin America? and RQ2) what are the published interventions to recruit and retain women in computing in Latin America? To answer RQ1, six subquestions were created covering year, language, country of origin, document type, and professional track. Furthermore, for RQ2, two subquestions were created involving educational level and the use of software development with a female theme. Methodology: This investigation used the systematic literature mapping process. To achieve a broad coverage of papers, the following sources were included: Scopus, Web of Science, Google Scholar, EBSCO, the proceedings of the Latin American Women in Computing Conference (LAWCC), and those of the Women in Technology (WIT) workshop colocated with the annual conference of the Brazilian Computer Society (SBC). The included papers were published in the last decade (2010–2020) and written in English, Portuguese, or Spanish. Findings: The literature mapping encompasses 197 academic documents, 48.2% of which were written in Portuguese, 28.7% in English, and 23.1% in Spanish. The papers originated from 15 of the 20 Latin American countries. Brazil and Costa Rica have the highest number of publications overall. The documents describe initiatives to increase the participation of women in computing majors that cover the entire educational spectrum, from K-12 to graduate programs, but papers targeting populations in higher education have started to appear recently. [ABSTRACT FROM AUTHOR]

The energy-efficient flexible job shop scheduling problem (FJSP) has attracted much attention in deterministic cases; however, uncertainty is seldom incorporated into energy-efficient FJSP and the neglecting of uncertainty will greatly diminish the application value of scheduling results. These make it necessary to handle uncertainty in the problem. In this study, energy-efficient fuzzy FJSP (EFFJSP) is considered and a bi-population evolutionary algorithm with feedback (FBEA) is proposed to minimize fuzzy makespan and fuzzy total energy consumption and maximize minimum agreement index. The computation of fuzzy energy consumption is given and four heuristics are proposed to produce the initial population. An effective method is presented to evaluate the quality of two populations and a feedback mechanism based on population quality is adopted to dynamically adjust the size of each population. A novel process of reproduction, crossover and mutation is developed based on feedback. An enhanced local search is also used to produce high-quality solutions. Extensive experiments are conducted to test the performance of FBEA. FBEA can provide promising results for EFFJSP. [ABSTRACT FROM AUTHOR]

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed. [ABSTRACT FROM AUTHOR]

Existing shadow removal methods often struggle with two problems: color inconsistencies in shadow areas and artifacts along shadow boundaries. To address these two problems, we propose a novel shadow mask-based semantic-aware network (S2Net) that uses shadow masks as guidance for shadow removal. The color inconsistency problem is solved in two steps. First, we use a series of semantic-guided dilated residual (SDR) blocks to transfer statistical information from non-shadow areas to shadow areas. The shadow mask-based semantic transformation (SST) operation in SDR enables the network to remove shadows while keeping non-shadow areas intact. Then, we design a refinement block by incorporating semantic knowledge of shadow masks and applying the learned modulated convolution kernels to get traceless and consistent output. To remove artifacts along shadow boundaries, we propose a newly designed boundary loss. The boundary loss encourages spatial coherence around shadow boundaries. By including the boundary loss as part of the loss function, a significant portion of artifacts along shadow boundaries can be removed. Extensive experiments on the ISTD, ISTD+, SRD and SBU datasets show our S2Net outperforms existing shadow removal methods. [ABSTRACT FROM AUTHOR]

We develop new approaches in multi-class settings for constructing loss functions and establishing corresponding regret bounds with respect to the zero-one or cost-weighted classification loss. We provide new general representations of losses by deriving inverse mappings from a concave generalized entropy to a loss through a convex dissimilarity function related to the multi-distribution $f$ -divergence. This approach is then applied to study both hinge-like losses and proper scoring rules. In the first case, we derive new hinge-like convex losses, which are tighter extensions outside the probability simplex than related hinge-like losses and geometrically simpler with fewer non-differentiable edges. We also establish a classification regret bound in general for all losses with the same generalized entropy as the zero-one loss, thereby substantially extending and improving existing results. In the second case, we identify new sets of multi-class proper scoring rules through different types of dissimilarity functions and reveal interesting relationships between various composite losses currently in use. We also establish new classification regret bounds in general for multi-class proper scoring rules and, as applications, provide simple meaningful regret bounds for two specific sets of proper scoring rules. These results generalize, for the first time, previous two-class regret bounds to multi-class settings. [ABSTRACT FROM AUTHOR]

Food is one of the basic essential elements for human beings. Its quality is directly related to our physical, emotional, and social well-being. An electronic nose is a non-destructive instrument and is an effective solution for quick and easy determination of food quality. In this paper, an integrated soft E-nose methodology focusing on performance enhancement and complexity reduction is proposed and implemented for the effective classification of beef quality and prediction of microbial population in beef. The soft E-nose methodology is tested on 18 available datasets. Ours is a four-step approach that includes validated data collection, signal processing with a moving average filter and principal component analysis for feature reduction, efficient pattern recognition using machine learning and deep learning techniques like support vector machine, k-nearest neighbors, artificial neural network, extreme learning machine, and deep neural network for classification and regression, and an output stage for classification and prediction of the microbial population of beef. This methodology could provide accuracy greater than 98% using k-nearest neighbors, artificial neural network methods, and a correlation coefficient of greater than 0.98 using support vector machine and artificial neural network methods. The proposed methodology is validated using 10-fold cross-validation. [ABSTRACT FROM AUTHOR]

A museum hybrid space combines physical artifacts co-located with virtual and augmented reality displays. Although the technology exists to provide museums with hybrid space, there are no empirical studies on effectiveness of the museum hybrid space in terms of learning and enjoyment. This article takes an experimental approach and measures the enjoyment and learning (dependent variables) of participants in response to selected environments (independent variables) including a traditional environment (based on photos and labels), a video-enhanced environment (based on projected video clips), and a VR-enhanced environment (based on video game). The main outcome of this article is demonstrating that the use of VR technology and the resulting hybrid space (i.e., VR-enhanced environment) results in novel museum experiences that provide greater impacts on audience in terms of learning and enjoyment. [ABSTRACT FROM AUTHOR]

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. In it, each city has one to multiple colors and allows a salesman in the same color to visit exactly once. This work presents for the first time a CTSP whose edge weights among the cities change over time. It can be applied to dynamic routing problems arising in logistic distribution systems with various goods accessibilities to different types of vehicles. A non-linear integer mathematical program is constructed. A Variable Neighborhood Search (VNS) algorithm with a direct-route encoding and random initialization is then presented to solve it. To increase the convergence rate and population diversity of VNS, it is further combined with two-stage greedy initialization and an appropriate population immigrant scheme to perform the population search in a dynamic environment. Then, off-line performance evaluation and Wilcoxon test of the proposed algorithms are performed. The results show that their solution quality is improved by 30% ~ 60% over the basic VNS’s. They can track the environmental changes of CTSP-VEW more rapidly and effectively. In the end, a case study with a practical scenario is conducted. [ABSTRACT FROM AUTHOR]

The population of electric vehicles (EVs) has grown rapidly over the past decade due to the development of EV technologies, battery materials, charger facilities, and public charging services. Many governments have implemented plans to ban fossil fuel vehicles considering the significance of EVs in reducing greenhouse gas emissions. However, due to the battery material characteristics and charger power limitations, the EV charging process requires more time than is needed to fill a non-EV with fuel at a gasoline station, causing drivers to experience range anxiety and impeding the promotion of EVs. Hence, the battery swapping station (BSS) model has been proposed as an alternative method. Recently, researchers have studied the BSS approach by proposing various operation systems and optimization methods, and BSS service operators have successfully implemented swapping at commercial and private stations. This paper reviews the state-of-the-art BSS literature and business models, where the BSS offers a recharged battery to an incoming EV with a low state-of-charge. First, four operation modes are presented: a single BSS, multiple BSSs, an integrated BSS and battery charging station (BCS), and multiple BSSs and BCSs. Then, the BSS decision scenarios are surveyed in relation to five operational areas, i.e., charging schedule, service policy, construction and planning, dispatching and routing, and power management, where the scenarios are compared in terms of the BSS mode, decision maker, EV category, number of battery types, vehicle to grid, and focus and objective. Finally, the survey concludes with a discussion of several future research directions for EV BSSs. [ABSTRACT FROM AUTHOR]

The capability of traffic-information systems to sense the movement of millions of users and offer trip plans through mobile phones has enabled a new way of optimizing city traffic dynamics, turning transportation big data into insights and actions in a closed-loop and evaluating this approach in the real world. Existing research has applied dynamic Bayesian networks and deep neural networks to make traffic predictions from floating car data, utilized dynamic programming and simulation approaches to identify how people normally travel with dynamic traffic assignment for policy research, and introduced Markov decision processes and reinforcement learning to optimally control traffic signals. However, none of these works utilized floating car data to suggest departure times and route choices in order to optimize city traffic dynamics. In this paper, we present a study showing that floating car data can lead to lower average trip time, higher on-time arrival ratio, and higher Charypar-Nagel score compared with how people normally travel. The study is based on optimizing a partially observable discrete-time decision process and is evaluated in one synthesized scenario, one partly synthesized scenario, and three real-world scenarios. This study points to the potential of a “living lab” approach where we learn, predict, and optimize behaviors in the real world. [ABSTRACT FROM AUTHOR]

In the past several years, it has become apparent that the effectiveness of Pareto-dominance-based multiobjective evolutionary algorithms deteriorates progressively as the number of objectives in the problem, given by $M$ , grows. This is mainly due to the poor discriminability of Pareto optimality in many-objective spaces (typically $M\geq 4$). As a consequence, research efforts have been driven in the general direction of developing solution ranking methods that do not rely on Pareto dominance (e.g., decomposition-based techniques), which can provide sufficient selection pressure. However, it is still a nontrivial issue for many existing non-Pareto-dominance-based evolutionary algorithms to deal with unknown irregular Pareto front shapes. In this article, a new many-objective evolutionary algorithm based on the generalization of Pareto optimality (GPO) is proposed, which is simple, yet effective, in addressing many-objective optimization problems. The proposed algorithm used an “($M-1$) + 1” framework of GPO dominance, ($M-1$)-GPD for short, to rank solutions in the environmental selection step, in order to promote convergence and diversity simultaneously. To be specific, we apply $M$ symmetrical cases of ($M-1$)-GPD, where each enhances the selection pressure of $M-1$ objectives by expanding the dominance area of solutions, while remaining unchanged for the one objective left out of that process. Experiments demonstrate that the proposed algorithm is very competitive with the state-of-the-art methods to which it is compared, on a variety of scalable benchmark problems. Moreover, experiments on three real-world problems have verified that the proposed algorithm can outperform the others on each of these problems. [ABSTRACT FROM AUTHOR]

Effective constraint handling techniques (CHTs) are of great significance for evolutionary algorithms (EAs) dealing with constrained optimization problems (COPs). To date, many CHTs, such as penalty function, superiority of feasible solutions, and $\epsilon $ -constraint (EC), have been designed. However, different CHTs are usually suited to different problems, even the most appropriate technique changes along with the stages of the optimization process. Motivated by this phenomenon, we propose a voting-mechanism-based ensemble framework, named voting mechanism for constraint handling (VMCH), to integrate multiple CHTs for solving various COPs. In this framework, each CHT acts as a voter, all voters vote for each pair of solutions, and the solution in each pair with the highest weighted votes is considered better. In addition, an adaptive strategy is developed to adjust the voter weights according to their historical voting performance. To investigate the performance of VMCH in improving existing algorithms, the proposed VMCH is embedded into the three best algorithms in the competition on constrained single objective real-parameter optimization at CEC 2018, namely, MAgES, iLSHADE $\epsilon $ , and IUDE, to form three new algorithm versions, i.e., MAgES-VMCH, iLSHADE $\epsilon $ -VMCH, and IUDE-VMCH. They are compared with seven state-of-the-art peer algorithms. Extensive experiments are conducted on 57 real-world COPs. The ranking results show that the new algorithm version MAgES-VMCH takes first place among the ten comparison algorithms. Moreover, all the new VMCH-enhanced versions of the three best algorithms are superior to their original versions. Therefore, the proposed VMCH framework can achieve competitive performance in solving COPs. [ABSTRACT FROM AUTHOR]

Subset selection plays an important role in the field of evolutionary multiobjective optimization (EMO). Especially, in an EMO algorithm with an unbounded external archive (UEA), subset selection is an essential post-processing procedure to select a prespecified number of solutions as the final result. In this article, we discuss the efficiency of greedy subset selection for the hypervolume, inverted generational distance (IGD), and IGD plus (IGD+) indicators. Greedy algorithms usually efficiently handle the subset selection. However, when a large number of solutions are given (e.g., subset selection from tens of thousands of solutions in a UEA), they often become time consuming. Our idea is to use the submodular property, which is known for the hypervolume indicator, to improve their efficiency. First, we prove that the IGD and IGD+ indicators are also submodular. Next, based on the submodular property, we propose an efficient greedy inclusion algorithm for each indicator. We demonstrate through computational experiments that the proposed algorithms are much faster than the standard greedy subset selection algorithms. The proposed algorithms also help the research on performance indicators. [ABSTRACT FROM AUTHOR]

Knowledge transfer plays a vastly important role in solving multitask optimization problems (MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality solution from one task to other tasks to enhance the optimization ability, which, however, may not work well or even have a negative effect if the tasks have very different task-specific knowledge. Hence, this article proposes a meta-knowledge transfer (MKT)-based differential evolution (MKTDE) algorithm by using a more general MKT method to solve MTOPs more efficiently. The meta-knowledge defined in this article refers to the knowledge that can evolve task-specific knowledge during the evolutionary search. That is, the meta-knowledge is a kind of “knowledge of knowledge,” which denotes the knowledge of “how to solve problem via evolution” and “the feature/way/method of evolving high-quality solution.” The evolutionary search for solving different tasks can share common meta-knowledge even though these tasks involve heterogeneous data and have very different task-specific knowledge. Therefore, the MKT can associate the heterogeneous multisource data of different tasks via transferring the meta-knowledge to help solve MTOPs more efficiently in a more general way. Moreover, to further enhance the MKTDE, two novel and efficient methods are proposed. One is multiple populations for the multiple tasks framework using a unified search space for making knowledge transfer flexibly. The other is an elite solution transfer method for achieving positive high-quality solution transfer. The superior performance of the proposed MKTDE is verified via extensive numerical experiments on both widely used MTOP benchmark problems and real-world robot navigation problems, with comparisons with some state-of-the-art and the latest well-performing algorithms. [ABSTRACT FROM AUTHOR]

The uncertain capacitated arc routing problem has many real-world applications in logistics domains. Genetic programming (GP) is a promising approach to training routing policies to make real-time decisions and handle uncertain events effectively. In the real world, there are various problem domains and no single routing policy can work effectively in all of them. Instead of training in isolation, we can leverage the relatedness between the problems and transfer knowledge from previously solved source problems to solve the target problem. The existing transfer methods are not effective enough due to the loss of diversity during the knowledge transfer. To increase the diversity of the transferred knowledge, in this article, we propose a novel GP method that removes phenotypic duplicates from the source individuals to initialize the target individuals. Furthermore, assuming that the transferred knowledge used in initialization already includes all the important knowledge explored for the source problem, it is more effective to explore new regions that have not been explored for the source problem. Therefore, we propose novel genetic operators that prohibit the search from revisiting the source individuals when solving the target problem. To speed up the revisit check, we propose to adapt a powerful hashing method for routing policies that greatly improves the efficiency of the genetic operators. Our experimental results show that the proposed method significantly outperforms the existing GP approaches with knowledge transfer in terms of both initial and final solution quality. [ABSTRACT FROM AUTHOR]