Search Area Research Articles

An Improved particle swarm optimization based on lévy flight and simulated annealing for high dimensional optimization problem

Particle swarm optimization (PSO) is a simple metaheuristic method to implement with robust performance. PSO is regarded as one of the numerous researchers' most well-studied algorithms. However, two of its most fundamental problems remain unresolved. PSO converges onto the local optimum for high-dimensional optimization problems, and it has slow convergence speeds. This paper introduces a new variant of a particle swarm optimization algorithm utilizing Lévy flight-McCulloch, and fast simulated annealing (PSOLFS). The proposed algorithm uses two strategies to address high-dimensional problems: hybrid PSO to define the global search area and fast simulated annealing to refine the visited search region. In this paper, PSOLFS is designed based on a balance between exploration and exploitation. We evaluated the algorithm on 16 benchmark functions for 500 and 1,000 dimension experiments. On 500 dimensions, the algorithm obtains the optimal value on 14 out of 16 functions. On 1,000 dimensions, the algorithm obtains the optimal value on eight benchmark functions and is close to optimal on four others. We also compared PSOLFS with another five PSO variants regarding convergence accuracy and speed. The results demonstrated higher accuracy and faster convergence speed than other PSO variants. Moreover, the results of the Wilcoxon test show a significant difference between PSOLFS and the other PSO variants. Our experiments' findings show that the proposed method enhances the standard PSO by avoiding the local optimum and improving the convergence speed.

MMOEA-SP: A multistage many-objective evolutionary algorithm based on sampling points

For the optimization algorithm based on Pareto dominance, how to obtain excellent performance in the high-dimensional objective space is a crucial issue. If the extreme solutions are correctly identified, the algorithm process can be accelerated. However, the existence of the dominance resistant solutions makes it suffer a devastating blow, especially when solving many-objective optimization problems. To address this issue, a multistage many-objective evolutionary algorithm based on sampling points (MMOEA-SP) is proposed. First of all, a state judgment strategy based on sampling points is developed to guide the population to explore or exploit, which divides the population into two stages and assigns different matching pools. The purpose of this strategy is to balance the convergence and diversity of the population. In the environment selection mechanism, two strategies are considered. On the one hand, a cluster-based dominance resistant solutions elimination strategy is introduced to accelerate population convergence and narrow the search area. On the other hand, solutions with larger differences are given priority as candidate solutions in the environmental selection mechanism to increase the diversity. The proposed algorithm is compared with plentiful state-of-the-art algorithms on a number of well-known benchmarks with 5–20 objectives. Finally, the experimental results show that the proposed algorithm can perform well on most of the test functions and generally outperforms its competitors.

Selection of Electrolyte Additive Quantities for Lithium‐Ion Batteries Using Bayesian Optimization

AbstractThe composition of the liquid electrolyte is a key factor in lifetime performance of lithium‐ion batteries. The selection and quantification of additives to the electrolyte is an active field of research. This study focuses on finding the optimal additive combination of fluoroethylene carbonate (FEC) and vinylene carbonate (VC) for NMC622‐Graphite cells. The central goal of this work is to accelerate the experimental search in a large search area by using a Bayesian‐optimization algorithm to guide the search. Different measurements are used as target variable such as open‐circuit voltage gradient and coulombic efficiency. Consequentially, the capability of these measurements for accelerated lifetime prediction compared to conventional ageing tests by cycling is investigated. The search gathered and confirmed additive combinations with excellent performance after four iterations with a total of 15 additive combinations analyzed. The results of this study give insights into the interaction of VC and FEC with regard to ageing.

Coordination of distributed unmanned surface vehicles via model-based reinforcement learning methods

This article presents a coordination algorithm for organizing a fleet of unmanned surface vehicles (USVs) to search multiple moving object targets in the ocean environment. During the fleet maneuver, USVs can exchange local sensing information through a wireless communication network. Based on both received and self-perceived information, a USV constructs a grid confidence map, which reflects how well the USV fleet perceives on every region of the search area. Then, the USV coordination is modeled as a reinforcement learning (RL) problem, where reward functions are defined based on the information obtained from the grid confidence map. Therefore, USVs are encouraged to explore new regions and prevented visiting the already searched areas. Search routes of USVs are calculated via a policy-iteration based path planning algorithm, while inter-vehicle collisions are avoided by applying policy constraints. Real-world experiments were conducted in the ocean environment to evaluate the validity of the proposed method. Compared to the conventional formation control strategy and the uncoordinated algorithm, experiment results show that the proposed method is more intelligent and efficient for searching object targets.

A surrogate-assisted Jaya algorithm based on optimal directional guidance and historical learning mechanism

An improved Jaya algorithm named surrogate-assisted Jaya algorithm based on optimal directional guidance and historical learning mechanism (SDH-Jaya) is proposed in this study to address the continuous optimization problems. In the SDH-Jaya, a surrogate-assisted model combined with the polynomial model and radial basis model built by the individual with real fitness is introduced to decrease the expensive computational simulations and accelerate the convergence speed. Two co-evolutionary mechanisms, which are named assisted co-evolutionary mechanism and self-learning co-evolutionary mechanism, are proposed to optimize the surrogate model and evolutionary population. Search directions and steps of the SDH-Jaya are adjusted adaptively by the differential vector resulting from the best solution and worst solution in the candidates at each generation. The historical population stored in an archive is selected randomly to provide new search areas for improving the diversity of the population during the evolution process of the SDH-Jaya. The performance of SDH-Jaya is tested on CEC2017 benchmark problems. The experimental results reveal that the effectiveness of the SDH-Jaya algorithm outperforms the classical Jaya algorithm, its variants, and state-of-the-art algorithms in terms of the quality of solution and execution time.

A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution

Many multi-objective optimization problems in the real world are dynamic, with objectives that conflict and change over time. These problems put higher demands on the algorithm’s convergence performance and the ability to respond to environmental changes. Confronting these two points, this paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). To overcome the instability of the traditional decomposition method for the changing Pareto optimal front (POF) shape, the proposed algorithm utilizes the complementary characteristics in the search area of the adversarial vector, and the two populations are alternately updated and co-evolved by adversarial search directions. Additionally, a novel particle update strategy is proposed to select promising neighborhood information to guide evolution and enhance diversity. To improve the ability to cope with environmental changes, an effective dynamic response mechanism is proposed, including three parts: archive set prediction, exploration of global optimal information, and retention of excellent particles to accelerate convergence to the Pareto optimal set (POS) in the new environment. The proposed algorithm is tested on a series of benchmark problems and compared to several state-of-the-art algorithms. The results show that ADNEPSO performed excellently in both convergence and diversity, and is highly competitive in dealing with dynamic problems.

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decision. This is the problem of information overload. Multi-document summarization is an important solution for this problem by producing a brief summary containing the most important information from a set of documents in a short time. This summary should preserve the main concepts of the documents. When the input documents are related to a specific domain, for example, medicine or law, summarization faces more challenges. Domain-oriented summarization methods use special characteristics related to that domain to generate summaries. This paper introduces the purpose of multi-document summarization systems and discusses domain-oriented approaches. Various methods have been proposed by researchers for multi-document summarization. This survey reviews the categorizations that authors have made on multi-document summarization methods. We also categorize the multi-document summarization methods into six categories: machine learning, clustering, graph, Latent Dirichlet Allocation (LDA), optimization, and deep learning. We review the different methods presented in each of these groups. We also compare the advantages and disadvantages of these groups. We have discussed the standard datasets used in this field, evaluation measures, challenges and recommendations.

Exhaustive Search of Correspondences between Multimodal Remote Sensing Images Using Convolutional Neural Network.

Finding putative correspondences between a pair of images is an important prerequisite for image registration. In complex cases such as multimodal registration, a true match could be less plausible than a false match within a search zone. Under these conditions, it is important to detect all plausible matches. This could be achieved by an exhaustive search using a handcrafted similarity measure (SM, e.g., mutual information). It is promising to replace handcrafted SMs with deep learning ones that offer better performance. However, the latter are not designed for an exhaustive search of all matches but for finding the most plausible one. In this paper, we propose a deep-learning-based solution for exhaustive multiple match search between two images within a predefined search area. We design a computationally efficient convolutional neural network (CNN) that takes as input a template fragment from one image, a search fragment from another image and produces an SM map covering the entire search area in spatial dimensions. This SM map finds multiple plausible matches, locates each match with subpixel accuracy and provides a covariance matrix of localization errors for each match. The proposed CNN is trained with a specially designed loss function that enforces the translation and rotation invariance of the SM map and enables the detection of matches that have no associated ground truth data (e.g., multiple matches for repetitive textures). We validate the approach on multimodal remote sensing images and show that the proposed “area” SM performs better than “point” SM.

FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area.

We propose a frequency-modulated continuous wave (FMCW) radar estimation algorithm with high resolution and low complexity. The fast Fourier transform (FFT)-based algorithms and multiple signal classification (MUSIC) algorithms are used as algorithms for estimating target parameters in the FMCW radar systems. FFT-based and MUSIC algorithms have tradeoff characteristics between resolution performance and complexity. While FFT-based algorithms have the advantage of very low complexity, they have the disadvantage of a low-resolution performance; that is, estimating multiple targets with similar parameters as a single target. On the other hand, subspace-based algorithms have the advantage of a high-resolution performance, but have a problem of very high complexity. In this paper, we propose an algorithm with reduced complexity, while achieving the high-resolution performance of the subspace-based algorithm by utilizing the advantages of the two algorithms; namely, the low-complexity advantage of FFT-based algorithms and the high-resolution performance of the MUSIC algorithms. The proposed algorithm first reduces the amount of data used as input to the subspace-based algorithm by using the estimation results obtained by FFT. Secondly, it significantly reduces the range of search regions considered for pseudo-spectrum calculations in the subspace-based algorithm. The simulation and experiment results show that the proposed algorithm achieves a similar performance compared with the conventional and low complexity MUSIC algorithms, despite its considerably lower complexity.

Tillyer Award Lecture: Visual search in noise and natural backgrounds

I will describe evidence for a theory of covert visual search developed within the framework of natural scene statistics and Bayesian statistical decision theory. The theory is unique in several ways: (1) it directly takes into account the statistical properties of natural images, (2) it takes into account the variation in neural processing with retinal location, as well as other known properties of the visual system, and hence contains almost no free parameters, and (3) it includes a principled attentional mechanism that efficiently allocates sensitivity gain across the visual field. This latter mechanism was discovered in experiments measuring covert search in white-noise backgrounds, where the target could appear anywhere within a large search area. In a separate experiment, target detectability (d') was measured across the visual field when the target location was cued/known. The shape of this "d' map" was consistent with the theory. The overall performance in the covert search task was also predicted quite well from this d' map, with no free parameters, assuming parallel unlimited-capacity processing. However, paradoxically, detection accuracy was low in the foveal region, even though it was predicted to be very high. We show that this "foveal neglect" is the expected consequence of efficiently allocating a fixed total attentional sensitivity gain across neurons in visual cortex, rather than across locations in visual space (the traditional assumption). Furthermore, the theory predicts the detailed pattern of covert search performance in the white-noise backgrounds. Finally, I will describe predictions of the theory for search in natural images.

Variable Stars in the Giant Satellite Galaxy Antlia 2

Abstract We report 350 pulsating variable stars found in four DECam fields (∼12 deg2) covering the Antlia 2 satellite galaxy. The sample of variables includes 318 RR Lyrae stars and eight anomalous Cepheids in the galaxy. Reclassification of several objects designated previously to be RR Lyrae as anomalous Cepheids get rid of the satellite’s stars intervening along the line of sight. This in turn removes the need for prolific tidal disruption of the dwarf, in agreement with the recently updated proper motion and pericenter measurements based on Gaia EDR3. There are also several bright foreground RR Lyrae stars in the field, and two distant background variables located ∼45 kpc behind Antlia 2. We found RR Lyrae stars over the full search area, suggesting that the galaxy is very large and likely extends beyond our observed area. The mean period of the RRab in Antlia 2 is 0.599 days, while the RRc have a mean period of 0.368 days, indicating the galaxy is an Oosterhoff-intermediate system. The distance to Antlia 2 based on the RR Lyrae stars is 124.1 kpc (μ 0 = 20.47) with a dispersion of 5.4 kpc. We measured a clear distance gradient along the semimajor axis of the galaxy, with the southeast side of Antlia 2 being ∼13 kpc farther away from the northwest side. This elongation along the line of sight is likely due to the ongoing tidal disruption of Ant 2.

Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching.

BackgroundKnowledge about adverse drug reactions (ADRs) in the population is limited because of underreporting, which hampers surveillance and assessment of drug safety. Therefore, gathering accurate information that can be retrieved from clinical notes about the incidence of ADRs is of great relevance. However, manual labeling of these notes is time-consuming, and automatization can improve the use of free-text clinical notes for the identification of ADRs. Furthermore, tools for language processing in languages other than English are not widely available.ObjectiveThe aim of this study is to design and evaluate a method for automatic extraction of medication and Adverse Drug Reaction Identification in Clinical Notes (ADRIN).MethodsDutch free-text clinical notes (N=277,398) and medication registrations (N=499,435) from the Cardiology Centers of the Netherlands database were used. All clinical notes were used to develop word embedding models. Vector representations of word embedding models and string matching with a medical dictionary (Medical Dictionary for Regulatory Activities [MedDRA]) were used for identification of ADRs and medication in a test set of clinical notes that were manually labeled. Several settings, including search area and punctuation, could be adjusted in the prototype to evaluate the optimal version of the prototype.ResultsThe ADRIN method was evaluated using a test set of 988 clinical notes written on the stop date of a drug. Multiple versions of the prototype were evaluated for a variety of tasks. Binary classification of ADR presence achieved the highest accuracy of 0.84. Reduced search area and inclusion of punctuation improved performance, whereas incorporation of the MedDRA did not improve the performance of the pipeline.ConclusionsThe ADRIN method and prototype are effective in recognizing ADRs in Dutch clinical notes from cardiac diagnostic screening centers. Surprisingly, incorporation of the MedDRA did not result in improved identification on top of word embedding models. The implementation of the ADRIN tool may help increase the identification of ADRs, resulting in better care and saving substantial health care costs.

A hybrid multifactorial evolutionary algorithm and firefly algorithm for the clustered minimum routing cost tree problem

The clustered minimum routing cost tree (CluMRCT) problem is a recent problem with a wide range of real-life applications, especially in designing computer networks with peer-to-peer architecture. Many multifactorial evolutionary algorithms have been proposed to solve multiple CluMRCT problems simultaneously. However, these algorithms only function effectively on complete graphs with small-and-medium sizes. Moreover, the blindness and randomness in the transfer of genetic materials cause a reduction in the exploitation ability and make these algorithms ineffective to solve low-similarity tasks. This paper proposes a hybrid multitasking algorithm named multifactorial firefly algorithm, which integrates the firefly algorithm’s strong exploitation ability to enhance the self-evolution of each task when facing low-similarity tasks while improving inter-task knowledge transfers by delivering higher-quality solutions. Also, the proposed algorithm is equipped with new encoding and decoding to focus more on potential search areas on both complete and sparse graphs. The experiments and Wilcoxon signed-rank tests were conducted on various instances to verify our proposal with several state-of-the-art methods. The results portrayed that the proposed encoding scheme helped multitasking algorithms improve solution quality by 32% on average. Besides, the statistical test values proved the superiority of the proposed hybrid algorithm in terms of solution quality and convergence trend.

Distributed wireless sensor node localization based on penguin search optimization

Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. Subsequently, the proposed algorithm is compared with four popular algorithms, namely particle swarm optimization (PSO), binary particle swarm optimization (BPSO), bat algorithm (BA), and cuckoo search algorithm (CS). The comparison is based on two performance metrics: localization accuracy and computation time to determine inordinate nodes. The results obtained from the simulation illustrate that PeSOA outperforms the other algorithms, achieving an accuracy higher than 30%. In terms of computation time to determine inordinate nodes, the proposed algorithm requires 28% less time (on average) than the other algorithms do.

Genomic Analysis of ESBL-Producing E. coli in Wildlife from North-Eastern Germany.

Antimicrobial resistance (AMR) is a serious global health threat and extended-spectrum beta-lactamase (ESBL)-producing Enterobacterales are a major contributor. This study aimed to gain a deeper insight into the AMR burden of wild animals. In total, 1595 fecal samples were collected by two systematic searches in Mecklenburg-Western Pomerania, north-east Germany. Samples were screened for ESBL-carrying Escherichia (E.) coli and isolates found were further analyzed using antimicrobial susceptibility testing and whole-genome sequencing. We found an estimated prevalence of 1.2% ESBL-producing E. coli in wild boar and 1.1% in wild ruminants. CTX-M-1 was the most abundant CTX-M type. We also examined fecal samples from wild boar and wild ruminants using shotgun metagenomics to gain insight into the resistome in wild animals. The latter revealed significantly lower normalized counts for AMR genes in wildlife samples compared to farm animals. The AMR gene levels were lower in wild ruminants than in wild boar. In conclusion, our study revealed a low prevalence of ESBL-producing E. coli and a low overall AMR gene burden in wild boar and wild ruminants, probably due to the secluded location of the search area.

Uma abordagem metodológica de Sensoriamento Remoto para o monitoramento da contaminação do rio Paraopeba pós-desastre de Brumadinho-MG

Este artigo tem o objetivo de apresentar e discutir o monitoramento por Sensoriamento Remoto do deslocamento da lama ocasionado pelo colapso da barragem de rejeitos de mineração do Córrego do Feijão, ocorrido em 25 de janeiro de 2019, na área rural do município de Brumadinho, Estado de Minas Gerais, Brasil. Esse evento foi classificado como um dos maiores desastres tecnoindustriais do Brasil, colocando em risco de contaminação uma das maiores e mais populosas bacias hidrográficas brasileiras: a bacia do rio São Francisco. A área de estudo compreendeu desde o local de contato da lama com a margem direita do rio Paraopeba até a sua foz na Represa de Três Marias, totalizando aproximadamente 315 km. Para esse monitoramento foi utilizado o método de razões de bandas espectrais de imagens dos sensores MSI/Sentinel-2 e OLI/Landsat-8 de diferentes datas, empregando-se a uniformização de médias e variâncias para compatibilizar as amplitudes dos valores de reflectância de superfície em cada imagem.

An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant.

This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.

Principal component analysis denoising improves sensitivity of MR diffusion to detect white matter injury in neuroHIV.

Diffusion-weighted imaging is able to capture important information about cerebral white matter (WM) structure. However, diffusion data can suffer from MRI and biological noise that degrades the quality of the images and makes finding important features difficult. We investigated how effectively local and nonlocal denoising increased the sensitivity to detect differences in cerebral WM in neuroHIV. We utilized principal component analysis (PCA) denoising to detect WM differences using fractional anisotropy. Local and nonlocal PCA denoising paradigms were implemented that varied in search area and number of components. We examined different-sized WM tracts that consistently show differences between people living with Human Immunodeficiency Virus (HIV) (PWH) and HIV-negative individuals (corpus callosum, forceps minor, and right uncinate fasciculus), and size-matched tracts not typically associated with HIV-related differences (spinothalamic, right medial lemniscus, and left occipitopontine). We first conducted a full sample comparison of WM differences between groups, and then randomly reduced the sample to the point where we still found differences in WM. Nonlocal PCA denoising allowed us to detect differences after a sample reduction of 35% in the forceps minor, 17% in the right uncinate fasciculus, and 6% in the corpus callosum. PCA denoising had a beneficial effect on detecting significant differences in PWH after sample size reduction. The smaller forceps minor tract and right uncinate fasciculus showed greater sensitivity to PCA denoising than the larger corpus callosum. These results show the importance of identifying the most effective PCA denoising strategy when investigating WM in PWH.

Object-Aware Adaptive Convolution Kernel Attention Mechanism in Siamese Network for Visual Tracking

As a classic framework for visual object tracking, the Siamese convolutional neural network has received widespread attention from the research community. This method uses a convolutional neural network to obtain the object features and to match them with the search area features to achieve object tracking. In this work, we observe that the contribution of each convolution kernel in the convolutional neural network for object tracking tasks is different. We propose an object-aware convolution kernel attention mechanism. Based on the characteristics of each object, the convolution kernel features are dynamically weighted to improve the expression ability of object features. The experiments performed using OTB and VOT benchmark datasets show that the performance of the tracking method fused with the convolution kernel attention mechanism is significantly better compared with the original method. Moreover, the attention mechanism can also be integrated with other tracking frameworks as an independent module to improve the performance.

Ultraluminous high-redshift quasars from SkyMapper – II. New quasars and the bright end of the luminosity function

ABSTRACT We search for ultraluminous Quasi-Stellar Objects (QSOs) at high redshift using photometry from the SkyMapper Southern Survey Data Release 3 (DR3), in combination with 2MASS, VHS DR6, VIKING DR5, AllWISE, and CatWISE2020, as well as parallaxes and proper motions from Gaia DR2 and eDR3. We report 142 newly discovered Southern QSOs at 3.8 < z < 5.5, of which 126 have M145 < −27 AB mag and are found in a search area of 14 486 deg2. This Southern sample, utilizing the Gaia astrometry to offset wider photometric colour criteria, achieves unprecedented completeness for an ultraluminous QSO search at high redshift. In combination with already known QSOs, we construct a sample that is >80 per cent complete for M145 < −27.33 AB mag at z = 4.7 and for M145 < −27.73 AB mag at z = 5.4. We derive the bright end of the QSO luminosity function at rest frame 145 nm for z = 4.7–5.4 and measure its slope to be β = −3.60 ± 0.37 and β = −3.38 ± 0.32 for two different estimates of the faint-end QSO density adopted from the literature. We also present the first z ∼ 5 QSO luminosity function at rest frame 300 nm.