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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Covariance-Based Activity Detection in Cooperative Multi-Cell Massive MIMO: Scaling Law and Efficient Algorithms

arXiv

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arXiv 2023年

作者： Wang, Ziyue Liu, Ya-Feng Wang, Zhaorui Yu, Wei The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

This paper focuses on the covariance-based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system. In this system, active devices transmit their signature sequences to multiple base stations (BSs), and the BSs cooperatively detect the active devices based on the received signals. While the scaling law for the covariance-based activity detection in the single-cell scenario has been extensively analyzed in the literature, this paper aims to analyze the scaling law for the covariance-based activity detection in the multi-cell massive MIMO system. Specifically, this paper demonstrates a quadratic scaling law in the multi-cell system, under the assumption that the path-loss exponent of the fading channel γ > 2. This finding shows that, in the multi-cell massive MIMO system, the maximum number of active devices that can be correctly detected in each cell increases quadratically with the length of the signature sequence and decreases logarithmically with the number of cells (as the number of antennas tends to infinity). Moreover, in addition to analyzing the scaling law for the signature sequences randomly and uniformly distributed on a sphere, the paper also establishes the scaling law for signature sequences based on a finite alphabet, which are easier to generate and store. Finally, this paper proposes two efficient accelerated coordinate descent (CD) algorithms with a convergence guarantee for solving the device activity detection problem. The first algorithm reduces the complexity of CD by using an inexact coordinate update strategy. The second algorithm avoids unnecessary computations of CD by using an active set selection strategy. Simulation results show that the proposed algorithms exhibit excellent performance in terms of computational efficiency and detection error probability. Copyright © 2023, The Authors. All rights reserved.

关键词： Fading channels

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Scaling laws and spatial effects of Brazilian health regions: A research protocol

Scaling laws and spatial effects of Brazilian health regions...

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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Soares, Giovane Thomazini Yamada, Diego Bettiol Bernardi, Filipe Andrade Neiva, Mariane Barros Lombardi, Luis Pedro Vinci, Andre Luiz Teixeira De Andrade Mioto, Ana Clara Alves, Domingos Ribeirão Preto Medical School University of Sao Paulo Av Bandeirantes 3900SP Ribeirao Preto14049900 Brazil Bioengineering Postgraduate Program Av Trabalhador Sao-Carlense 400SP Sao Carlos13566590 Brazil Institute of Mathematics and Computer Sciences Av Trabalhador Sao-Carlense 400SP Sao Carlos13566590 Brazil Institute of Mathematics Statistics and Scientific Computing State University of Campinas Rua Sergio Buarque de Holanda 651SP Campinas13083859 Brazil

The literature has already consolidated the importance of health regions for Brazilian public health. Complexity properties strongly mark such regions. In this context, there are abundant indications that health regions should be analyzed with approaches linked to the sciences of complexity. One of these approaches, the estimation of scaling laws, can describe important properties of socio-spatial elements. However, no studies estimate the scaling laws of Brazilian health regions. This research protocol can remedy this limitation, proposing the estimation of scaling laws of the previously mentioned regions, mainly considering variables relevant to Brazilian public health. Still, this paper can substantially mitigate other relevant limitations of usual research that estimate scaling laws of socio-spatial elements. These mitigations, which provide advances in the literature on estimating scaling laws, are given by the proposal of modeling (if necessary) spatial effects and estimating scaling laws for the entire population of the socio-spatial elements. According to the theory, the expected results are non-linear scaling laws, which will likely vary with space and time and coexist with relevant spatial effects. From such laws and effects, it will be possible to accurately characterize the performance of each health region through Spatial and Scale Adjusted Metropolitan Indicators and unravel spatio-Temporal properties, stabilities, and instabilities of sets composed of health regions. The expected findings of this paper can help rearrange health regions and improve the quality of information used in Brazilian public health planning. © 2023 Elsevier B.V.. All rights reserved.

关键词： Scaling laws

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Informational infrastructure of the common digital space of scientific knowledge

Informational infrastructure of the common digital space of ...

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2020 International Conference "Common Digital Space of scientific Knowledge: Problems and Solutions", CDSSK 2020

作者： Ataeva, Olga Kalenov, Nikolay Serebriakov, Vladimir Sotnikov, Alexander Computing Center FRS "Computer Sciences and Control" of Russian Academy of Sciences Vavilova str. 40b Moscow119333 Russia Joint Supercomputer Center of Russian Academy of Sciences – Branch of Federal State Institution "Scientific Research Institute for System Analysis" of Russian Academy of Sciences Leninskiy pr. 32a Moscow119334 Russia

The space of scientific knowledge is understood as a system of knowledge from various fields of science tested by the scientific community. The Common Digital Space of scientific Knowledge (CDSSK) is a digital environment into which information resources and objects that have been tested by the scientific community and that reflect scientific knowledge from a different fields of science are integrated. The common digital space of scientific knowledge consists of a set of thematic subspaces related to various areas of science, built according to common principles. The first step towards the implementation of the CDSSK is the development of its ontology, i.e. a multi-level system of concepts describing resources and objects of subject areas, concepts, terms and connections between them, characterized by an open hierarchical and dynamic structuring, oriented both for storing existing knowledge and for extracting new ones. Since the CDSSK is a fundamentally new information environment, there are a number of significant problems on the way to its implementation. The paper deals with the problems and tasks of forming such a space and developing tools for implementation and support the CDSSK. © 2021 Copyright for this paper by its authors

关键词： Ontology

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Nonlinear ensemble filtering with diffusion models: Application to the surface quasi-geostrophic dynamics

arXiv

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arXiv 2024年

作者： Bao, Feng Chipilski, Hristo G. Liang, Siming Zhang, Guannan Whitaker, Jeffrey S. Department of Mathematics Florida State University TallahasseeFL United States Department of Scientific Computing Florida State University TallahasseeFL United States Computer Science and Mathematics Division Oak Ridge National Laboratory United States Physical Sciences Laboratory NOAA/Earth System Research Laboratories BoulderCO United States

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the proposed Ensemble Score Filter (EnSF) is completely training-free and can efficiently generate a set of analysis ensemble members. In this study, we apply the EnSF to a surface quasi-geostrophic model and compare its performance against the popular Local Ensemble Transform Kalman Filter (LETKF), which makes Gaussian assumptions on the posterior distribution. Numerical tests demonstrate that EnSF maintains stable performance in the absence of localization and for a variety of experimental settings. We find that EnSF achieves competitive performance relative to LETKF in the case of linear observations, but leads to significant advantages when the state is nonlinearly observed and the numerical model is subject to unexpected shocks. A spectral decomposition of the analysis results shows that the largest improvements over LETKF occur at large scales (small wavenumbers) where LETKF lacks sufficient ensemble spread. Overall, this initial application of EnSF to a geophysical model of intermediate complexity is very encouraging, and motivates further developments of the algorithm for more realistic problems. © 2024, CC BY.

关键词： Kalman filters

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Improved phase-field-based lattice Boltzmann method for thermocapillary flow

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Physical Review E 2022年第1期105卷 015314-015314页

作者： Liqing Yue Zhenhua Chai Huili Wang Baochang Shi School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan 430074 China School of Mathematical and Computer Sciences Wuhan Textile University Wuhan 430074 China

In this paper, we present an improved phase-field-based lattice Boltzmann (LB) method for thermocapillary flows with large density, viscosity, and thermal conductivity ratios. The present method uses three LB models to solve the conservative Allen-Cahn equation, the incompressible Navier-Stokes equations, and the temperature equation. To overcome the difficulty caused by the convection term in solving the convection-diffusion equation for the temperature field, we first rewrite the temperature equation as a diffuse equation where the convection term is regarded as the source term and then construct an improved LB model for the diffusion equation. The macroscopic governing equations can be recovered correctly from the present LB method; moreover, the present LB method is much simpler and more efficient. In order to test the accuracy of this LB method, several numerical examples are considered, including the planar thermal Poiseuille flow of two immiscible fluids, the two-phase thermocapillary flow in a nonuniformly heated channel, and the thermocapillary Marangoni flow of a deformable bubble. It is found that the numerical results obtained from the present LB method are consistent with the theoretical prediction and available numerical data, which indicates that the present LB method is an effective approach for the thermocapillary flows.

关键词： Drops & bubbles Multiphase flows Lattice-Boltzmann methods Navier-Stokes equation

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Multirate Time-Integration based on Dynamic ODE Partitioning through Adaptively Refined Meshes for Compressible Fluid Dynamics

arXiv

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arXiv 2024年

作者： Doehring, Daniel Schlottke-Lakemper, Michael Gassner, Gregor J. Torrilhon, Manuel Applied and Computational Mathematics RWTH Aachen University Schinkelstrasse 2 North Rhine-Westphalia Aachen52062 Germany University of Stuttgart Germany High-Performance Scientific Computing Center for Advanced Analytics and Predictive Sciences University of Augsburg Germany Department of Mathematics and Computer Science Center for Data and Simulation Science University of Cologne Germany

In this paper, we apply the Paired-Explicit Runge-Kutta (P-ERK) schemes by Vermeire et. al. [1, 2] to dynamically partitioned systems arising from adaptive mesh refinement. The PERK schemes enable multirate time-integration with no changes in the spatial discretization methodology, making them readily implementable in existing codes that employ a method-of-lines approach. We show that speedup compared to a range of state of the art Runge-Kutta methods can be realized, despite additional overhead due to the dynamic re-assignment of flagging variables and restricting nonlinear stability properties. The effectiveness of the approach is demonstrated for a range of simulation setups for viscous and inviscid convection-dominated compressible flows for which we provide a reproducibility repository. In addition, we perform a thorough investigation of the nonlinear stability properties of the Paired-Explicit Runge-Kutta schemes regarding limitations due to the violation of monotonicity properties of the underlying spatial discretization. Furthermore, we present a novel approach for estimating the relevant eigenvalues of large Jacobians required for the optimization of stability *** Codes 65L06, 65M20, 76Mxx, 76Nxx © 2024, CC BY.

关键词： Runge Kutta methods

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Enhanced multiple sclerosis diagnosis by MRI image retrieval using convolutional autoencoders

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Egyptian Informatics Journal 2025年 30卷

作者： Haggag, R.M. Ali, Eman M. Khalifa, M.E. Taha, Mohamed Scientific Computing Department Faculty of Computers and Artificial Intelligence Benha University Egypt Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Benha University Egypt

Multiple sclerosis (MS) is an autoimmune disorder characterized by damage to the central nervous system (CNS), leading to neuronal degeneration and affecting over 2.8 million individuals globally. Early and accurate diagnosis of MS is critical, given its significant social and economic consequences. Magnetic resonance imaging (MRI) remains the gold standard for MS diagnosis and monitoring. This study introduces a novel Content-Based Medical Image Retrieval (CBMIR) framework that leverages a newly designed Convolutional Autoencoder (CAE) model to improve the diagnostic evaluation of MS-related MRI scans. The proposed system extracts latent features from query and reference MRI images using the CAE. Extensive ablation studies involving nine distance metrics and diverse feature space dimensions identify 64 as the optimal latent feature size and validate the Mahalanobis distance as the superior similarity measure. Evaluated on four publicly available MS MRI datasets, the framework achieves Mean Average Precision (MAP) scores of 91.23%, 98.68%, 99.88%, and 99.69%, respectively, demonstrating enhanced diagnostic accuracy. The system also outperforms existing similar CBMIR frameworks for other diseases in MAP scores and generalizes effectively without requiring extensive preprocessing or segmentation. The primary contribution of this work is the development of a CAE-driven CBMIR system optimized for MS diagnosis, achieving state-of-the-art MAP performance while maintaining an average retrieval latency of 780 ms outperforming the compared systems. © 2025

关键词： Content based retrieval

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Evidence-Aware Multi-Modal Data Fusion and its Application to Total Knee Replacement Prediction

Evidence-Aware Multi-Modal Data Fusion and its Application t...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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Two Methods for Regularized Non-Monotone Submodular Maximization

SSRN

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SSRN 2024年

作者： Sun, Xin Han, Congying Wu, Chenchen Xu, Dachuan Zhou, Yang Du, Ding-Zhu School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China College of Sciecne Tianjin University of Technology Tianjin300072 China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China School of Mathematics and Statistics Shandong Normal University Jinan250014 China Department of Computer Science University of Texas at Dallas RichardsonTX75080 United States

In this paper, we investigate regularized non-monotone submodular maximization over a down-monotone family of sets by applying the Lyapunov method and the distorted continuous greedy algorithm. Based on the Lyapunov method, we systematically design parameterized algorithm frameworks in two phases. In the first phase, a theoretical approximation algorithm can be derived, as long as a set of parameters is chosen. Contingent upon the selection of a suitable set of parameters, the approximation guarantee can achieve $(1/e, \frac{\gamma-\gamma/e-1}{\gamma-1})$, where $\gamma\in[0,1)\cup[e,+\infty)$ reflects the relative dominance of the positive and negative parts of the optimal value for the regular term. In the second phase, an implementable algorithm is presented through discretization in $\mathcal{O}(n^3/\epsilon)$ with arbitrarily small loss in guarantee. Additionally, we establish that no polynomial-time algorithm yields a $(0.478,\lambda)$-approximation with $\lambda\in[0,1]$. Furthermore, we demonstrate that the Lyapunov method can also be applied in regularized monotone submodular maximization with the tight $(1-1/e, 1)$-approximation ratio. © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

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