This work presents a strategy to implement a distributed form of genetic algorithm (GA) on low power, low cost, and small-sized memory aiming for increased performance and reduction of energy consumption when compared to standalone GAs. This strategy...
Bayesian approaches to decision trees (DTs) using Markov Chain Monte Carlo (MCMC) samplers have recently demonstrated state-of-the-art accuracy performance when it comes to training DTs to solve classification problems. Despite the competitive classi...
Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This...
Resampling is a well-known statistical algorithm that is commonly applied in the context of Particle Filters (PFs) in order to perform state estimation for non-linear non-Gaussian dynamic models. As the models become more complex and accurate, the ru...
In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capac...
The LU factorization of very large sparse matrices requires a significant amount of computing resources, including memory and broadband communication. A hybrid MPI + OpenMP + CUDA algorithm named SuperLU3D can efficiently compute the LU factorization...
In this work, we introduce a scalable and efficient GPU-accelerated methodology for volumetric particle advection and finite-time Lyapunov exponent (FTLE) calculation, focusing on the analysis of Lagrangian coherent structures (LCS) in large-scale di...
Structural changes (martensitic transformation, rearrangements of martensitic variants) in shape memory alloys have an intermittent character that is accompanied by the emission of different (thermal, acoustic, and magnetic) noises, which are fingerp...
A wide range of applications use optimization algorithms to find an optimal value, often a minimum one, for a given function. Depending on the application, both the optimization algorithm’s behavior, and its computational time, can prove to be...
A numerous group of optimization algorithms based on heuristic techniques have been proposed in recent years. Most of them are based on phenomena in nature and require the correct tuning of some parameters, which are specific to the algorithm. Heuris...
Distributed permutation flowshop scheduling is an NP-hard problem that has become a hot research topic in the fields of optimization and manufacturing in recent years. Multimodal optimization finds multiple global and local optimal solutions of a fun...
Multiobjective clustering algorithm using particle swarm optimization has been applied successfully in some applications. However, existing algorithms are implemented on a single machine and cannot be directly parallelized on a cluster, which makes i...
Privacy amplification is an indispensable procedure for key generation in the quantum key distribution system and the physical layer key distribution system. In this paper, we propose a high-speed privacy amplification algorithm that saves hardware m...
Large-scale inverse problems that require high-performance computing arise in various fields, including regional air quality studies. The paper focuses on parallel solutions of an emission source identification problem for a 2D advection–diffus...
Public goods games have been extensively studied to determine the mechanism behind cooperation in social dilemmas. Previous public goods games based on particle swarm algorithms enabled individuals to integrate their past best strategies with the cur...
The performance of machine learning (ML) algorithms depends on the nature of the problem at hand. ML-based modeling, therefore, should employ suitable algorithms where optimum results are desired. The purpose of the current study was to explore the p...
Aiming at the problem of spatial query processing in distributed computing systems, the design and implementation of new distributed spatial query algorithms is a current challenge. Apache Spark is a memory-based framework suitable for real-time and...
The ever-increasing complexity of industrial and engineering problems poses nowadays a number of optimization problems characterized by thousands, if not millions, of variables. For instance, very large-scale problems can be found in chemical and mat...
The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed...
Cloudlet networks are an emerging distributed data processing paradigm, which contain multiple cloudlets deployed beside base stations to serve local user devices (UDs). Each cloudlet is a small data center with limited memory, in which multiple virt...
Software quantum simulators are essential tools for designing and testing quantum algorithms on classical computing architectures, especially given the current limitations of physical quantum hardware. This work focuses on studying and evaluating mem...
Mutual exclusion is a fundamental problem in concurrent/parallel/distributed systems. The first pure-software solution to this problem for two processes, which is not based on hardware instructions like test-and-set, was proposed in 1965 by Th.J. Dek...
Balancing efficiency and accuracy is often challenging in the numerical solution of three-dimensional (3D) point source acoustic wave equations for layered media. To overcome this, an efficient solution method in the spatial-wavenumber domain is prop...
Inthis paper, a distributed semi-supervised partial multi-label learning (dS2PML) algorithm is proposed, which can be used to address the problem of distributed classification of partially multi-labeled data and unlabeled data. In this algorithm, we...
Online learning is a framework for processing and learning from sequential data in real time, offering benefits such as promptness and low memory usage. However, it faces critical challenges, including concept drift, where data distributions evolve o...
A new stochastic process was developed by considering the internal performance of macro-states in which the sojourn time in each one is phase-type distributed depending on time. The stationary distribution was calculated through matrix-algorithmic me...
This study estimates the effects of the dual long memory property and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron structural break test, Inclán and Tiao’s iterated cumulativ...
We provide a comprehensive presentation of algorithms, data structures, and implementation techniques for high-performance sparse multivariate polynomial arithmetic over the integers and rational numbers as implemented in the freely available Basic P...
Packet classification is a core function of network devices for providing advanced services, with the key challenge being to optimize classification speed while maintaining low memory usage. So far, many have proposed software-based packet classifica...
The new barrier mode in Apache Spark allows for embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage does not depend on any other tasks in the same stage, and hence i...
In the present paper, the nonlocal-in-time damping models, called “damping-with-memory” models, are reviewed. Since such models do not involve the distribution along the longitudinal coordinate, they are easily adjustable for the FEM (Fin...
A robust–accurate estimation of fluid flow is the main building block of a distributed virtual flow meter. Unfortunately, a big leap in algorithm development would be required for this objective to come to fruition, mainly due to the inability...
To plan the work of power generation equipment, it is necessary to ensure that the power supply is sufficient and to achieve the minimum cost to ensure the safety and economy of the microgrid. Based on back propagation neural network–local mean...
Due to its non-volatility and large capacity, NVM devices gradually take place at various levels of memories. However, their limited endurance is still a big concern for large-scale data centres. Compression algorithms have been used to save NVM spac...
Pattern recognition as a computing task is very well suited for machine learning algorithms utilizing artificial neural networks (ANNs). Computing systems using ANNs usually require some sort of data storage to store the weights and bias values for t...
The traditional heterogeneous embedding method based on a random walk strategy does not focus on the random walk fundamentally because of higher-order Markov chains. One of the important properties of Markov chains is stationary distributions (SDs)....
Efficient management and utilization of edge server memory buffers are crucial for improving the efficiency of concurrent editing in the concurrent editing application scenario of large-scale video in edge computing. In order to elevate the efficienc...
The 3D inversion algorithm for gravity data based on a smooth model constraint has been proven to yield a reasonable density distribution. However, as the amount of observed data and model parameters increases, the algorithm experiences issues with h...
By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algo...
The distribution of the sum of negative binomial random variables has a special role in insurance mathematics, actuarial sciences, and ecology. Two methods to estimate this distribution have been published: a finite-sum exact expression and a series...
The economic operation of hydropower stations has the potential to increase water use efficiency. However, there are some challenges, such as the fixed and unchangeable flow characteristic curve of the hydraulic turbines, and the large number of vari...
Metaheuristic and exact methods are one of the most common tools to solve Mixed-Integer Optimization Problems (MIPs). Most of these problems are NP-hard problems, being intractable to obtain optimal solutions in a reasonable time when the size of the...
In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the e...
This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-obj...
Service mesh is gaining popularity as a microservice architecture paradigm due to its lightness, transparency, and scalability. However, fully releasing configurations to the data plane during the business development phase can result in noticeable p...
With the development of various information and communication technologies, the amount of big data has increased, and distributed file systems have emerged to store them stably. The replication technique divides the original data into blocks and writ...
Sustainability research faces many challenges as respective environmental, urban and regional contexts are experiencing rapid changes at an unprecedented spatial granularity level, which involves growing massive data and the need for spatial relation...
Super points detection plays an important role in network research and application. With the increase of network scale, distributed super points detection has become a hot research topic. The key point of super points detection in a multi-node distri...
Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ra...
The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much la...
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