Article
Computer Science, Artificial Intelligence
Jesus Guillermo Falcon-Cardona, Raquel Hernandez Gomez, Carlos A. Coello Coello, Ma. Guadalupe Castillo Tapia
Summary: This paper presents a survey of parallel implementations of multi-objective evolutionary algorithms (pMOEAs), discussing their significance in tackling computationally expensive applications, describing taxonomy and methods review, and proposing open questions for further research.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Hardware & Architecture
Chi Zhang, Haisheng Tan, Haoqiang Huang, Zhenhua Han, Shaofeng H-C Jiang, Guopeng Li, Xiang-Yang Li
Summary: In this paper, online job dispatching and scheduling strategies are proposed to handle jobs with different utility functions. Experimental results show that these strategies can significantly improve overall utility. Moreover, the algorithms demonstrate good robustness to estimation errors.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Management
Mehrnoosh Shafiee, Javad Ghaderi
Summary: This study investigates the problem of scheduling parallel-task jobs with heterogeneous resource requirements in a cluster of machines. It proposes approximation algorithms under different scenarios and validates the effectiveness of the algorithms through extensive simulation experiments using real traffic trace data.
OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Clement Flint, Ludovic Paillat, Berenger Bramas
Summary: This study presents a heuristic-based approach for assigning priorities to task types and evaluates its performance on multiple HPC applications.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Information Systems
Rui She, Wei Zhao
Summary: This paper proposes a heterogeneous computing resource scheduling model based on Stackelberg differential game, aiming to maximize the utility function of Computing Power Trading Platforms (CPTPs) and Heterogeneous Computing Service Providers (HCSPs). It also addresses the trade-off between profit and costs in the process of scheduling heterogeneous computing resources.
Article
Computer Science, Information Systems
Mulya Agung, Yuta Watanabe, Henning Weber, Ryusuke Egawa, Hiroyuki Takizawa
Summary: A parallel job scheduling method is proposed in this paper to effectively utilize shared heterogeneous systems for urgent computations. The method employs an in-memory process swapping mechanism to preempt jobs running on the coprocessor devices. Simulation results demonstrate a significant reduction in response time and slowdown of regular jobs without substantial delays of urgent jobs.
Article
Computer Science, Theory & Methods
Chi-Yeh Chen
Summary: This paper addresses the coflow scheduling problem in heterogeneous parallel networks and proposes two polynomial-time approximation algorithms for flow-level scheduling and coflow-level scheduling. Simulation results show the performance improvement over previous research.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Khu-rai Kim, Youngjae Kim, Sungyong Park
Summary: This article introduces a new parallel loop scheduling strategy BO FSS, which automatically tunes the internal parameter of FSS using Bayesian optimization and accelerates the convergence by considering the temporal locality effect.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Article
Operations Research & Management Science
Ilan Reuven Cohen, Izack Cohen, Iyar Zaks
Summary: This research proposes a polynomial-time algorithm with a constant approximation ratio for minimizing the weighted sum of job completion times for capacitated parallel machines. The algorithm prioritizes jobs based on the smallest volume-by-weight ratio and offers provable approximation guarantees.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Information Systems
Kai Liu, Ke Xiao, Penglin Dai, Victor C. S. Lee, Songtao Guo, Jiannong Cao
Summary: This paper introduces a fog computing empowered architecture and scheduling algorithm for data dissemination in vehicular ad-hoc networks, aiming to minimize service delay through cooperative service and improve data transmission efficiency.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Computer Science, Information Systems
Salwani Abdullah, Ayad Turky, Mohd Zakree Ahmad Nazri, Nasser R. Sabar
Summary: This article introduces a two-stage evolutionary variable neighbourhood search method for effectively solving the challenging industrial problem of unrelated parallel machine scheduling with sequence-dependent setup times.
Article
Computer Science, Artificial Intelligence
Xianpeng Wang, Hangyu Lou, Zhiming Dong, Chentao Yu, Renquan Lu
Summary: This paper investigates the VM and task joint scheduling problem and proposes a multi-objective mathematical model to optimize makespan, cost, and total tardiness. A problem-specific three-layer encoding approach is designed and a decomposition-based multi-objective evolutionary algorithm with pre-selection and dynamic resource allocation (MOEA/D-PD) is proposed. Experimental results show that the proposed algorithm outperforms existing approaches in the literature.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Energy & Fuels
Juraj Kardos, Timothy Holt, Vincenzo Fazio, Luca Fabietti, Filippo Spazzini, Olaf Schenk
Summary: This study analyzes the computational aspects in massively parallel simulations from the perspective of efficient hardware utilization, presenting a method for efficiently managing and processing computational tasks. A series of numerical experiments demonstrate that the optimized high-throughput computation strategy significantly reduces response times and prevents memory bottlenecks.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Computer Science, Hardware & Architecture
Soheir M. Khamis, Naglaa M. Reda, Wael Zakaria
Summary: Grid scheduling is a well-known NP-complete problem due to machine heterogeneity. Sort-Mid and Range-Suffrage are two efficient scheduling algorithms based on mean and average computations for optimization. The proposed RSSM algorithm combines the strengths of these two algorithms to achieve high resource utilization and minimize completion times.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Theory & Methods
Yu Yao, Yukun Song, Ying Huang, Wei Ni, Duoli Zhang
Summary: This paper proposes a novel list scheduling algorithm, MIPPOSS, which effectively solves the task scheduling problem on Memory-Constraint Heterogeneous Muti-Processor System (MCHMPS). The algorithm uses a predictive approach for task prioritization and processor selection, and employs a novel memory-constraint-aware approach in the processor selection phase. MIPPOSS has polynomial complexity and achieves better application scheduling results on the target architecture.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Review
Green & Sustainable Science & Technology
Diego Gabriel Rossit, Sergio Nesmachnow
Summary: This article provides a comprehensive review of recent advances in the Waste Bins Location Problem, highlighting the existing challenges in simultaneously optimizing bin location and waste collection, and considering uncertainty of model parameters and integrated approaches.
JOURNAL OF CLEANER PRODUCTION
(2022)
Editorial Material
Operations Research & Management Science
Hector Cancela, Fernando Tohme, Pedro Pineyro, Daniel Alejandro Rossit
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohamed El Khadiri, Wei-Chang Yeh, Hector Cancela
Summary: The accurate evaluation of the reliability parameter for the quickest path multi-state flow network is a difficult problem. Existing methods consist of two steps: generating relevant paths and computing the reliability. However, these methods are impractical for configurations with more than thirty paths. In this paper, a new factoring method is proposed, which recursively transforms the given problem into smaller networks and allows for the computation of networks with hundreds or even thousands of paths efficiently.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Hector Cancela, Leslie Murray, Gerardo Rubino
Summary: This article proposes a splitting-based Monte Carlo method for handling nonindependent components and demonstrates its efficiency through comparative experimental analysis.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Chemistry, Analytical
Andrei Gladkov, Egor Shiriaev, Andrei Tchernykh, Maxim Deryabin, Mikhail Babenko, Sergio Nesmachnow
Summary: In this paper, a routing solution for Wireless Sensor Networks (WSN) and Mobile Ad hoc NETworks (MANET) is proposed. The solution combines Secret Sharing Schemes (SSS) and Redundant Residual Number Systems (RRNS) to provide efficient and secure transmission in a heterogeneous network.
Editorial Material
Management
Victor M. Albornoz, Hector Cancela, Alejandro Mac Cawley, Sergio Maturana, Andres Weintraub
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Sergio Nesmachnow, Renzo Massobrio, Santiago Guridi, Santiago Olmedo, Andrei Tchernykh
Summary: This article introduces a model based on big data analysis to evaluate the travel times of buses in public transportation systems. The results show a reasonably good level of punctuality in the public transportation system, with some variability in terms of speed.
Proceedings Paper
Computer Science, Artificial Intelligence
Rodrigo Porteiro, Sergio Nesmachnow
Summary: This article presents an unsupervised machine learning approach for detecting air conditioning usage in households during the summer. The proposed methodology achieved an accuracy of 0.897, showcasing its promising potential. This non-intrusive method is valuable as it does not require large amounts of labeled data for training, enabling the identification of useful characteristics in electricity consumption patterns.
SMART CITIES, ICSC-CITIES 2022
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Claudio Risso, Sergio Nesmachnow, Diego Rossit
Summary: Providing an efficient public transportation system is crucial for improving the livability and sustainability of modern cities. This article proposes a mixed-integer programming model to address the bus timetabling problem and enhance multi-leg trips or transfers. The model aims to maximize the number of transfers while considering budgetary and quality of service constraints. Evaluation on real scenarios from the public transportation system in Montevideo, Uruguay shows that the proposed model outperforms the current timetable in terms of transfers, cost, and required buses.
SMART CITIES, ICSC-CITIES 2022
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Maximiliano Bove, Sergio Nesmachnow, Martin Draper
Summary: This article proposes a method for generating synthetic velocity fields in Large Eddy Simulations. The method utilizes a Generative Adversarial Network and considers relevant information about horizontal slices of turbulent velocity fields. It is evaluated on a real-world case study of enhancing resolution in horizontal velocity fields downstream of a wind turbine. The main results demonstrate that the proposed approach can generate high resolution images of horizontal velocity fields without the need for computationally expensive Large Eddy Simulations.
SMART CITIES, ICSC-CITIES 2022
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Andres Collares, Diego Helal, Sergio Nesmachnow, Andrei Tchernykh
Summary: This article analyzes the demand for and characterization of mobility using public transportation in Montevideo, Uruguay during the COVID-19 pandemic. A data analysis approach is used to extract insights from open data from different sources, including mobility patterns, the public transportation system, and COVID cases. The results provide valuable information about the reduction of trips during each wave of the pandemic, the correlation between trip numbers and COVID cases, and the recovery of public transportation usage.
SMART CITIES, ICSC-CITIES 2022
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Federico Gomez, Sergio Nesmachnow
Summary: This article presents a proposal for generating synthesized data for public transportation systems using a conditional Generative Adversarial Network approach. The solution is relevant for the planning and operation of Intelligent Transportation Systems. The practical validation in Montevideo, Uruguay demonstrates that the proposed approach can accurately generate synthesized data.
SMART CITIES, ICSC-CITIES 2022
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Lucas Gonzalez, Jamal Toutouh, Sergio Nesmachnow
Summary: This article explores the application of deep neural networks architectures for automatic building extraction, which is crucial for urban city planning and management. The results show that UNet-based architectures provide the most accurate predictions.
SMART CITIES, ICSC-CITIES 2022
(2023)
Article
Computer Science, Information Systems
Dora Jimenez, Javiera Barrera, Hector Cancela
Summary: In the last decade, the research community has shown great interest in the reliability of networks affected by large-scale disasters, especially earthquakes. This study utilizes Probabilistic Seismic Hazard Analysis to estimate the state of network elements after an earthquake. The approach considers a seismic source model and ground prediction equations to assess the intensity measure for each element. By incorporating terrain characteristics and component robustness, the analysis provides a more comprehensive evaluation of network performance at a manageable computational cost. The study highlights the dependence of results on infrastructure robustness/fragility and demonstrates the limitations of performance measures based solely on network topology.
Article
Green & Sustainable Science & Technology
Carlos E. Torres-Aguilar, Pedro Moreno-Bernal, Sergio Nesmachnow, Karla M. Aguilar-Castro, Luis Cisneros-Villalobos, Jesus Arce
Summary: This article presents an annual performance evaluation of single and double-air-channel solar chimneys for natural ventilation induction in Mexico. The results show that double-air-channel solar chimneys induce at least 70% more airflow than single-air-channel solar chimneys.
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
