Article
Engineering, Electrical & Electronic
Qinghui Xu, Sanyou Zeng, Fei Zhao, Ruwang Jiao, Changhe Li
Summary: For antenna designers, constrained multiobjective optimization problems are recommended as the most suitable type to model antenna arrays, and a dynamic constrained multiobjective evolutionary algorithm is a general and efficient algorithm that can solve various optimization problems.
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
(2021)
Article
Computer Science, Information Systems
Mario Garza-Fabre, Aaron L. Sanchez-Martinez, Edwin Aldana-Bobadilla, Ricardo Landa
Summary: Evolutionary multiobjective algorithms are popular for clustering problems due to their ability to optimize multiple criteria and their robustness to changes in data characteristics. This paper proposes a learning-based approach to decision making in clustering by building a model that can estimate solution quality and facilitate the selection of the best choice. Experimental results demonstrate the effectiveness of this approach compared to existing decision-making strategies.
Article
Computer Science, Artificial Intelligence
Tribhuvan Singh
Summary: The study combines the k-means clustering algorithm with IEAM-RP to improve the convergence rate and diversity of multiobjective optimization algorithms.
Article
Automation & Control Systems
Qinqin Fan, Yilian Zhang, Ning Li
Summary: The paper introduces an automatic selection strategy of multiobjective evolutionary algorithms based on performance indicators (MOEAS-PI). This strategy can effectively improve the efficiency and robustness of solving multiobjective optimization problems.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Hakan Ezgi Kiziloz, Ayca Deniz
Summary: In this study, a robust framework for feature selection is built leveraging the multi-core nature of a regular PC. Multiple execution settings are facilitated through the use of two multiobjective selection algorithms, four initial population generation methods, and five machine learning techniques. Extensive experiments on 11 UCI benchmark datasets show remarkable improvement in terms of maximum accuracy.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Automation & Control Systems
Zhi-Zhong Liu, Yong Wang, Bing-Chuan Wang
Summary: This study combines indicator-based multiobjective evolutionary algorithms with constraint-handling techniques to develop a framework for constrained multiobjective optimization. Nine indicator-based CMOEAs were developed and experimentally evaluated on 19 widely used test functions. The results show the importance of both indicator-based MOEAs and constraint-handling techniques in the performance of indicator-based CMOEAs, providing valuable insights for future research.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Energy & Fuels
Christos Kyriklidis, Marios-Errikos Kyriklidis, Efstratios Loizou, Adam Stimoniaris, Constantinos G. Tsanaktsidis
Summary: This paper proposes an evolutionary computation approach to optimize raw materials mixtures for Bio Marine Fuel (BMF) production, focusing on producing high-quality Biodiesel first and then creating low-sulfur BMF to reduce pollutant emissions. The genetic algorithm utilized improves fuel prices and demonstrates the capability of coping with mixture optimization problems.
Article
Computer Science, Artificial Intelligence
Weiyu Chen, Hisao Ishibuchi, Ke Shang
Summary: This article discusses the importance of subset selection in evolutionary multiobjective optimization and proposes efficient greedy algorithms based on submodular property. Computational experiments show that these algorithms are faster than the standard greedy algorithms and also contribute to the research on performance indicators.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Hsien-Chung Wu
Summary: This paper proposes a new approach to solving multiobjective optimization problems using genetic algorithms and cooperative game concepts. By considering the objective functions as players and obtaining suitable weights from the core values of a cooperative game, a set of Core-Pareto optimal solutions can be obtained.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Automation & Control Systems
Zhengping Liang, Tiancheng Wu, Xiaoliang Ma, Zexuan Zhu, Shengxiang Yang
Summary: In recent years, dynamic multiobjective optimization problems (DMOPs) have gained increasing attention. This article proposes a dynamic multiobjective evolutionary algorithm (DMOEA-DVC) based on decision variable classification, aiming to balance population diversity and convergence. Experimental results comparing DMOEA-DVC with six other algorithms on 33 benchmark DMOPs demonstrate its superior overall performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Civil
Chao Wang, Rongrong Liu, Yansen Su, Xingyi Zhang
Summary: The electric location-routing problem involves optimizing electric vehicle routing and charging facility location. Existing algorithms often use a two-phase search strategy to optimize routing and location alternately, but they are criticized for inefficiency as problem size increases. To improve search efficiency, we propose an accelerating two-phase multiobjective evolutionary algorithm that uses a learning method to extract useful information from historical searches and generate high-quality routing and location solutions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jing Liang, Xuanxuan Ban, Kunjie Yu, Boyang Qu, Kangjia Qiao, Caitong Yue, Ke Chen, Kay Chen Tan
Summary: Handling constrained multiobjective optimization problems is challenging due to the need to simultaneously optimize multiple conflicting objectives subject to various constraints. This article provides a comprehensive survey of evolutionary constrained multiobjective optimization. It categorizes and analyzes numerous algorithms, reviews benchmark test problems, investigates the performance of constraint handling techniques and algorithms, discusses emerging and representative applications, and outlines future research directions.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Giovanni Acampora, Autilia Vitiello
Summary: This study introduces a new evolutionary algorithm utilizing an actual quantum processor, which employs quantum phenomena to achieve significant speed-up in computation. By implementing quantum concepts such as quantum chromosome and entangled crossover, the proposed algorithm efficiently executes genetic evolution on quantum devices to converge towards proper sub-optimal solutions of a given optimization problem. The experimental results show that the synergy between quantum and evolutionary computation leads to a promising bio-inspired optimization strategy.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Qiuzhen Lin, Xunfeng Wu, Lijia Ma, Jianqiang Li, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes an ensemble surrogate-based framework for solving computationally expensive multiobjective optimization problems (EMOPs). The framework trains a global surrogate model and multiple surrogate submodels to enhance prediction accuracy and reliability. Experimental results demonstrate the advantages of this approach in solving EMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Eric O. Scott, Mark Coletti, Catherine D. Schuman, Bill Kay, Shruti R. Kulkarni, Maryam Parsa, Chathika Gunaratne, Kenneth A. De Jong
Summary: Asynchronous evolutionary algorithms are popular for solving computationally expensive search and optimization problems using many processors. The SWEET strategy improves the ability of slow-evaluating individuals with higher fitness to multiply in the population. It shows effectiveness in optimizing problems with positive correlation between solution quality and evaluation time.
Article
Computer Science, Information Systems
Ismael Vazquez, Maria Novo-Loures, Reyes Pavon, Rosalia Laza, Jose Ramon Mendez, David Ruano-Ordas
Summary: Current research requires adequate description of algorithms and results, as well as reproducibility and comparison with other approximations. Public data sets are crucial for experimental protocols, but have limitations. Enhancements are needed for data repositories, such as customized data sets, comparison of techniques with different pre-processing methods, availability of software applications for reproducing pre-processing steps, and protection of licensing issues and user rights.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Computer Science, Hardware & Architecture
Daniela L. Freire, Rafael Z. Frantz, Fabricia Roos-Frantz, Vitor Basto-Fernandes
Summary: Enterprises are increasingly integrating applications and services in business processes due to the advancement of cloud and mobile applications. Integration tools need to adapt to handle high volumes of data and utilize cloud computational resources efficiently to avoid increasing operational costs. The proposed Queue-priority algorithm with Particle Swarm Optimization shows promise in improving task scheduling for integration processes with high volumes of data.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Daniela L. Freire, Rafael Z. Frantz, Fabricia Roos-Frantz, Vitor Basto-Fernandes
Summary: Cloud computing enables enterprises to integrate applications and computational resources, with integration platforms reducing maintenance costs. Improvements in task scheduling are needed due to high-performance demands. This study explores the specifics and vulnerabilities of integration task scheduling, proposing a ranking method based on conceptual models to optimize performance and minimize costs for companies.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Cassio L. M. Belusso, Sandro Sawicki, Vitor Basto-Fernandes, Rafael Z. Frantz, Fabricia Roos-Frantz
Summary: Growing demand for reduced local hardware infrastructure is driving the adoption of Cloud Computing. In this study, the researchers propose D-AHP, a decision-making methodology based on Pareto Dominance and Analytic Hierarchy Process (AHP), to assist users in selecting virtual machine instances. The study finds that considering the datacenter location as a criterion for instance selection can lead to different results on which instance is more suitable for the user.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Maria Novo-Loures, David Ruano-Ordas, Reyes Pavon, Rosalia Laza, Silvana Gomez-Meire, Jose R. Mendez
Summary: This study examines the use of various features to complement synset-based and bag-of-words representations of texts for spam filtering using classical ML approaches. The evaluation of features across different channels and classifiers demonstrates the effectiveness of detecting non-textual entities and using language probability information for classification improvement. Additionally, features influenced by specific behaviors of Internet service users are found to be not useful for spam filtering.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Daniela L. Freire, Rafael Z. Frantz, Fabricia Roos-Frantz, Vitor Basto-Fernandes
Summary: The article proposes a simulation tool for task scheduling in Enterprise Application Integration, evaluating the performance of three different heuristics through statistical tests.
JOURNAL OF SIMULATION
(2022)
Article
Chemistry, Multidisciplinary
Silvana Gomez-Meire, Cesar Gabriel Marquez, Eliana Patricia Aray-Cappello, Jose R. Mendez
Summary: This study introduces the Live Spam Beater (LiSB) framework for executing email filtering techniques during SMTP transactions, aiming to improve the effectiveness and efficiency of existing proactive filtering mechanisms. By implementing proactive filtering schemes, senders can be notified of spam emails through SMTP response codes during the transaction process. The framework, written in Python, acts as an MTA server and reverse proxy for SMTP, allowing easy incorporation of new proactive filtering techniques through plugins.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Gabriela del Rocio Roldan-Molina, Iryna Yevseyeva, Silvana Gomez-Meire, Vitor Basto-Fernandes, Jose Ramon Mendez
Summary: This article presents an innovative approach for knowledge management using OWL format to enhance the quality of automatically created ontologies and assist domain experts in improving the target ontology. The method is validated using Babelnet semantic graph and a manually generated ontology, and its effectiveness is demonstrated by comparing the improved target ontology with the original version.
JOURNAL OF INFORMATION SCIENCE
(2022)
Article
Computer Science, Information Systems
Bruno Coutinho, Joao Ferreira, Iryna Yevseyeva, Vitor Basto-Fernandes
Summary: The recent increase in cybersecurity threats and cyberattacks has impacted organizations at different levels, including critical business processes and the overall survival of the organizations. This study focuses on the cybersecurity of the healthcare sector, which is considered critical national infrastructure in many countries. An integrated cybersecurity methodology and supporting tools called Cyber.SCuris are proposed to complement existing cyber threat intelligence and incident response standards such as NIST SP800.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Artificial Intelligence
Inaki Velez de Mendizabal, Vitor Basto-Fernandes, Enaitz Ezpeleta, Jose R. Mendez, Silvana Gomez-Meire, Urko Zurutuza
Summary: By using synset-based grouping methods, both lossless and low-loss reduction strategies can be implemented to address linguistic phenomena and improve the efficiency of spam filtering classifiers.
PEERJ COMPUTER SCIENCE
(2023)
Article
Forestry
Maria Novo-Loures, Maria Fernandez-Gonzalez, Reyes Pavon, Kenia C. Sanchez Espinosa, Rosalia Laza, Guillermo Guada, Jose R. Mendez, Florentino Fdez-Riverola, Francisco Javier Rodriguez-Rajo
Summary: Black alder, a widely distributed tree species in Europe, especially along watercourses in urban areas, has a high allergenic potential. This study proposes the use of a Machine Learning model to accurately predict high-risk periods for allergies among sensitive people, considering the increased allergenicity caused by global temperature rise induced by climate change. The Random Forest model was found to be the best performing model in detecting medium and high-risk days.
Article
Computer Science, Artificial Intelligence
Maria Novo-Loures, Yeray Lage, Reyes Pavon, Rosalia Laza, David Ruano-Ordas, Jose Ramon Mendez
Summary: Data mining has become a powerful tool for exploring unseen connections between variables and facts in different domains. However, current data analysis frameworks, specifically those using pipelining schemes, lack early error detection techniques and developer support mechanisms. In this study, a new pipelining framework, BDP4J, is introduced with improved features to address these limitations.
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
