Article
Computer Science, Interdisciplinary Applications
Jose Pedro G. Carvalho, Erica C. R. Carvalho, Denis E. C. Vargas, Patricia H. Hallak, Beatriz S. L. P. Lima, Afonso C. C. Lemonge
Summary: This paper discusses multi-objective structural optimization problems with various objective functions, including weight, natural frequencies of vibration, maximum nodal displacement, and global stability of the structure. Differential evolution algorithms are used for optimization on different types of trusses and ground-structure systems, with multi-criteria decision-making being applied to extract solutions.
COMPUTERS & STRUCTURES
(2021)
Article
Computer Science, Artificial Intelligence
Vibhu Trivedi, Manojkumar Ramteke
Summary: A new hybrid variant of multi-objective differential evolution algorithm is developed in this study, which combines the abilities of DE/rand/1 strategy and adaptive social evolution algorithm to improve convergence speed and effectiveness. The algorithm outperforms other established algorithms in solving computationally intensive multi-objective optimization problems, showing better convergence with a relatively simple structure and no additional computational cost needed.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Peng Wang, Bing Xue, Jing Liang, Mengjie Zhang
Summary: By identifying relevant features, feature selection methods can maintain or improve classification accuracy and reduce dimensionality. This paper proposes a diversity-based multi-objective differential evolution approach to effectively handle the trade-offs between convergence and diversity. The method detects and removes irrelevant and weakly relevant features to reduce the search space and proposes a new binary mutation operator to produce better feature subsets. Experimental results show that the proposed method outperforms current popular multi-objective feature selection methods on 14 datasets with varying difficulty.
INFORMATION SCIENCES
(2023)
Article
Engineering, Multidisciplinary
Ying Hou, YiLin Wu, Zheng Liu, HongGui Han, Pu Wang
Summary: The DMODE-IEP algorithm improves optimization performance through dynamic adjustment based on evolution progress information. The convergence of the algorithm is proved using probability theory, and testing results demonstrate its superiority in optimization effectiveness compared to other multi-objective optimization algorithms.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2021)
Article
Multidisciplinary Sciences
Mingwei Fan, Jianhong Chen, Zuanjia Xie, Haibin Ouyang, Steven Li, Liqun Gao
Summary: In this paper, an improved multi-objective differential evolution algorithm (MOEA/D/DEM) based on a decomposition strategy is proposed to enhance the search performance for practical multi-objective nutrition decision problems. The algorithm utilizes a neighborhood intimacy factor and a new Gaussian mutation strategy to improve diversity and local search ability. Experimental results show that the proposed algorithm achieves better search capability and obtains competitive results compared to other multi-objective algorithms.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Yupeng Han, Hu Peng, Changrong Mei, Lianglin Cao, Changshou Deng, Hui Wang, Zhijian Wu
Summary: This paper proposes a new multistrategy multiobjective differential evolutionary algorithm, RLMMDE, to solve the exploration and exploitation dilemma in multiobjective optimization problems (MOPs). The algorithm utilizes a multistrategy and multicrossover DE optimizer, an adaptive reference point activation mechanism based on RL, and a reference point adaptation method. Experimental results show that RLMMDE outperforms some advanced MOEAs on benchmark test suites and practical mixed-variable optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Automation & Control Systems
Vikas Palakonda, Jae-Mo Kang
Summary: This article proposes a preference-inspired differential evolution algorithm for multi and many-objective optimization, which effectively deals with a wide range of problems. The algorithm generates individuals with good convergence and distribution properties by utilizing a preference-inspired mutation operator and determining local knee points based on a clustering method. Experimental results demonstrate its superior performance compared to eight state-of-the-art algorithms on 35 benchmark problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Energy & Fuels
Lucas de Landa Couto, Nicolas Estanislau Moreira, Josue Yoshikazu de Oliveira Saito, Patricia Habib Hallak, Afonso Celso de Castro Lemonge
Summary: In this paper, a composite wind turbine blade is subjected to multi-objective structural optimization considering resonance and global stability. The NSGA-II algorithm is used to solve the optimization problems and non-dominated solutions are extracted. The study is expected to contribute to the multi-objective optimization and structural design of wind turbine blades.
Article
Automation & Control Systems
Yara Quilles Marinho, Fabiano Fruett, Mateus Giesbrecht
Summary: This research proposes a method of vibration spectrum analysis using twin-microaccelerometers, which is tuned by adjusting the actuation voltages amplitudes. The advantages and disadvantages of this strategy, as well as the tuning results using different variations of the Generalized Differential Evolution algorithm, are discussed.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Operations Research & Management Science
Djaafar Zouache, Fouad Ben Abdelaziz
Summary: In this study, a guided differential evolution algorithm is proposed to solve many-objective optimization problems by using strengthened dominance relation and bi-goal evolution. The guided search strategy, which utilizes adapted differential evolutionary operators for crossover and mutation, allows convergence towards the Pareto front with good solution diversity.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Carlos Eduardo da Silva Santos, Renato Coral Sampaio, Leandro dos Santos Coelho, Guillermo Alvarez Bestard, Carlos Humberto Llanos
Summary: The study introduces a multiobjective metaheuristic, APMT-MODE, for addressing the parameters selection problem in SVM and SVR, which is capable of providing more accurate and straightforward solutions under simple kernel functions and has been successfully applied in a real case study.
PATTERN RECOGNITION
(2021)
Article
Thermodynamics
Yuyang Yuan, Xuesheng Wang, Xiangyu Meng, Zhao Zhang, Jiaming Cao
Summary: The study proposed an optimal design strategy for helical coils based on differential evolution algorithm, with numerical investigations on Nu, f', and Ns changes and the establishment of a multi-objective optimization model optimizing design parameters. It was found that Differential Evolution provides better global optimal solutions, especially when minimizing Ns for heat transfer optimization.
INTERNATIONAL JOURNAL OF THERMAL SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoliang Ma, Zhitao Huang, Xiaodong Li, Lei Wang, Yutao Qi, Zexuan Zhu
Summary: This article introduces a merged differential grouping (MDG) method, which is a divide-and-conquer strategy to solve large-scale global optimization problems. By decomposing the problem into manageable subproblems and using binary search to group variables, the method improves the efficiency and accuracy of problem decomposition.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Sheng Xin Zhang, Shao Yong Zheng, Li Ming Zheng
Summary: This paper proposes an objective-dimension feedback (ODF) method with two novel mechanisms to enhance the performance of differential evolution. The experiments confirm the effectiveness of the ODF method compared to single utilization of objective and dimension space knowledge, single utilization of dimensional learning strategies, and other literature-based methods.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Dong Liu, Hao He, Qiang Yang, Yiqiao Wang, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a simple and effective mutation scheme named DE/current-to-rwrand/1 to enhance the optimization ability of differential evolution (DE) in solving complex optimization problems. The proposed mutation strategy, called function value ranking aware differential evolution (FVRADE), balances high diversity and fast convergence of the population. Experimental results demonstrate that FVRADE outperforms several state-of-the-art methods and shows promise in solving real-world optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Jose P. G. Carvalho, Afonso C. C. Lemonge, Erica C. R. Carvalho, Patricia H. Hallak, Heder S. Bernardino
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2018)
Article
Engineering, Aerospace
Eric Vargas Loureiro, Nicolas Lima Oliveira, Patricia Habib Hallak, Flavia de Souza Bastos, Lucas Machado Rocha, Rafael Grande Pancini Delmonte, Afonso Celso de Castro Lemonge
Summary: The increasing use of unmanned aerial vehicles and micro air vehicles creates a strong demand for the accurate aerodynamic performance of small-diameters fixed-pitch propellers, which can be analyzed using both low-fidelity methods like blade element momentum theory (BEMT) and high-fidelity methods like computational fluid dynamics (CFD). This study aims to employ these methods to analyze an APC propeller, highlighting the relationship between analysis method choice and Reynolds number, as well as the performance differences at different advance ratio velocities.
AEROSPACE SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Civil
Jose P. G. Carvalho, Afonso C. C. Lemonge, Patricia H. Hallak, Denis E. C. Vargas
Article
Thermodynamics
L. W. S. Crispim, F. C. Peters, J. Amorim, P. H. Hallak, M. Y. Ballester
Summary: A simulation study of cyanide production through electric discharge in a vehicular spark plug in dry air with typical outdoor CO2 levels is presented. The study uses a 2D theoretical and numerical framework, with simulations conducted at atmospheric pressure. The plasmo-chemical kinetics model includes 69 species and 710 collision processes, discussing principal pathways for cyanide formation and reporting spatial and temporal evolution of species of interest.
COMBUSTION AND FLAME
(2021)
Article
Computer Science, Interdisciplinary Applications
Jose Pedro G. Carvalho, Erica C. R. Carvalho, Denis E. C. Vargas, Patricia H. Hallak, Beatriz S. L. P. Lima, Afonso C. C. Lemonge
Summary: This paper discusses multi-objective structural optimization problems with various objective functions, including weight, natural frequencies of vibration, maximum nodal displacement, and global stability of the structure. Differential evolution algorithms are used for optimization on different types of trusses and ground-structure systems, with multi-criteria decision-making being applied to extract solutions.
COMPUTERS & STRUCTURES
(2021)
Article
Green & Sustainable Science & Technology
Manuel A. Rendon, Marcelo Assato, Vitor A. C. Martins, Patricia H. Hallak, Alexandre S. Altgott, Ricardo Graca, Zaire Landy, Nicolas Lima Oliveira, Grande Pancini Delmonte Rafael
Summary: This study focuses on developing design methods to transform existing aircraft propulsion systems into hybrid power and analyzing their performance. High fidelity models of the aircraft's components are created, and GasTurb is used to model the turboprop engines in both conventional and hybrid systems. MATLAB is then used to simulate steady state conditions and optimize the characteristics of the hybrid motor components.
JOURNAL OF CLEANER PRODUCTION
(2022)
Review
Computer Science, Artificial Intelligence
Erica C. R. Carvalho, Jose Pedro G. Carvalho, Heder S. Bernardino, Afonso C. C. Lemonge, Patricia H. Hallak, Denis E. C. Vargas
Summary: This paper investigates the conflicting objectives of minimizing the mass of a structure and maximizing its first natural frequency of vibration in structural design. The authors formulate and solve multi-objective structural optimization problems of trusses using various algorithms. The experiments demonstrate that the proposed methods achieve satisfactory results in optimizing truss structures.
EVOLUTIONARY INTELLIGENCE
(2022)
Article
Energy & Fuels
Lucas de Landa Couto, Nicolas Estanislau Moreira, Josue Yoshikazu de Oliveira Saito, Patricia Habib Hallak, Afonso Celso de Castro Lemonge
Summary: In this paper, a composite wind turbine blade is subjected to multi-objective structural optimization considering resonance and global stability. The NSGA-II algorithm is used to solve the optimization problems and non-dominated solutions are extracted. The study is expected to contribute to the multi-objective optimization and structural design of wind turbine blades.
Article
Engineering, Mechanical
Juliema Fronczak, Alexandre Miguel Silva Araujo, Gabriel Antonio Mendes das Flores, Lucas Lucinda de Sa, Alexandre Abrahao Cury, Patricia Habib Hallak
Summary: This paper presents a new approach to study wind-induced vibrations in structures, by combining structural dynamic systems with fluid mechanics. By analyzing the behavior of rectangular structures under static and dynamic conditions, satisfactory results were obtained.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Jose Pedro G. Carvalho, Denis E. C. Vargas, Breno P. Jacob, Beatriz S. L. P. Lima, Patricia H. Hallak, Afonso C. C. Lemonge
Summary: This paper formulates a multi-objective structural optimization problem and utilizes multiple evolutionary algorithms to solve it. By optimizing the grouping of structural members, the best truss structure can be found. After analyzing various benchmark problems, the study reveals the existence of competitive structural member configurations beyond symmetry-based groupings.
COMPUTERS & STRUCTURES
(2024)
Article
Engineering, Mechanical
Alexandre Miguel Silva Araujo, Juliema Fronczak, Gabriel Antonio Mendes das Flores, Lucas Lucinda de Sa, Alexandre Abrahao Cury, Patricia Habib Hallak
Summary: This study presents a methodology based on computational fluid dynamics to obtain flutter derivatives and critical flutter velocity. Two approaches were analyzed, with the first being more robust but computationally more expensive. The study was applied to different structures and validated with numerical and experimental studies. This research provides an assertive and pragmatic CFD methodology for obtaining critical flutter velocity on structures.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2023)
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)
