Article
Engineering, Electrical & Electronic
Xinyu Lin, Yingjie Zhou, Yipeng Liu, Ce Zhu
Summary: Detection of interest points, such as corners and blobs, is crucial for various vision tasks. Existing gradient-based methods lack sufficient utilization of gradient orientations. This letter proposes two robust interest point detectors that leverage level line differences and weights, showcasing superior performance in numerical experiments.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Lin, Wenjian Luo, Naijie Gu, Qingfu Zhang
Summary: This paper investigates the dynamic preferences of decision makers in multiobjective optimization problems and proposes an algorithm framework using a reference point change model. Experimental results show that the algorithm performs well in portfolio optimization problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Lin, Wenjian Luo, Naijie Gu, Qingfu Zhang
Summary: In the field of preference-based evolutionary multiobjective optimization, this paper focuses on multiobjective optimization problems with dynamic preferences of the decision maker (DM). Prior to proposing a change model of the reference point to simulate the change of the preference over time, a dynamic preference-based multiobjective evolutionary algorithm framework is designed. Experimental results on portfolio optimization problems demonstrate the superior performance of the proposed algorithm among compared optimization algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Kin Keung Lai, Mohd Hassan, Jitendra Kumar Maurya, Sanjeev Kumar Singh, Shashi Kant Mishra
Summary: This paper considers convex multiobjective optimization problems with equality and inequality constraints in real Banach space, establishing saddle point necessary and sufficient Pareto optimality conditions under certain constraint qualifications, and also discussing second order symmetric duality for nonlinear multiobjective mixed integer programs for arbitrary cones. The study also relates to necessary and sufficient optimality conditions for vector equilibrium problems on Hadamard manifolds by Ruiz-Garzon et al. in 2019.
Article
Engineering, Aerospace
Rohan Sharma, Kishan Patel, Sanyami Shah, Michal Aibin
Summary: This paper proposes a method using computer vision algorithms to improve railway maintenance, specifically by using aerial footage and the YOLO algorithm to identify points of interest. The results show a high accuracy in detecting missing ties, vegetation, and water pooling.
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
Computer Science, Artificial Intelligence
Zeneng She, Wenjian Luo, Xin Lin, Yatong Chang, Yuhui Shi
Summary: This paper focuses on the study of multiparty multiobjective optimization problems (MPMOPs) and proposes a new algorithm OptMPNDS3 to solve these problems. Comparisons with other algorithms on a problem suite show that OptMPNDS3 performs strongly and similarly.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Environmental Sciences
Jing Zhang, Da Xu, Yunsong Li, Liping Zhao, Rui Su
Summary: In this paper, a one-stage end-to-end network called FusionPillars is proposed to fuse multisensor data, including LiDAR point cloud and camera images. FusionPillars includes three branches: a point-based branch, a voxel-based branch, and an image-based branch. Experimental results revealed that, compared to existing one-stage fusion networks, FusionPillars yield superior performance, with a considerable improvement in the detection precision for small objects.
Article
Computer Science, Artificial Intelligence
Guo Yu, Lianbo Ma, Yaochu Jin, Wenli Du, Qiqi Liu, Hengmin Zhang
Summary: This article provides a comprehensive survey of knee-oriented optimization, focusing on the suggestion to target naturally interesting regions in solving multi-objective optimization problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Songbai Liu, Qiuzhen Lin, Ka-Chun Wong, Qing Li, Kay Chen Tan
Summary: This study compares existing optimizers for evolutionary large-scale multiobjective optimization (ELMO) on different benchmarks and finds that significant improvements are needed in both benchmarks and algorithms for ELMO. Therefore, a new test suite and optimizer framework are proposed to further advance ELMO research. The new benchmarks incorporate more realistic features challenging for existing optimizers, and the proposed optimizer, with a variable group-based learning strategy, shows distinct advantages in tackling these benchmarks.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Fangqing Gu, Haosen Liu, Yiu-ming Cheung, Hai -Lin Liu
Summary: This study proposes an adaptive constraint regulation method to balance the feasibility and convergence of solutions by adjusting the constraint violation of infeasible solutions. Experimental results demonstrate that the proposed method effectively achieves solution balance and improves solution diversity.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yatong Chang, Wenjian Luo, Xin Lin, Zhen Song, Carlos A. Coello Coello
Summary: This paper proposes the definition of the biparty multiobjective optimal power flow (BPMOOPF) problem and introduces a novel evolutionary biparty multiobjective optimization algorithm (BPMOOPF-EA) to solve the problem. Experimental results show that BPMOOPF-EA outperforms other algorithms in solving the MOOPF problem.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Dawei Ding, Xianheng Ding, Haibo Zhang, Shaoxu Ling
Summary: Population-based multiobjective metaheuristics have potential applications in microwave component designs for optimizing electric properties simultaneously and finding tradeoff designs efficiently. An efficient parallel implementation of MOEA/D is proposed to reduce computational time, and its performance is investigated through standard test instances. A complete design procedure based on the optimized algorithm is presented to locate multiple Pareto optimal designs of an antenna.
INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED 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
Computer Science, Artificial Intelligence
Lie Meng Pang, Hisao Ishibuchi, Ke Shang
Summary: Recent studies in evolutionary large-scale multiobjective optimization (ELMO) have found that increasing the number of decision variables can improve the experimental results for certain large-scale multiobjective test problems, and conventional evolutionary multiobjective optimization algorithms (EMOAs) outperform state-of-the-art ELMOAs in some cases. This suggests that ELMOAs are not always evaluated on appropriate test problems and their performance is not consistently better than conventional EMOAs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Chemistry, Analytical
Carlos Soubervielle-Montalvo, Oscar E. Perez-Cham, Cesar Puente, Emilio J. Gonzalez-Galvan, Gustavo Olague, Carlos A. Aguirre-Salado, Juan C. Cuevas-Tello, Luis J. Ontanon-Garcia
Summary: This study presents the design, implementation, and assessment of a low-power embedded system for real-time video tracking using an SoC-FPGA platform and the honeybee search algorithm. The findings demonstrate that the combination of SoC-FPGA and HSA reduces power consumption while maintaining computational precision, enabling portability.
Article
Computer Science, Artificial Intelligence
Gerardo Ibarra-Vazquez, Gustavo Olague, Mariana Chan-Ley, Cesar Puente, Carlos Soubervielle-Montalvo
Summary: This study compares the effects of adversarial attacks on deep convolutional neural networks and computer vision methods in the context of art media categorization. The results demonstrate that brain programming shows stronger robustness against adversarial examples.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Astronomy & Astrophysics
B. Hernandez-Valencia, J. H. Castro-Chacon, M. Reyes-Ruiz, M. J. Lehner, C. A. Guerrero, J. S. Silva, J. B. Hernandez-Aguila, F. Alvarez-Santana, E. Sanchez, J. M. Nunez, L. T. Calvario-Velasquez, Liliana Figueroa, C-K Huang, Shiang-Yu Wang, C. Alcock, W-P Chen, Agueda Paula Granados Contreras, J. C. Geary, K. H. Cook, J. J. Kavelaars, T. Norton, A. Szentgyorgyi, W-L Yen, Z-W Zhang, G. Olague
Summary: We present a new pipeline based on the Support Vector Machine algorithm for the confirmation and classification of small solar system objects through serendipitous stellar occultations. The pipeline analyzes light curves to identify occultation events and classify the occulting bodies based on their size and distance from the Sun. The study explores various parameters affecting occultation light curves and finds high detection and classification efficiency for certain conditions.
PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC
(2022)
Article
Computer Science, Artificial Intelligence
Gustavo Olague, C. I. C. E. S. E. Res Ctr CICESE Res Ctr
Summary: This article discusses the application of the optimal camera placement problem in photogrammetry and highlights its differences from the set cover problem. The document reviews bundle adjustment and network design, revealing the mistakes made by Kritter et al. in their survey.
APPLIED SOFT COMPUTING
(2022)
Article
Chemistry, Multidisciplinary
Gustavo Olague, Jose Armando Menendez-Clavijo, Matthieu Olague, Arturo Ocampo, Gerardo Ibarra-Vazquez, Rocio Ochoa, Roberto Pineda
Summary: Despite recent improvements in computer vision, the design of artificial visual systems remains challenging due to the lack of explanation for visual computing algorithms. Salient object detection is a unresolved problem due to difficulties in understanding the brain's inner workings. This research proposes an approach based on genetic programming and artificial evolution to enhance handcrafted techniques for solving salient object detection problems. The proposed methodology discovers critical structures in a template through artificial evolution, demonstrating outstanding results in benchmark tests.
APPLIED SCIENCES-BASEL
(2022)
Editorial Material
Computer Science, Artificial Intelligence
Gustavo Olague, Mario Koppen, Oscar Cordon
Summary: This article introduces the field of Evolutionary Computer Vision (ECV), which is at the intersection of computer vision (CV) and evolutionary computation (EC). ECV utilizes evolutionary algorithms and metaheuristic approaches combined with analytical methods to achieve human-competitive results. It aims to design software and hardware solutions for challenging CV problems and enhance our understanding of visual processing in nature.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Debbrota Paul Chowdhury, Sambit Bakshi, Chiara Pero, Gustavo Olague, Pankaj Kumar Sa
Summary: This article introduces an Industry 4.0 compliant ear biometric recognition method based on DenseNet. Compared to other biometric traits, ear recognition has been challenging due to limited images and the potential of deep learning is still unexplored. The proposed DenseNet achieves state-of-the-art results on challenging benchmarks and popular ear databases, showing better performance than existing methods. With fewer parameters and fast processing, this method can ensure privacy preservation over the Internet of Biometric Things.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Rocio Ochoa-Montiel, Humberto Sossa, Gustavo Olague, Carlos Sanchez-Lopez
Summary: An evolutionary vision approach is used for the automatic recognition of AML leukemia images in this study. Unlike common approaches, the feature extraction process in the presented model is transparent, and the obtained solutions are interpretable by human users.
COMPUTACION Y SISTEMAS
(2023)
Article
Computer Science, Artificial Intelligence
Leonardo Trujillo, Joel Nation, Luis Munoz, Edgar Galvan
Summary: This study proposes a novel method to determine the compatibility of two problems for transfer learning, and for the first time, studies within genetic programming. By comparing the feature space representations of problems, a similarity measure is computed, and the results show significant distinction between compatible and non-compatible problems for transfer learning.
Article
Multidisciplinary Sciences
Gerardo Ibarra-Vazquez, Maria Soledad Ramirez-Montoya, Mariana Buenestado-Fernandez, Gustavo Olague
Summary: This study used machine learning models to analyze open education competency data and predict the competency levels based on students' perceptions of knowledge, skills, and attitudes related to open education. The results showed that students' perceptions provided satisfactory data for building machine learning models to predict competency levels.
Article
Mathematics, Interdisciplinary Applications
Enrique Naredo, Candelaria Sansores, Flaviano Godinez, Francisco Lopez, Paulo Urbano, Leonardo Trujillo, Conor Ryan
Summary: Robotics technology has made significant advancements in various fields, particularly in manufacturing and navigation. This research aims to explore how training scenarios affect the learning process for autonomous navigation tasks, with a focus on whether the initial conditions have a positive or negative impact on developing general controllers. The study aims to optimize the training process and improve the quality of autonomous navigation controllers.
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS
(2023)
Article
Automation & Control Systems
J. Enriquez-Zarate, S. Gomez-Penate, C. Hernandez, Francisco Villarreal-Valderrama, R. Velazquez, Leonardo Trujillo
Summary: This article presents the design of a nonlinear hybrid controller for an underactuated Duffing oscillator with 2 degrees of freedom. The controller aims to reduce the frequency-response to specific resonant-frequencies while maintaining its robustness to external disturbances. Simulation results show that the proposed control scheme can reduce the system's response to external vibrations up to 83.88%.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2023)
Article
Computer Science, Information Systems
Dalia A. Rodriguez, Julia Diaz-Escobar, Arnoldo Diaz-Ramirez, Leonardo Trujillo
Summary: Violence against women is a significant social issue, and social media contains a large amount of misogynistic content. This study introduces a BERT architecture to automatically detect misogynistic tweets in Spanish, achieving good results. Manual error analysis revealed misogynistic bias in the dataset, and a debiased model outperformed existing literature on misogyny detection.
SOCIAL NETWORK ANALYSIS AND MINING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Rocio Ochoa-Montiel, Humberto Sossa, Gustavo Olague, Carlos Sanchez-Lopez
Summary: This study analyzes the performance of three commonly used classifiers in the brain programming symbolic learning model, showing that MLP and SVM classifiers are robust to noisy data, with MLP demonstrating the most stable behavior in the symbolic learning model.
PATTERN RECOGNITION, MCPR 2022
(2022)
Article
Mathematics, Interdisciplinary Applications
Mariela Cerrada, Leonardo Trujillo, Daniel E. Hernandez, Horacio A. Correa Zevallos, Jean Carlo Macancela, Diego Cabrera, Rene Vinicio Sanchez
Summary: This paper utilizes Auto Machine Learning (AutoML) tools to select proper features and models for three different gearbox failure modes. The performance of statistical condition indicators (SCI) extracted from vibration signals under different failure modes is analyzed using two AutoML systems. The models produced by both systems achieve very high performances, with accuracy up to 90%, and focus on similar subsets of features.
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS
(2022)
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)
