Article
Computer Science, Information Systems
Xue Feng, Anqi Pan, Zhengyun Ren, Zhiping Fan
Summary: Balancing convergence and diversity is a challenge in multi-objective optimization problems, especially when the proportion of feasible regions is low. This paper proposes a constrained multi-objective optimization algorithm based on a hybrid driven strategy to enhance the feasibility and diversity performance of Pareto solutions. The algorithm outperforms peer algorithms, especially in large-infeasible-regions multi-objective optimization problems.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Bing-Chuan Wang, Han-Xiong Li, Yun Feng, Wen-Jing Shen
Summary: The paper proposes an adaptive fuzzy penalty method to address the issue of tuning the penalty coefficient in constrained evolutionary optimization, adjusting the coefficient at both individual and population levels. By using differential evolution to design a search algorithm, the constrained optimization evolutionary algorithm AFPDE is proposed, showing competitiveness through experiments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Wei Li, Wenyin Gong, Fei Ming, Ling Wang
Summary: This paper proposes an improved two-archive-based evolutionary algorithm C-TAEA2, which achieved better performance for constrained multi-objective optimization problems by introducing new fitness evaluation, update, and mating selection strategies.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Asia Noureen, Wali Khan Mashwani, Faiz Rehman, Muhammad Sagheer, Habib Shah, Muhammad Asim
Summary: This study incorporates teaching learning-based optimization (TLBO) with superiority of feasibility (SF) and applies it to CEC-2006 constrained benchmark functions. Three constrained versions, including hybrid superiority of feasibility (HSF), are proposed. Among them, HSF-TLBO shows better performance on most of the constrained optimization problems.
Article
Computer Science, Artificial Intelligence
Huanrong Tang, Fan Yu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: The difficulty of solving constrained multi-objective optimization problems lies in balancing constraint satisfaction and objective optimization while considering the diversity of the solution set. In this study, a population state detection strategy and a restart scheme are proposed to address these issues. Experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art constrained multi-objective algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Mengjun Ming, Anupam Trivedi, Rui Wang, Dipti Srinivasan, Tao Zhang
Summary: The study introduces a dual-population-based evolutionary algorithm, c-DPEA, for constrained multiobjective optimization problems (CMOPs), which achieves a balance between convergence and diversity through the design of novel self-adaptive penalty and fitness functions. Extensive experiments demonstrate the superiority of c-DPEA over six state-of-the-art CMOEAs on most test problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Engineering, Multidisciplinary
Noha Hamza, Ruhul Sarker, Daryl Essam, Saber Elsayed
Summary: The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, a new evolutionary framework with multiple novel mechanisms is proposed to deal with such problems, and the results demonstrate its significant contribution in achieving good quality solutions, high feasibility rates, and fast convergence in rapidly changing environments.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Automation & Control Systems
Zhichao Sun, Hang Ren, Gary G. Yen, Tianfu Chen, Junjie Wu, Hongyang An, Jianyu Yang
Summary: In this article, an evolutionary algorithm with constraint relaxation strategy based on differential evolution algorithm (CRS-DE) is proposed to solve Highly Constrained Multiobjective Optimization Problems (HCMOPs). The algorithm relaxes the constraints by dividing the infeasible solutions into semifeasible subpopulation (SF) and infeasible subpopulation (IF), and devises corresponding reproduction and selection strategies for SF, IF, and feasible subpopulations. To prevent premature convergence, a mobility restriction mechanism is developed to restrict the individuals in SF and IF from entering the feasible subpopulation and enhance the diversity of the whole population.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Physics, Multidisciplinary
Cheng Peng, Cai Dai, Xingsi Xue
Summary: This article proposes a many-objective evolutionary algorithm based on dual selection strategy (MaOEA/DS) to solve many-objective optimization problems in high-dimensional space. The algorithm introduces a new distance function and a point crowding-degree (PC) strategy to assess diversity and balance convergence. Experimental results demonstrate the superiority of the proposed algorithm in overall performance compared to other state-of-the-art algorithms.
Article
Computer Science, Information Systems
Genghui Li, Lindong Xie, Zhenkun Wang, Huajun Wang, Maoguo Gong
Summary: This paper proposes an evolutionary algorithm called IDRCEA, which utilizes an individual-distribution search strategy (IDS) and a regression-classification-based prescreening mechanism (RCP) to improve the ability to solve various complex and high-dimensional expensive optimization problems (EOPs). Experimental results validate the advantages of IDRCEA over some state-of-the-art surrogate-assisted evolutionary algorithms (SAEAs) on many complex benchmark problems and an oil reservoir production optimization problem.
INFORMATION SCIENCES
(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, Artificial Intelligence
Jun Dong, Wenyin Gong, Fei Ming, Ling Wang
Summary: One of the key issues in solving constrained multi-objective optimization problems is balancing convergence, diversity, and feasibility. This paper proposes a two-stage constrained multi-objective evolutionary algorithm with different emphases on the three indicators. Experimental results demonstrate that the proposed algorithm achieves significant improvements on most benchmark problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Mingming Xia, Minggang Dong
Summary: This paper proposes a novel two-archive evolutionary algorithm for constrained multi-objective optimization problems with small feasible regions. The algorithm achieves a balance between convergence, diversity, and feasibility through mechanisms such as cooperation-based mating selection, high-quality solution selection, dynamic selection strategy, and ideal point replacement. Comprehensive experiments demonstrate the superiority of the proposed algorithm in terms of increment p and hypervolume compared to state-of-the-art algorithms.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jinlong Zhou, Juan Zou, Jinhua Zheng, Shengxiang Yang, Dunwei Gong, Tingrui Pei
Summary: This paper proposes an infeasible solutions diversity maintenance strategy for solutions with constraint violations degree greater than epsilon. Experimental results demonstrate that our proposed algorithm is highly competitive with other state-of-the-art algorithms for constrained multiobjective optimization problems.
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, Software Engineering
Xinyi Yu, Hanxiong Li, Haidong Yang
Summary: This paper proposes an algorithm for low-light image enhancement that addresses the issues of noise and color distortion by integrating denoising and color restoration. Experimental results demonstrate that this method greatly improves the quality of low-light images.
Article
Automation & Control Systems
Zhong-Yi Shui, Xu-Hao Li, Yun Feng, Bing-Chuan Wang, Yong Wang
Summary: The parameters of a lithium-ion battery are crucial for an effective battery management system. Parameter estimation using the pseudo-two-dimensional (P2D) model is more cost-effective than direct measurement methods, but the simulation of the P2D model is time-consuming. To overcome this, a two-phase surrogate model-assisted parameter estimation algorithm (TPSMA-PEAL) is proposed, combining the advantages of reduced-order and data-driven models. TPSMA-PEAL addresses challenges such as overfitting and low observability using differential evolution and parameter sensitivity analysis. Simulations and experiments demonstrate the efficiency and accuracy of TPSMA-PEAL.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Lei Lei, Han-Xiong Li, Hai-Dong Yang
Summary: This article proposes a multiscale convolution-based detection methodology for classifying defects in bare printed circuit boards (PCBs) under uncertainty. A novel window-based loss function is designed to tackle inter-class imbalance and uncertainty. A multiscale convolution network is constructed to process defects with intra-class variance, and large scale extraction features are fused on the small scale to guide the extraction process. Experimental studies demonstrate that the proposed methodology achieves satisfactory detection performance and visual interpretability compared to baseline methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Xinyi Yu, Han-Xiong Li, Haidong Yang
Summary: Surface defect detection of printed circuit boards (PCBs) is a critical stage in ensuring product quality. Existing defect detection methods using deep learning models are limited by image uncertainty and label uncertainty. This paper proposes a novel collaborative learning classification model that addresses these difficulties. Results show that the proposed model achieves excellent performance on various quantitative metrics.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Yun Feng, Yaonan Wang, Qin Wan, Xiaogang Zhang, Bing-Chuan Wang, Han-Xiong Li
Summary: So far, fault detection for distributed parameter systems (DPSs) has been mostly model-based and heavily reliant on prior known model information, limiting their usability in industrial applications. In this article, a brand-new framework is proposed for online systems modeling and fault detection of unknown high-dimensional DPSs. The framework includes an interaction between the two parts, where the systems modeling error is transformed into residual signals for fault detection and the online modeling switches to offline mode based on fault-detection results. The effectiveness of the proposed method is validated through experiments on sensor fault diagnosis for the thermal process of a 2-D battery cell.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Zhijia Zhao, Yiming Liu, Tao Zou, Keum-Shik Hong, Han-Xiong Li
Summary: In this study, a novel adaptive fault-tolerant control strategy is proposed to address the vibration issues in marine risers, ensuring system stability and performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xian-Bing Meng, C. L. Philip Chen, Han-Xiong Li
Summary: In industrial applications, the modeling and online applications of distributed parameter systems (DPSs) are difficult due to their infinite dimension, spatiotemporal coupled dynamics, nonlinearity, and model uncertainties. To address these issues, an online spatiotemporal modeling method is proposed based on confidence-aware multiscale learning. The proposed method integrates evolutionary learning-based spatial basis function, efficient broad learning system for temporal dynamics, and Gaussian process regression for spatiotemporal-scale learning to enable online confidence-aware prediction for DPSs. Experiments based on the curing process in snap curing oven demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Zhijia Zhao, Yiming Liu, Ge Ma, Keum-Shik Hong, Han-Xiong Li
Summary: In this article, a new adaptive fuzzy fault-tolerant control (FTC) is proposed for a three-dimensional riser-vessel system with unknown backlash nonlinearity. A model for the smooth inverse dynamics of the backlash is introduced and the control input is divided into an expected input and a compensation error. Fuzzy adaptive technology is employed to achieve compensation considering the imprecision of system modeling and unknown external disturbances. The simulation results demonstrate the effectiveness of the derived scheme.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Zhijia Zhao, Yiming Liu, Sentao Cai, Zhifu Li, Yiwen Wang, Keum-Shik Hong, Han-Xiong Li
Summary: This article proposes an adaptive control method for a flexible manipulator to deal with distributed disturbances, unknown dead zones, and input quantization. The unknown dead zone and input quantization are formulated and represented based on essential transformations. An adaptive robust quantized control with online updating laws is developed to ensure robustness, angle position, and reduce vibration. The Lyapunov theoretical analysis is employed to ensure bounded stability. Numerical simulations and experiments are conducted to verify the feasibility and superiority of the proposed method using a Quanser platform.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yun Feng, Yaonan Wang, Yang Mo, Yiming Jiang, Zhijie Liu, Wei He, Han-Xiong Li
Summary: Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known. However, it is common that accurate first-principles physical models are difficult to obtain for many industrial applications. Therefore, the applicability of traditional model-based methods is limited. In this study, an adaptive neural network (AdNN) is constructed to estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Experimental results validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Yun Feng, Yaonan Wang, Bing-Chuan Wang, Li Ding
Summary: The global spread of COVID-19 has drawn attention to epidemic control. Researchers aim to control the epidemic by adjusting the weights of the epidemic spreading networks, using evolutionary algorithms to optimize the weights' adaptation. They propose a constrained cooperative coevolution strategy to address the issue of increasing problem dimension.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Yaxin Wang, Han-Xiong Li, Shengli Xie
Summary: In this article, a spatial model predictive control (MPC) approach is proposed for a nonlinear distributed parameter system (DPS). A data-driven modeling method is utilized to predict the system performance, and a dual adaptation approach is developed to capture the most recent dynamics. Theoretical analysis guarantees stability, and simulations/experiments demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Jinhui Zhou, Wenjing Shen, Zhengwei Ma, Xiaolin Mou, Yu Zhou, Han-Xiong Li, Liqun Chen
Summary: This article proposes a Chebyshev-Galerkin-based thermal fault detection and localization framework for the pouch-type Li-ion battery under limited sensing. The method utilizes Chebyshev functions to construct spatial basis functions and derives time coefficients through the Galerkin method. The proposed method demonstrates effectiveness in fault detection and localization through simulations and experiments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
