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, Artificial Intelligence
Peidi Wang, Yongjie Ma
Summary: The DMOEA is a powerful solver for DMOPs, but the current algorithms lack strategies in both the environment response and static optimization stages. To address this, a new algorithm was proposed that incorporates different strategies in both stages to balance convergence and diversity. The algorithm uses nondominated solutions-guided evolution in the static optimization stage and fine prediction strategy in the environment response stage to improve performance in dynamic environments.
APPLIED INTELLIGENCE
(2023)
Article
Automation & Control Systems
Weifeng Gao, Genghui Li, Qingfu Zhang, Yuting Luo, Zhenkun Wang
Summary: A two-phase evolutionary algorithm is proposed to find multiple solutions of a nonlinear equations system. The algorithm transforms the nonlinear equations system into a multimodal optimization problem by combining multiobjective optimization technique and niching technique in phase one, and using a detection method and a local search method in phase two to encourage convergence. Experimental results show that the proposed algorithm outperforms other state-of-the-art algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Construction & Building Technology
Faegheh Moazeni, Javad Khazaei
Summary: This study introduces a multiobjective optimization formulation to optimize the demand, leakage, water age, and energy consumption of water distribution systems. The use of the Pareto navigator approach allows for the attainment of the final Pareto optimal solution for water network, leading to significant reductions in leakage, water age, water usage, and energy consumption. Additionally, the design of the energy system based on isolated energy units aims to minimize dependency on fossil-fuel-based power plants, while improving the carbon footprint of water distribution systems and their impact on climate change.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
Ke Shang, Hisao Ishibuchi, Linjun He, Lie Meng Pang
Summary: This article provides a comprehensive survey on the hypervolume indicator widely used in the field of evolutionary multiobjective optimization. The goal is to help researchers deepen their understanding of the principles and applications of the hypervolume indicator, and to promote further utilization of it.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Automation & Control Systems
Hong-Gui Han, Cong Chen, Hao-Yuan Sun, Jun-Fei Qiao
Summary: This article proposes a multi-objective integrated optimal control (MIOC) strategy for nonlinear systems, which achieves coordinate optimal control through a comprehensive cost function and collaborative optimization algorithm, and improves the operation and control performance of the systems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Bing-Chuan Wang, Han-Xiong Li, Qingfu Zhang, Yong Wang
Summary: This paper utilizes decomposition-based multiobjective optimization to solve constrained optimization problems and introduces a restart strategy to enhance the optimization performance of the population. Extensive experiments on benchmark test functions demonstrate that the proposed method shows better or at least competitive performance against other state-of-the-art methods.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Geochemistry & Geophysics
Chuanlong Ye, Fazhi He, Jinkun Luo, Lyuyang Tong, Xiaoxin Gao, Tongzhen Si, Linkun Fan
Summary: This article proposes a multistrategy evolutionary multiobjective method based on roulette wheel selection and the genetic algorithm (RWS-GA) for hyperspectral endmember extraction. The method designs two parallel algorithms corresponding to global exploration and local exploitation. Experimental results show that the proposed method outperforms other endmember extraction methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(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
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
Jing-Yu Ji, Man Leung Wong
Summary: This study attempts to solve nonlinear equation systems using decomposition-based multiobjective optimization. By transforming the system into a bi-objective optimization problem using reference points, an improved decomposition algorithm is applied for solving. Experimental results demonstrate the superior performance and shorter execution time of the proposed method compared to other algorithms.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Gustavo A. Prudencio de Morais, Lucas Barbosa Marcos, Filipe Marques Barbosa, Bruno H. G. Barbosa, Marco Henrique Terra, Valdir Grassi
Summary: This study proposes a robust recursive controller designed via multiobjective optimization to overcome the challenges of system uncertainties, along with a local search method for multiobjective optimization problems. This method is applicable to any established multiobjective evolutionary algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
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
Wei Zhou, Liang Feng, Kay Chen Tan, Min Jiang, Yong Liu
Summary: Dynamic multiobjective optimization problem refers to a multiobjective optimization problem that varies over time. To solve this kind of problem, evolutionary search with prediction approaches have been developed to estimate the changes in the problem. However, existing prediction methods only focus on the change in the decision space. In this article, a new approach is proposed that conducts prediction from both the decision and objective spaces. Experimental results show the effectiveness of the proposed method in solving both benchmark and real-world DMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Kai Zhang, Gary G. Yen, Zhenan He
Summary: In this article, a recursive evolutionary algorithm EvoKnee(R) is proposed to directly search for global knee solutions and multiple local knee solutions using the minimum Manhattan distance approach, instead of a large number of Pareto optimal solutions. Unlike traditional approaches, only nondominated solutions in rank one are preserved in each generation, reducing computational cost and allowing quick convergence to knee solutions.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
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
Computer Science, Artificial Intelligence
Qian-Qian Li, Zi-Peng Wang, Tingwen Huang, Huai-Ning Wu, Han-Xiong Li, Junfei Qiao
Summary: This article addresses fault-tolerant stochastic sampled-data fuzzy control for nonlinear delayed parabolic PDE systems under spatially point measurements. A T-S fuzzy PDE model is used to accurately describe the system. A fault-tolerant SD fuzzy controller with stochastic sampling is designed considering possible actuator failure, and a novel time-dependent Lyapunov functional is constructed to obtain sufficient conditions for the mean square exponential stability of the closed-loop system based on linear matrix inequalities. The effectiveness of the designed approach is illustrated through three examples.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Peng Wei, Han-Xiong Li
Summary: In this article, a spatiotemporal entropy method is proposed to detect and locate thermal abnormalities of Li-ion battery (LIB) packs. The spatial entropy and temporal entropy are constructed from different scales based on Karhunen-Loeve (KL) decomposition, and then integrated into the comprehensive spatiotemporal entropy. The kernel density estimation is used to derive the detection threshold, and the entropy contribution function is designed for abnormality localization. Experimental results demonstrate the effectiveness of the proposed method in timely detecting and precisely locating abnormal cells at the early stage.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
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
Zhongwei Ma, Yong Wang
Summary: This article presents a new constraint-handling technique called shift-based penalty (ShiP) for solving constrained multiobjective optimization problems. ShiP utilizes a two-step process by shifting infeasible solutions towards feasible solutions and penalizing them based on constraint violations. ShiP encourages diverse feasible solutions during the early stage of evolution and promotes convergence towards Pareto optimal solutions in the later stage. The effectiveness of ShiP is demonstrated through experiments on benchmark test problems and its application in vehicle scheduling.
IEEE TRANSACTIONS ON CYBERNETICS
(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
Pei-Qiu Huang, Yong Wang, Kezhi Wang, Qingfu Zhang
Summary: This article studies an intelligent reflecting surface (IRS)-aided communication system under the time-varying channels and stochastic data arrivals. It proposes a method called LETO that combines Lyapunov optimization with evolutionary transfer optimization (ETO) to solve the optimization problem. LETO decouples the long-term optimization problem into deterministic optimization problems in short time slots, ensuring queue stability, and then solves the optimization problem in each time slot using the evolutionary transfer method to achieve real-time decisions.
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
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)
