Article
Computer Science, Artificial Intelligence
Xiaobing Yu, Wenguan Luo, WangYing Xu, ChenLiang Li
Summary: This study addresses the issue of selecting feasible and infeasible solutions in Constrained Multi-objective Optimization Problems (CMOPs) by developing a constrained multi-objective Differential Evolution (DE) algorithm. The experiments demonstrate that the algorithm can find well-distributed Pareto front and achieve superior performance indicator results.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jianchao Cheng, Zhibin Pan, Hao Liang, Zhaoqi Gao, Jinghuai Gao
Summary: DE is a global optimization algorithm that relies on mutation operation and individual positions. This paper introduces a new mutation operator, FDDE, which assigns suitable positions by considering both individuals' fitness and diversity contributions. The experimental results show that FDDE outperforms its competitors in convergence performance on various benchmark sets.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
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
Computer Science, Artificial Intelligence
Libao Deng, Chunlei Li, Yanfei Lan, Gaoji Sun, Changjing Shang
Summary: The paper introduces a novel differential evolution algorithm DCDE based on a dynamic combination mutation operator and a two-level parameter regulation strategy. By dynamically combining mutation operator and control parameters, DCDE achieves a balance between global exploration ability and local exploitation ability, demonstrating superior performance compared to state-of-the-art DE variants and non-DE algorithms on IEEE CEC 2017 benchmark suite.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Qian Wang, Lu Chen, Xiaoguang Li, Xuexian Li
Summary: This paper proposes a new multi-objective particle swarm optimization algorithm with a dynamic neighborhood balancing mechanism (DNB-MOPSO) to solve the multi-modal multi-objective optimization problems with the same fitness value for Pareto-optimal solutions. The algorithm balances local and global search using an adaptive parameter adjustment strategy and employs a mutation operator to escape from local optima. Additionally, a dynamic neighborhood reform strategy based on current niching methods is implemented to enhance exploration and maintain population diversity. Experimental results demonstrate the superiority of the proposed algorithm in locating more optimal solutions in the decision space.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Chemical
Samira Ghorbanpour, Yuwei Jin, Sekyung Han
Summary: An adaptive Grid-based Multi-Objective Differential Evolution algorithm is proposed in this paper to address multi-objective optimization, aiming to improve algorithm performance by performing mutation strategy in a grid environment and considering performance metrics.
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
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
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
Multidisciplinary Sciences
Tassawar Ali, Hikmat Ullah Khan, Tasswar Iqbal, Fawaz Khaled Alarfaj, Abdullah Mohammad Alomair, Naif Almusallam
Summary: Differential evolution is an evolutionary algorithm that balances exploration and exploitation to find the optimal genes for an objective function. To address the challenge of finding this balance, a clustering-based mutation strategy called Agglomerative Best Cluster Differential Evolution (ABCDE) is proposed. ABCDE efficiently converges without getting trapped in local optima by clustering the population and avoiding poor-quality genes through adaptive crossover rate. ABCDE outperforms classical mutation strategies and random neighborhood mutation strategy in generating a population where the difference between the values of the trial vector and objective vector is less than 1% for some benchmark functions. The optimal and fast convergence of differential evolution has potential applications in weight optimization of artificial neural networks and stochastic/time-constrained environments like cloud computing.
Article
Computer Science, Hardware & Architecture
Dahai Xia, Xinyun Wu, Meng Yan, Caiquan Xiong
Summary: This paper presents an innovative approach called the adaptive stochastically ranking-based tournament selection method (ASR-TS), which combines tournament selection and stochastic ranking to strike a balance between exploration and exploitation. The experimental results demonstrate that the proposed ASR-TS method outperforms various other methods, proving its efficiency and effectiveness.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Hafiz Tayyab Rauf, Jiechao Gao, Ahmad Almadhor, Ali Haider, Yu-Dong Zhang, Fadi Al-Turjman
Summary: A novel variant of differential evolution called MPC-DE is proposed to solve multi-model and multi-objective optimization problems. It utilizes multiple selection strategies and chaotic mapping methods for population initialization and mutation. The performance of MPC-DE is evaluated on benchmark problems and compared with recent DE variants, showing superior results for multi-objective optimization problems and the economic load dispatch problem.
APPLIED SOFT COMPUTING
(2023)
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
Geying Yang, Junjiang He, Lina Wang, Bo Zeng, Tian Wu
Summary: This study proposes a novel multi-objective immunization algorithm based on dynamic variation distance, which ensures the uniform distribution of optimal solutions, designs a new evolutionary strategy, and explores adaptive handling of infeasible solutions. Experimental results demonstrate the superiority of the proposed algorithm over existing algorithms in most tested problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Naveen Saini, Diksha Bansal, Sriparna Saha, Pushpak Bhattacharyya
Summary: In this study, the Search Results Clustering problem is treated as a multi-view clustering problem and solved through optimization. Various views based on syntactic and semantic similarity measures are considered, with three new views incorporated in the framework. Experimental results show that the proposed approach outperforms existing techniques.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Xu Chen, Xuan Wei, Guanxue Yang, Wenli Du
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Agriculture, Multidisciplinary
Kangji Li, Zhengdao Sha, Wenping Xue, Xu Chen, Hanping Mao, Gang Tan
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2020)
Letter
Computer Science, Information Systems
Kangji Li, Xianming Xie, Wenping Xue, Xu Chen
SCIENCE CHINA-INFORMATION SCIENCES
(2020)
Article
Automation & Control Systems
Wenxiang Zhao, Anqi Ma, Jinghua Ji, Xu Chen, Tian Yao
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2020)
Article
Thermodynamics
Xu Chen
Article
Computer Science, Artificial Intelligence
Xu Chen, Hugo Tianfield, Wenli Du
Summary: This paper introduces a novel bee-foraging learning PSO (BFL-PSO) algorithm with three different search phases, showing very competitive performance in terms of solution accuracy.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Wenxiang Zhao, Tian Yao, Liang Xu, Xu Chen, Xinxin Song
Summary: This article presents a modular linear permanent-magnet vernier machine optimized for high precision and safety-critical direct-drive applications. The machine features fault-tolerant capability and improved force performance, achieved through a modular mover structure and a new multi-objective optimization method. The optimization process involves comprehensive sensitivity analysis, combined approximation models, and a multi-objective differential evolution algorithm, resulting in desired performance with high efficiency and accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Thermodynamics
Xu Chen, Guowei Tang
Summary: An improved competitive swarm optimization (ImCSO) algorithm is proposed in this paper to solve Multi-area Economic Dispatch (MAED) problems, which enhances performance by introducing a ranking paired learning strategy and a differential evolution strategy. Experimental results show that the ImCSO algorithm has superior solution accuracy and reliability in solving MAED problems.
Article
Energy & Fuels
Wenqiang Yang, Zhanlei Peng, Zhile Yang, Yuanjun Guo, Xu Chen
Summary: An enhanced exploratory whale optimization algorithm (EEWOA) is proposed to solve the complex Dynamic Economic Dispatch (DED) problem efficiently and effectively, by enhancing population diversity and improving variable repairing ability. EEWOA shows significant advantages over several state-of-the-art optimization algorithms on various benchmarks and DED cases.
Article
Computer Science, Artificial Intelligence
Xu Chen, Anning Shen
Summary: In this study, an improved differential evolution algorithm called SDEGCM is proposed to tackle large-scale CHPED problems. The algorithm incorporates Gaussian-Cauchy mutation, parameter self-adaptation, and constraint repair techniques, demonstrating advantages in solution accuracy and stability compared to existing methods.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Food Science & Technology
Yuhan Ding, Yuli Yan, Jun Li, Xu Chen, Hui Jiang
Summary: This paper proposes a method for classifying tea quality levels based on near-infrared spectroscopy. The method includes obtaining absorbance spectra of tea samples, converting the spectral data to transmittance, dimensionally reducing the data using PCA, establishing a SVM classification model, and optimizing the model using PSO and CLPSO algorithms. The experimental results show high classification accuracy of 99.17% for the proposed method.
Article
Energy & Fuels
Xu Chen, Shuai Fang, Kangji Li
Summary: This study proposes a novel reinforcement-learning-based multi-objective differential evolution (RLMODE) algorithm to solve the combined heat and power economic emission dispatch problem. The RLMODE algorithm achieved smaller cost and emission values and better Pareto-optimal frontiers compared to four well-established multi-objective algorithms, particularly for large-scale problems.
Article
Computer Science, Theory & Methods
Xu Chen, Xueliang Miao, Hugo Tianfield
MULTIAGENT AND GRID SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Xu Chen, Kangji Li, Bin Xu, Zhile Yang
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Automation & Control Systems
Xiaoke Su, Hong Yue, Xu Chen
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2020)
Article
Automation & Control Systems
Haifei Peng, Jian Long, Cheng Huang, Shibo Wei, Zhencheng Ye
Summary: This paper proposes a novel multi-modal hybrid modeling strategy (GMVAE-STA) that can effectively extract deep multi-modal representations and complex spatial and temporal relationships, and applies it to industrial process prediction.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2024)
