Article
Green & Sustainable Science & Technology
Iman Ahmadianfar, Ali Kheyrandish, Mehdi Jamei, Bahram Gharabaghi
Summary: An adaptive differential evolution with particle swarm optimization (A-DEPSO) algorithm is developed to derive optimal operating rules for multi-reservoir systems in hydropower generation, showing improved performance compared to other well-known optimizers in the literature.
Article
Computer Science, Information Systems
Zhenyu Meng, Cheng Yang
Summary: DE algorithm is a powerful evolutionary algorithm for global optimization with great success in engineering applications. However, existing DE variants have two main weaknesses in mutation strategy and parameter control. To address these weaknesses, a novel Hip-DE algorithm is proposed with historical population-based mutation strategy and parameter adaptive mechanisms for performance enhancement.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jian Feng, Shaoning Liu, Shengxiang Yang, Jun Zheng, Jinze Liu
Summary: Convergence, diversity, and feasibility are crucial factors in solving constrained multi-objective optimization problems. This paper proposes an adaptive tradeoff evolutionary algorithm (ATEA) to achieve a balance between convergence and diversity while ensuring population feasibility.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
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
Mathematics
Wenchao Yi, Zhilei Lin, Youbin Lin, Shusheng Xiong, Zitao Yu, Yong Chen
Summary: This paper proposes an epsilon-constrained method based on adaptive differential evolution to solve the optimal power flow problems. The effectiveness of the proposed algorithm is verified through single and multi-objective tests on the IEEE 30-bus test system. The comparison with state-of-the-art algorithms illustrates significant improvements.
Article
Computer Science, Artificial Intelligence
Kangjia Qiao, Jing Liang, Kunjie Yu, Minghua Yuan, Boyang Qu, Caitong Yue
Summary: The paper introduces a self-adaptive resources allocation-based differential evolution (SRADE) to balance diversity, convergence, constraints, and objective function in addressing constrained optimization problems. By dynamically assigning different mutation strategies to individuals based on their performance feedback, the method effectively improves search efficiency under limited resources by focusing on the most efficient strategy.
KNOWLEDGE-BASED SYSTEMS
(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
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
Hu Peng, Yupeng Han, Changshou Deng, Jing Wang, Zhijian Wu
Summary: Engineering design problems with mixed-variable optimization problems (MVOPs) pose challenges due to non-linear functions, complex constraints, and a mix of continuous and discrete variables. This study introduces a new variation of differential evolution called MCDEmv, which combines multi-strategy co-evolution and statistics-based local search to effectively solve MVOPs. Experimental results demonstrate the superior performance and efficiency of MCDEmv compared to similar algorithms in handling mixed-variable engineering design problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
S. Brindha, S. Miruna Joe Amali
Summary: This article presents an improved Multi-objective Differential Evolution based algorithm named FAMDE-DC, which utilizes fuzzy system to control population diversity and identifies potential candidates in decision and objective spaces, achieving true Pareto-optimal solutions. The algorithm exhibits good performance and does not require manual parameter tuning.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoyu Li, Lei Wang, Qiaoyong Jiang, Ning Li
Summary: The research proposes a new differential evolution variant (MPMSDE) which utilizes multi-population cooperation and multi-strategy integration to allocate computational resources effectively. Experimental results demonstrate that the MPMSDE algorithm is highly competitive in terms of calculation accuracy and convergence speed.
Article
Computer Science, Artificial Intelligence
Liyun Fu, Haibin Ouyang, Chengyun Zhang, Steven Li, Ali Wagdy Mohamed
Summary: This paper proposes a constrained cooperative adaptive multi-population differential evolutionary (CCAM-PDE) algorithm for solving large-scale and complex constrained engineering optimization problems. The algorithm enhances the global search capability by introducing a dynamic constraint handling region and an improved population generation scheme, as well as improves the differential evolution algorithm through constraint handling technology. Experimental results demonstrate that the CCAM-PDE algorithm performs well in constraint handling efficiency, global search capability, and convergence speed.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Zhiping Tan, Yu Tang, Huasheng Huang, Shaoming Luo
Summary: Differential evolution (DE) is an efficient evolutionary algorithm for solving continuous or discrete numerical optimization problems. This paper proposes a dynamic fitness landscape-based adaptive mutation strategy selection differential evolution (DFLDE) algorithm, which selects the optimal mutation strategy based on the dynamic fitness landscape characteristics of each optimization problem. Experimental results show that DFLDE outperforms five well-known DE variants in terms of searching for the optimal value, convergence speed, and robustness.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jing-Yu Ji, Sanyou Zeng, Man Leung Wong
Summary: Constrained multiobjective optimization problems are commonly encountered in real-world applications. This study proposes an improved e-constrained multiobjective differential evolution algorithm to address these problems and demonstrates its superior performance on benchmark test functions.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Libao Deng, Yifan Qin, Chunlei Li, Lili Zhang
Summary: Differential evolution (DE) is an efficient global optimization algorithm, but its random nature can cause individuals to deviate from the global optima, leading to a waste of computing resources. To address this, we propose an adaptive mutation strategy correction framework (AMSC) for DEs. AMSC splits the population into two subpopulations and designs auxiliary mutant vectors to enhance their exploration and exploitation abilities. Additionally, an adaptive cooperative rule is introduced to balance exploration and exploitation based on the crossover rates. Experimental results show that AMSC significantly improves DEs' performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
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)