Article
Energy & Fuels
Jianzhou Wang, Yilin Zhou, Zhiwu Li
Summary: This article presents a comprehensive system for solar photovoltaic power forecasting, which enhances the utility and stability of the predictive system through automatic optimization and multi-objective intelligent optimization algorithms. The study demonstrates that the designed system achieves high accuracy and stability in predicting photovoltaic power data.
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
Computer Science, Artificial Intelligence
Jinhua Zheng, Zhenfang Du, Juan Zou, Shengxiang Yang
Summary: In researching multi-objective evolutionary algorithms, a preference-based MOEA called MOEA/D-ND is proposed. It uses a normal distribution to generate a weight vector and incorporates the decision-maker's preference information to guide convergence. An angle-based niche selection strategy is adopted to prevent falling into local optima. Experimental results show that this algorithm outperforms in various benchmark problems with 2 to 15 goals.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Minshi Chen, Xin Xu, Gary G. Yen
Summary: The paper proposes an evolution strategy MMO-MOES for solving multimodal multi-objective optimization problems, focusing on searching for multiple groups of optimal solutions in decision space. By using a novel niching strategy and requiring a small population size, MMO-MOES is effective in finding well-distributed and well-converged Pareto optimal solutions. Experimental results show exceptional performance compared to leading-edge MMOEAs in various test problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Weifeng Gao, Yiming Wang, Lingling Liu, Lingling Huang
Summary: A gradient-based search method (GBSM) is developed to solve multi-objective optimization problems, utilizing multi-objective gradient information to construct Pareto descent directions (PDDs) and adopting a multi-objective evolutionary algorithm based on decomposition to improve diversity. Comparisons with other multi-objective evolutionary algorithms and gradient-based algorithms show that the proposed method is competitive and effective in solving benchmark functions.
INFORMATION SCIENCES
(2021)
Article
Multidisciplinary Sciences
Qianlin Ye, Zheng Wang, Yanwei Zhao, Rui Dai, Fei Wu, Mengjiao Yu
Summary: This paper proposes a clustering-based competitive multi-objective particle swarm optimizer called EGC-CMOPSO, which aims to quickly and accurately find a set of trade-off solutions. The enhanced grid mechanism in EGC-CMOPSO is designed to locate superior Pareto optimal solutions and hierarchical-based clustering is established to improve the accuracy rate of grid selection. EGC-CMOPSO is capable of solving multi-objective optimization problems with various Pareto front shapes.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Fei Wu, Wanliang Wang, Jiacheng Chen, Zheng Wang
Summary: The dynamic multi-objective optimization problem is a common problem in real life, and the dynamic multi-objective prediction method based on classification of decision variables (DVC) can better balance population diversity and convergence.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Xue Feng, Zhengyun Ren, Anqi Pan, Juchen Hong, Yinghao Tong
Summary: This paper proposes a multi-preference-based constrained multi-objective optimization algorithm, which determines preferences by analyzing evolution states and population characteristics, and implements them through reasonable scheduling evolutionary models. Compared to other algorithms, it performs better in diversity difficulty and convergence difficulty multi-objective optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Zhiwei Xu, Xiaoming Liu, Kai Zhang, Juanjuan He
Summary: This paper presents a novel multi-objective evolution strategy based on cultural transmission theory for solving multi-objective multi-task optimization problems. The proposed algorithm utilizes elite-guided variation strategy and horizontal cultural transmission strategy to improve convergence efficiency, and introduces an adaptive information transfer strategy to address negative transfer. Comprehensive experimental results demonstrate that the algorithm outperforms previous state-of-the-art multi-objective EMT algorithms.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Ahmed A. Ewees, Mohamed Abd Elaziz, Diego Oliva
Summary: This paper introduces a new multi-objective optimization method based on an improved algorithm, which combines different algorithms to achieve global exploration and exploitation of the search space. The results show that this method outperforms other existing algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Thermodynamics
Petronilla Fragiacomo, Giuseppe Lucarelli, Matteo Genovese, Gaetano Florio
Summary: This paper introduces an innovative multi-objective optimization model for CCHP poly-generative energy systems, which utilizes two levels of optimization to optimize the system's operating strategy and size. The model is highly flexible and allows for sensitivity analyses. Performance curves for fuel cell systems were validated with existing commercial units, showing a high degree of correlation.
Review
Computer Science, Artificial Intelligence
Tao Zhang, Wang Qi, Xin Zhao, Yuzheng Yan, Yahui Cao
Summary: This study establishes a multi-objective optimization model for local dimming tasks in a local dimming system, with objectives including reducing image distortion, improving image contrast ratio, and reducing system power consumption. By using an enhanced multi-objective evolutionary algorithm called MOEA/D-SFLA, better optimization results are achieved compared to traditional methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Cleber A. C. F. da Silva, Daniel Carneiro Rosa, Pericles B. C. Miranda, Filipe R. Cordeiro, Tapas Si, Andre C. A. Nascimento, Rafael F. L. Mello, Paulo S. G. de Mattos Neto
Summary: This study proposes a method that uses a multi-objective grammatical evolution framework to automatically generate and optimize CNNs for image classification problems. The results show that the proposed method is able to generate simpler networks that statistically outperform state-of-the-art CNNs in terms of accuracy and F1-score.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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
Pharmacology & Pharmacy
Xiao-Yi Zeng, Hao Wang, Fang Bai, Xiu Zhou, Song-Pei Li, Lu-Ping Ren, Ruo-Qiong Sun, Charlie C. L. Xue, Hua-Liang Jiang, Li-Hong Hu, Ji-Ming Ye
BRITISH JOURNAL OF PHARMACOLOGY
(2015)
Article
Chemistry, Medicinal
Fang Bai, Sha Liao, Junfeng Gu, Hualiang Jiang, Xicheng Wang, Honglin Li
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2015)
Article
Multidisciplinary Sciences
Fang Bai, Faruck Morcos, Yang-Sung Sohn, Merav Darash-Yahana, Celso O. Rezende, Colin H. Lipper, Mark L. Paddock, Luhua Song, Yuting Luo, Sarah H. Holt, Sagi Tamir, Emmanuel A. Theodorakis, Patricia A. Jennings, Jose N. Onuchic, Ron Mittler, Rachel Nechushtai
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2015)
Article
Multidisciplinary Sciences
Kendrick H. Yim, Thomas L. Prince, Shiwei Qu, Fang Bai, Patricia A. Jennings, Jose N. Onuchic, Emmanuel A. Theodorakis, Leonard Neckers
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2016)
Article
Multidisciplinary Sciences
Fang Bai, Faruck Morcos, Ryan R. Cheng, Hualiang Jiang, Jose N. Onuchic
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2016)
Article
Chemistry, Medicinal
Wei Zhou, Xiaofeng Liu, Zhengchao Tu, Lianwen Zhang, Xin Ku, Fang Bai, Zhenjiang Zhao, Yufang Xu, Ke Ding, Honglin Li
JOURNAL OF MEDICINAL CHEMISTRY
(2013)
Article
Multidisciplinary Sciences
Fang Bai, Yechun Xu, Jing Chen, Qiufeng Liu, Junfeng Gu, Xicheng Wang, Jianpeng Ma, Honglin Li, Jose N. Onuchic, Hualiang Jiang
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2013)
Article
Chemistry, Medicinal
Yu Zhou, Yan Fu, Wanchao Yin, Jian Li, Wei Wang, Fang Bai, Shengtao Xu, Qi Gong, Tao Peng, Yu Hong, Dong Zhang, Dan Zhang, Qiufeng Liu, Yechun Xu, H. Eric Xu, Haiyan Zhang, Hualiang Jiang, Hong Liu
Summary: A novel kinetics-driven drug design strategy was employed to discover new-generation AChE inhibitors with longer drug-target residence time and larger safety window. Compound 12 was identified as a highly potent, selective, orally bioavailable, and brain preferentially distributed AChE inhibitor, showing significant cognitive improvement in different mouse models. Phase I trials demonstrated good safety, tolerance, and pharmacokinetic profiles of 12, supporting its further investigation in phase II trials for AD treatment.
JOURNAL OF MEDICINAL CHEMISTRY
(2021)
Article
Chemistry, Medicinal
Qian Wang, Lin Wang, Yumin Zhang, XiangLei Zhang, Leike Zhang, Weijuan Shang, Fang Bai
Summary: This study reveals the globally cooperative conformational dynamics of the S protein of SARS-CoV-2 through coarse-grained molecular dynamic simulations, and discovers an allosteric path that correlates the motion of the receptor-binding domain with the motion of the subdomains of the S protein. Based on this finding, non-RBD binding modulators were designed to inhibit the conformational change of the S protein and inhibit SARS-CoV-2. The inhibitory effect and function stages of these modulators were evaluated experimentally.
JOURNAL OF MEDICINAL CHEMISTRY
(2022)
Article
Biochemistry & Molecular Biology
Shiwei Li, Sanan Wu, Lin Wang, Fenglei Li, Hualiang Jiang, Fang Bai
Summary: This article summarizes the major developments in computational methods for predicting protein-protein interactions (PPIs) using artificial intelligence algorithms. It covers the sources of experimental PPI data, PPI prediction methods based on sequential information, methods using structural information as input feature, and methods designed by combining different features. The state-of-the-art computational PPI prediction methods are reviewed for each category. The article also discusses the flaws in this field and future directions for next-generation algorithms.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Zhongneng Zhou, Zijing Chen, Xiu-Wen Kang, Yalin Zhou, Bingyao Wang, Siwei Tang, Shuhua Zou, Yifei Zhang, Qiaoyu Hu, Fang Bai, Bei Ding, Dongping Zhong
Summary: In this study, the photocycle of the FMN-Gln-Tyr motif in the BLUF domain of OaPAC was investigated using computational simulations. The results show that forward PCET is concerted, while only the wild-type protein proceeds with an ultrafast reverse PCET process.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Chemistry, Physical
Fang Bai, Hualiang Jiang
Summary: Traditional drug discovery focuses on binding affinity, but drug efficacy is also influenced by binding kinetics. The binding free energy landscape constructor can predict binding kinetics and provide insights into molecular mechanisms, aiding drug design.
JOURNAL OF PHYSICAL CHEMISTRY B
(2022)
Article
Chemistry, Medicinal
Lin Wang, Feng-lei Li, Xin-yue Ma, Yong Cang, Fang Bai
Summary: Protein-protein interactions (PPIs) play crucial roles in biological processes. PPI-Miner is a computational method that utilizes protein motifs as queries to identify potential protein interacting partners. It matches motifs to proteins and builds PPI complex structures based on the binding mode of the discovered motifs. PPI-Miner has applications in discovering PPIs and can be used in the rational design of molecular glues and protein vaccines.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Review
Chemistry, Medicinal
Steve Agajanian, Mohammed Alshahrani, Fang Bai, Peng Tao, Gennady M. Verkhivker
Summary: Allosteric mechanisms play a crucial role in regulating complex biochemical processes and communication in cells. Understanding and characterizing these mechanisms require innovative computational and experimental approaches, empowered by artificial intelligence technologies, to obtain detailed knowledge of allosteric states and interactions. Recent developments in deep mutational scanning, structural prediction methods, machine learning, and structural biology have provided new insights into allosteric protein functions and their implications in viral infection.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Multidisciplinary
Anat Iosub-Amir, Fang Bai, Yang-Sung Sohn, Luhua Song, Sagi Tamir, Henri-Baptiste Marjault, Guy Mayer, Ola Karmi, Patricia A. Jennings, Ron Mittler, Jose N. Onuchic, Assaf Friedler, Rachel Nechushtai