Article
Computer Science, Artificial Intelligence
Huixian Qiu, Xuewen Xia, Yuanxiang Li, Xianli Deng
Summary: In this paper, a cloud workflow scheduling problem is modeled as a multi-objective optimization problem and a dynamic multiple population genetic algorithm (DMGA) is proposed to address the problem. The experimental results show that DMGA outperforms other algorithms on multiple tested datasets, especially on large-scale problems. Moreover, the effectiveness of the new proposed strategies is also verified by a set of experiments.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Xianglei Zhang, Cuiyu Yang
Summary: Industrial special clothing refers to the clothing worn by employees of industrial enterprises and departments in order to dress uniformly, facilitate work, or protect their bodies from external injuries. They have the common characteristics of practicality and identification, and are applicable in different fields.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Article
Green & Sustainable Science & Technology
Chandra Shekhar Yadav, Raghuraj Singh, Sambit Satpathy, S. Baghavathi Priya, B. T. Geetha, Vishal Goyal
Summary: In software development, accurately assessing effort, cost, energy, and time is crucial for effective resource planning. A proposed effort estimator called Double Hidden Layers Bi-directional Associative Memory (DHBAM) outperforms the Single Hidden Layer Feed-Forward Neural Net (SHFNN) model in predicting project completion time. The optimized based genetic algorithm and root mean square error (RMSE) method confirm the superiority of the DHBAM architecture.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2023)
Article
Operations Research & Management Science
Narin Petrot, Nimit Nimana
Summary: The study introduces an algorithm combining the incremental proximal gradient method with smooth penalization technique for minimizing a finite sum of convex functions subject to the set of minimizers of a convex differentiable function. The convergence of the algorithm is proven, and its effectiveness is demonstrated through numerical experiments.
Article
Mathematics
Wei Kuang Lai, Chin-Shiuh Shieh, Chao-Ping Yang
Summary: This article discusses the routing issue within D2D communication groups and proposes a utility function considering delay and throughput, as well as a BICA* algorithm to tackle the optimization problem. Simulations show that the proposed approach outperforms existing methods in terms of delay, throughput, and satisfaction.
Article
Computer Science, Artificial Intelligence
Mohaideen Pitchai
Summary: In Wireless Sensor Networks, dynamic load-balanced clustering process relies on dynamic genetic algorithm optimization and selection mechanisms to choose the best cluster heads and member assignment strategies. By considering different metrics of nodes, more effective load balancing and performance optimization can be achieved.
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Peizhuang Cong, Yuchao Zhang, Bin Liu, Wendong Wang, Zehui Xiong, Ke Xu
Summary: This paper proposes a lightweight router design that achieves small storage requirement while retaining the original communication connection performance through AI prediction strategy and block-based insertion strategy.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2022)
Article
Energy & Fuels
Lin Xiqiao, Cheng Min
Summary: This paper proposes a new energy planning scheme for Guangxi Province in China to promote the construction of ecological civilization. The scheme provides energy planning demonstrations and reference scenarios for the Fourteenth Five-Year Plan, as well as utilizing genetic algorithm to optimize the path and select the optimal energy planning scheme.
Article
Computer Science, Artificial Intelligence
Lis Arufe, Riccardo Rasconi, Angelo Oddi, Ramiro Varela, Miguel A. Gonzalez
Summary: This paper presents a genetic algorithm for solving the Quantum Circuit Compilation Problem (QCCP) in Quantum Approximate Optimization Algorithms (QAOA). The algorithm utilizes a new coding scheme to reduce the number of SWAP gates and circuit depth. Experimental results demonstrate the superior performance of the proposed method compared to previous algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yanrong Jiang, Long Tang, Hailin Liu, An Zeng
Summary: This paper proposes an improved genetic algorithm for service composition and selection in the dynamic environment of cloud manufacturing. By using a variable-length encoding scheme and improved crossover and mutation algorithm, it addresses the challenges of changes and uncertainties in the traditional methods. Experimental results demonstrate the effectiveness of the proposed approach.
APPLIED SOFT COMPUTING
(2022)
Article
Chemistry, Physical
Tomoyasu Yokoyama, Satoru Ohuchi, Emiko Igaki, Taisuke Matsui, Yukihiro Kaneko, Takao Sasagawa
Summary: This study successfully predicted the crystal structures and phase diagrams of MA-Pb-I systems using first-principles calculations and genetic algorithms. The stable phases were found to be MAPbI(3) and MA(2)PbI(4), consistent with experimental results. The electronic and optical properties of the predicted structures were calculated and solar cell performance was evaluated.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2021)
Article
Energy & Fuels
Chun Chang, Qiyue Wang, Jiuchun Jiang, Tiezhou Wu
Summary: This paper proposes an online method based on incremental capacity and wavelet neural networks, which can estimate the health status of the battery under current discharge. By extracting important variables from IC curves and optimizing the WNN model parameters using genetic algorithm, the SOH of the battery is successfully estimated with an error of less than 3%.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Business
Xiaoxi Liu, Xiaoling Yuan, Nan Ye, Rui Zhang
Summary: Enhancing energy efficiency, employing renewable energy sources, improving greenhouse gas mitigation, creating innovative technologies to absorb greenhouse gases, and eliminating incentives for environmentally damaging operations are tasks that support low-carbon advancement. A low-carbon economy (LCE) minimizes greenhouse gas emissions by replacing nonrenewable energy sources with renewable and natural energy resources. This article introduces a genetic algorithm-enabled replacement recommendation model (GA-RRM) for managing LCEs in open environments. The GA-RRM model identifies the links among energy exhaustion, requirements, and generation for emission management and uses current and previous climatic change factors to identify LCE management trends. The performance of GA-RRM is evaluated based on emission control and energy usage scenarios, and high precision is observed.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Computer Science, Information Systems
Gyan Singh Yadav, Parth Mangal, Gaurav Parmar, Shubham Soliya
Summary: Steganography is widely used in security systems for hiding secret information into data. However, most existing schemes do not provide enough capacity for embedding and degrade the image quality. Histograms can reveal the presence of secret information and are important for data security. This paper proposes a technique that uses a genetic algorithm to select the best chromosome for embedding, reducing bit-flip cost count and maximizing PSNR value. The proposed technique is robust against steganographic attacks and significantly increases the PSNR by approximately 7%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Wei Guo, Lanju Kong, Xudong Lu, Lizhen Cui
Summary: The optimization of collaborative service scheduling is crucial for improving efficiency and reducing costs. This paper proposes an Intelligent Genetic algorithm (IGS) which enhances the scalability and diversity of the algorithm. By changing the initial population generation strategy and implementing an adaptive selection based on mutation factors, the IGS is able to maintain individual diversity, accelerate convergence speed, and avoid local optimal solutions. The experimental results demonstrate that IGS has better scalability and diversity, while increasing the probability of excellent individuals and accelerating convergence speed.
Article
Computer Science, Artificial Intelligence
Xu Yang, Juan Zou, Shengxiang Yang, Jinhua Zheng, Yuan Liu
Summary: This article proposes a fuzzy decision variables framework for large-scale multiobjective optimization. The framework divides the evolutionary process into fuzzy evolution and precise evolution stages. Fuzzy evolution blurs the decision variables to reduce the search range in the decision space for quick convergence, while precise evolution directly optimizes the actual decision variables to increase population diversity. Experimental results demonstrate that this framework significantly improves the performance and computational efficiency of multiobjective optimization algorithms in large-scale problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Theory & Methods
Shouyong Jiang, Juan Zou, Shengxiang Yang, Xin Yao
Summary: Evolutionary dynamic multi-objective optimisation (EDMO) is a rapidly growing area that uses evolutionary approaches to solve multi-objective optimisation problems with time-varying changes. After nearly two decades, significant advancements have been made in theoretic research and applications. This article provides a comprehensive survey and taxonomy of existing research on EDMO, as well as highlighting multiple research opportunities for further development.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Meirong Chen, Yinan Guo, Yaochu Jin, Shengxiang Yang, Dunwei Gong, Zekuan Yu
Summary: This study proposes an environment-driven hybrid dynamic multi-objective evolutionary optimization method to balance the quality of obtained solutions and the computation cost, and select an appropriate optimization method based on the characteristics of the dynamic environment.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Engineering, Environmental
Gaobo Lou, Qingqing Rao, Qing Li, Zhicheng Bai, Xingwei He, Youhua Xiao, Jinfeng Dai, Shenyuan Fu, Shengxiang Yang
Summary: In this study, a novel ionic complex MPA-DAD was successfully synthesized for use as a flame retardant and toughening additive in epoxy resin (EP). Results showed that adding 8 wt% MPA-DAD enabled EP to pass the UL 94 V-0 rating and greatly reduced the heat release, fulfilling the fire safety requirement. Additionally, the EP/8% MPA-DAD composite exhibited significantly enhanced toughness and preserved strength. This work provides a facile fabrication route for high efficiency, multifunctional flame retardants in advanced EP composite materials manufacturing.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Computer Science, Information Systems
Jialiang Zhang, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: This paper proposes a multi-modal multi-objective evolutionary algorithm (MMEAs) based on independently evolving sub-problems to solve the problem of poor diversity maintenance in traditional algorithms. A two-stage environmental selection strategy is used to ensure the convergence of the objective space and the distribution of the decision space. The k-nearest neighbor deletion strategy is employed in the decision space to guarantee the distributivity of each equivalent Pareto optimal solution.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoling Gong, Ziheng Rong, Jian Wang, Kai Zhang, Shengxiang Yang
Summary: In this paper, a hybrid algorithm based on state-adaptive slime mold model and fractional-order ant system (SSMFAS) is proposed to solve the travelling salesman problem (TSP). The SSMFAS algorithm emphasizes critical connections and balances exploration and exploitation ability through two targeted auxiliary strategies in the state-adaptive slime mold (SM) model. The incorporation of fractional-order calculus in the ant system (AS) takes advantage of neighboring information. The modified pheromone update rule of AS dynamically integrates the flux information of SM. Convergence analysis is provided through mathematical proofs to understand the search behavior of the proposed algorithm. Experimental results show the efficiency of the hybridization and demonstrate the competitive ability of the proposed algorithm in finding better solutions for TSP instances compared to state-of-the-art algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Likai Wang, Qingyang Zhang, Xiangyu He, Shengxiang Yang, Shouyong Jiang, Yongquan Dong
Summary: This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. The algorithm consists of two important courses, escape phase and adjustment phase. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems, and neural network training models. Results show that BSO has competitive performance compared with other state-of-the-art optimization techniques in terms of both convergence and accuracy.
Article
Plant Sciences
Peng Liu, Yue Tan, Jian Yang, Yan-Duo Wang, Qi Li, Bing-Da Sun, Xiao-Ke Xing, Di-An Sun, Sheng-Xiang Yang, Gang Ding
Summary: Endophytic fungi from desert plants are a unique microbial community that has not been extensively studied chemically. They may serve as a new source for bioactive natural products. In this study, new secondary metabolites were obtained from an endophytic fungus isolated from two desert plant species. The compounds showed potential cytotoxic and phytotoxic activities.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Automation & Control Systems
Yaru Hu, Jinhua Zheng, Shouyong Jiang, Shengxiang Yang, Juan Zou
Summary: This article proposes an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The algorithm predicts population changes in response to environmental changes and maintains a balance between population diversity and convergence.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Retraction
Mycology
Yi Kuang, Kirstin Scherlach, Christian Hertweck, Shengxiang Yang, Diego A. Sampietro, Petr Karlovsky
MYCOTOXIN RESEARCH
(2023)
Article
Chemistry, Applied
Yanxin Zhang, Jinlong Dai, Xiaoyun Ma, Chengguo Jia, Junyou Han, Chenggang Song, Yuqing Liu, Dongsheng Wei, Hongfei Xu, Jianchun Qin, Shengxiang Yang
Summary: The reduction in blueberry harvest due to pathogen infection was reported to reach 80%. Essential oil (EO) and its nano-emulsion (MNE) derived from Monarda didyma L were found to inhibit the growth of pathogenic fungi isolated from blueberries, with MNE exhibiting superior antimicrobial activity and causing morphological changes in the fungi, as well as reducing the rot and weight loss rate of blueberries.
Article
Computer Science, Information Systems
Juan Zou, Jian Luo, Yuan Liu, Shengxiang Yang, Jinhua Zheng
Summary: The core element in solving CMOPs is to balance objective optimization and constraint satisfaction. We propose a flexible two-stage evolutionary algorithm based on automatic regulation (ARCMO) to adapt to complex CMOPs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Weixiong Huang, Juan Zou, Yuan Liu, Shengxiang Yang, Jinhua Zheng
Summary: This paper proposes a constrained multi-objective evolutionary algorithm framework based on global and local feasible solutions search to address the complexity of feasible regions caused by constraints. The framework is divided into three stages and an adaptive method is used to decide when to switch the search state. The experimental results show that the proposed framework is highly competitive for solving CMOPs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jie Chen, Shengxiang Yang, Zhu Wang, Hua Mao
Summary: Sparse representation techniques have shown impressive impact on various fields such as image processing, computer vision, and pattern recognition, due to their capability in effectively learning intrinsic structures from high-dimensional data. In this article, two algorithms based on locality-constrained linear representation learning with probabilistic simplex constraints are proposed to learn sparse representations. Experimental results demonstrate that these algorithms perform better than several state-of-the-art algorithms for learning with high-dimensional data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Jie Chen, Zhu Wang, Shengxiang Yang, Hua Mao
Summary: This article proposes a two-stage sparse representation clustering (TSSRC) method based on sparse representation techniques to address the critical issues in data stream clustering. The TSSRC algorithm evaluates the effective relationship among data objects in landmark windows with an accurate number of clusters and efficiently passes previously learned knowledge to the current landmark window. Experimental results demonstrate the effectiveness and robustness of TSSRC.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
