Article
Entomology
Adam M. Lambert, Lisa A. Tewksbury, Richard A. Casagrande
Summary: This study evaluated the survival and development of a native butterfly and an introduced moth fed native or introduced lineages of common reed, finding that an artificial diet supplemented with common reed rhizome powder can increase survival rates for the moth. Only larvae of the native butterfly reared on leaves from native plants completed development, while the introduced moth successfully completed developmental stages on both native and introduced rhizomes.
Article
Computer Science, Theory & Methods
Xialin Zhang, Lingkun Lian, Fukang Zhu
Summary: The study improves the accuracy and automation of variogram fitting models through a hybrid algorithm, demonstrating stronger optimization ability and higher precision compared to traditional methods.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Engineering, Multidisciplinary
Alireza Khaloo, Amirhossein Amirahmadi
Summary: In this study, the support conditions and behavior of a steel cantilever beam were investigated using the finite element model updating (FEMU) method. Displacement and strain information from the beam surface were continuously measured using the digital image correlation (DIC) method. A hybrid method called Powell particle swarm optimization was used to minimize the cost function, which resulted in the calculation of rotation and displacement of support and investigation of the dependency of beam behavior on each variable.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Environmental Sciences
Feng Gao, Xueyan Shao
Summary: A novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed for accurate prediction of natural gas consumption (NGC), outperforming benchmark models on annual NGC data in China.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Erol Egrioglu, Eren Bas
Summary: A new hybrid recurrent artificial neural network is proposed for nonlinear time series forecasting in this study. The network combines simple exponential smoothing and a single multiplicative neuron model to solve the insufficiency of classical forecasting methods in forecasting nonlinear and complex time series structures. The proposed method outperforms other artificial neural networks in terms of performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Physical
Ayse Tugba Dosdogru, Asli Boru Ipek
Summary: Energy sources are crucial for national economic growth, with wind energy playing a significant role in low-carbon energy technologies. This study focuses on improving wind speed prediction by utilizing a hybrid approach of XGBoost, AdaBoost, and ANN, aiming to provide more accurate results for wind speed forecasting.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Operations Research & Management Science
Quan Gan
Summary: Logistics distribution is a crucial link that connects consumers and logistics activities, and distribution costs account for a significant proportion of overall logistics costs. Optimizing vehicle routes in distribution plays a key role in improving service level, cost, and benefit. This paper proposes a hybrid optimization algorithm based on fish swarm algorithm and ant colony algorithm to solve the distribution route optimization problem. The proposed algorithm outperforms other heuristic algorithms, as verified using Solomon's problem data. The various parameters of the algorithm are compared and analyzed to improve its effectiveness.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Thermodynamics
Cristian Chinas-Palacios, Carlos Vargas-Salgado, Jesus Aguila-Leon, Elias Hurtado-Perez
Summary: This study proposes a model based on Artificial Neural Networks and Particle Swarm Optimization algorithm to estimate the biomass needed for a Biomass Gasification Plant to produce syngas to meet energy demand. The results show that the proposed model outperforms existing models in terms of MSE.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Mathematics
Sumit Kumar, Shiva Shankar Choudhary, Avijit Burman, Raushan Kumar Singh, Abidhan Bardhan, Panagiotis G. Asteris, Zongwei Luo
Summary: This study presents an effective computational technique for probabilistic analyses of Mount St. Helens. By using a hybrid model of artificial neural network and firefly algorithm, the probability of failure of rock slope stability was estimated.
Article
Thermodynamics
Jesus Aguila-Leon, Carlos Vargas-Salgado, Cristian Chinas-Palacios, Dacil Diaz-Bello
Summary: This study proposes a methodology to incorporate optimized artificial networks into a self-adaptable energy management system in order to improve microgrids performance. Through simulated tests and correlation analysis, the results show that the proposed model achieves better accuracy in energy parameter estimation compared to related works.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Engineering, Multidisciplinary
Wenyu Zheng, Shahab S. Band, Hojat Karami, Sohrab Karimi, Saeed Samadianfard, Sadra Shadkani, Kwok-Wing Chau, Amir H. Mosavi
Summary: The research investigates the influential factors on estimating the discharge capacity of inflatable dams and proposes a hybrid model based on PSO and GA to improve accuracy. The results show that internal pressure plays a vital role in forecasting the discharge coefficient. The proposed PSO-GA hybrid model provides the most accurate results compared to other models.
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Abdelrigeeb Al-Gathe, Salem O. Baarimah, Abbas M. Al-Khudafi, Mohammed Bawahab, Hazim Dmour
Summary: Estimating gas viscosity experimentally is difficult, but using artificial neural networks (ANN) can help predict it accurately. This study combines the Particle Swarm Optimization (PSO) algorithm and the back-propagation learning rule to create a hybrid model for predicting gas viscosity. The results show that this model is reliable and accurate.
EARTH SCIENCE INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Cetengfei Zhang, Quan Zhou, Bin Shuai, Huw Williams, Yanfei Li, Lun Hua, Hongming Xu
Summary: The digital twin is a promising technology that combines artificial intelligence and big data to provide a cyber-physical platform for rapid product development. In this study, a dedicated adaptive particle swarm optimization (DAPSO) algorithm is developed to optimize the energy management strategy (EMS) for a plug-in hybrid vehicle (PHEV) based on the digital twin. The DAPSO algorithm, which incorporates the widely used particle swarm optimization (PSO) algorithm and an adaptive swarm control strategy, improves the optimality and trustworthiness of the DT-based EMS optimization. Experimental evaluations show that the DAPSO algorithm outperforms conventional PSO algorithms, achieving significant improvements in cost function value and computing time for the PHEV application.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Computer Science, Artificial Intelligence
Naveen Kumar Kedia, Anil Kumar, Yogendra Singh
Summary: An optimized artificial neural network-based hybrid prediction model is proposed to predict underground metro train-induced ground vibration. The model considers multiple soil properties and achieves higher accuracy compared to other traditional methods.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yixin Zhou
Summary: By integrating artificial neural network (ANN) and particle swarm optimization (PSO), this study evaluates the effectiveness of applying an automated logistic system in hospital management using a computer simulation method. The results demonstrate the system's success in enhancing serviceability and lowering costs based on extensive data and regression indicators.
ENGINEERING WITH COMPUTERS
(2022)