Reinforcing long lead time drought forecasting with a novel hybrid deep learning model: a case study in Iran by Mahnoosh Moghaddasi, Mansour Moradi, Mahdi Mohammadi Ghaleni, Zaher Mundher Yaseen

“…Key parameters of the DFFNN, including the number of neurons and layers, learning rate, training function, and weight initialization, were optimized using the WSO algorithm. The model’s performance was validated against two established optimizers: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). …”
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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan Sulaiman, Zuriani Mustaffa

“…Results demonstrate that the CNN-LSTM-BMO achieves superior performance with the lowest Root Mean Square Error (RMSE) of 0.5523 and highest R² value of 0.9435, showing statistically significant improvements over other optimization methods as confirmed by paired t-tests (P < 0.05). …”
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The performance evaluation of chaotic maps in estimating the shape parameters of radial basis functions to solve partial differential equations by Javad Alikhani Koupaei, Mohammad Javad Ebadi, Majid Iran Pour

“…Purpose: This study aims to investigate the potential of chaotic optimization algorithms in improving performance compared to other optimization methods, focusing on determining the appropriate shape parameter of radial basis functions for solving partial differential equations.Methodology: In this research, a two-stage process is employed where the Kansa method, based on meshless local techniques, is combined with the FCW method. …”
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A study on the prediction of mountain slope displacement using a hybrid deep learning model by Yuyang Ma, Xiangxiang Hu, Yuhang Liu, Yaya Shi, Zhiyuan Yu, Xinmin Wang, Liangbai Hu, Shuailing Liu, Dongdong Pang

“…Abstract To address the challenges of large prediction errors and limited reliability in conventional modeling approaches, this study proposes a hybrid framework that integrates optimization and deep learning techniques. The method employs an Improved Whale Optimization Algorithm (IWOA) to fine-tune parameters for GNSS data fitting, ensuring accurate signal feature extraction. …”
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The Application of Kalman Filter Algorithm in Rail Transit Signal Safety Detection by Zhinong Miao, Qilong Liao

“…Firstly, the improved Kalman filter algorithm is used to denoise the signal to ensure the accuracy of signal transmission. …”
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Performance Analysis of Marine Predators Algorithm for Automatic Voltage Regulator System by Zeynep Garip, Murat Erhan Çimen, Ali Fuat Boz

“…With the proposed algorithm, this study aimed to minimize the maximum percent excess of the terminal voltage, settling time, rise time, and steady-state error and improve the transient response of the automatic voltage regulator system with an optimal proportional–integral– derivative controller. …”
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Research on Optimization of Magnetic Ranging by Mechanism & Intelligence by QIAN Hongyu, CHE Yang, WANG Xiufang, FENG Fuping, JIANG Chunlei

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Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour by Xinyi Dong, Ying Dong, Jinming Liu, Siting Wu

“…Compared to regression models built with competitive adaptive reweighted sampling and genetic algorithm for feature wavelength selection, the performance improved significantly, enhancing generalization capability. …”
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Identification of soil texture and color using machine learning algorithms and satellite imagery by Jiyang Wang

“…For future research, it is recommended to explore the combination of SVR with optimization techniques such as genetic algorithms to further improve the accuracy of soil texture and color predictions.…”
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Prediction of Arrival Time of Pure Electric Bus Based on FA-BP Algorithm by Yuanwen Lai, Hangyu Liang, Liling Huang

“…Based on the analysis of the influencing factors of the arrival time of the pure electric bus, the BP neural network arrival time prediction model optimized by the firefly algorithm (FA-BP prediction model) is established by selecting vehicle type, SOC value, battery age, and time as input conditions. …”
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Visual SLAM algorithm for underground robots in coal mines based on point-line features by Li WANG, Tianxiang ZANG, Bo SU

“…Therefore, a binocular vision localization algorithm SL-SLAM for underground mobile robots in coal mines based on the improved ORB (Oriented Fast and Rotated Brief)-SLAM3 algorithm is proposed. …”
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Research on short-term traffic flow prediction based on the PCC-IGA-LSTM model by Junxi Zhang, Shiru Qu, Yang Bi, Lijing Ma

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Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…In the realm of autonomous driving, the design and implementation of collaborative multi-task perception algorithms pose significant challenges. These challenges are primarily rooted in the need for real-time processing speeds, effective feature sharing among diverse tasks, and seamless information fusion. …”
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Dynamic strategy for adaptive block size optimization in blockchain technology by Shafique Ahmed Awan, Muazzam Ali Khan Khattak, Anwar Ali Sathio, Haleem Farman, Sumaira Memon, Moustafa M. Nasralla, Ahmed Sedik

“…This study proposes a dynamic block size optimization framework for private blockchain networks using hybrid heuristic algorithms—Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO)—integrated with Merkle trees and Directed Acyclic Graphs (DAGs). …”
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Efficient Optical Spatial First-Order Differentiator Based on Graphene-Based Metalines and Evolutionary Algorithms by Tian Zhang, Jia'nan Xie, Yihang Dan, Shuai Yu, Xu Han, Jian Dai, Kun Xu

“…The optimization results show that some performance metrics of the differentiator, for example normalized root-mean-square deviation, are better than the previous structures. …”
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Research on prediction algorithm of effluent quality and development of integrated control system for waste-water treatment by JianWun Lai

“…The ICS is superior to standard WWTCS by a vital error boundary, minimizing energy consumption by 17% and boosting chemical-based consumption optimization by 24%. With an average removal rate of 94.23% for Chemical Oxygen Demand (COD) compared to 88.76% for standard systems, the findings from experiments exhibited significant performance improvements.…”
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Application of deep reinforcement learning in parameter optimization and refinement of turbulence models by Zhan Zhang

“…The DDPG optimization method significantly reduced the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) of the WPC, and its optimization effect was significantly better than the GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) methods.…”
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A Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion by Chao Yan, Tao Li, Yandong Gao, Shijin Li, Xiang Zhang, Xuefei Zhang, Di Zhang, Huiqin Liu

“…To solve this problem, this paper combines a deep neural network model with the traditional PhU model and proposes a novel two-stage learning-based phase unwrapping (TLPU) algorithm via multimodel fusion. The major advantages of TLPU are as follows: 1) A high-resolution U-Net (HRU-Net) model trained on a dataset constructed according to InSAR interferometric geometry is utilized for the PhU for the first time, which effectively improves the performance of the DLPU. 2) TLPU utilizes the traditional PhU method to optimize the results of DLPU, addressing the issue of weak generalization ability of a single DLPU, while improving accuracy in areas with large-gradient changes. …”
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Detection of capsaicin content by near-infrared spectroscopy combined with optimal wavelengths by LÜ Xiaohan, JIANG Jinlin, YANG Jing, CHEN Jianying, CEN Haiyan, FU Hongfei, ZHOU Yifei

“…In addition, compared with the full spectra of 200 wavelengths, the number of the optimal wavelengths selected by CARS was reduced by 96%, which indicated that optimal wavelengths can be used to simplify the models and improve the operation efficiency. …”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…Then, a convergence factor is introduced to balance the global and local search abilities of the whale algorithm to improve the convergence speed. The sample space is then iteratively searched using the improved whale algorithm. …”
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