Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique by Tran-Hieu Nguyen, Anh-Tuan Vu

“…A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. …”
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Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches by Sanjog Chhetri Sapkota, Ajad Shrestha, Moinul Haq, Satish Paudel, Waiching Tang, Hesam Kamyab, Daniele Rocchio

“…This study proposes hybrid ML models that integrate three nature inspired metaheuristic algorithms—Giant Armadillo Optimization (GOA), Spotted Hyena Optimization (SHO) and Leopard seal optimization (LSA)- Extreme Gradient Boosting (XGB) to predict the shear strength of UHPC beams. …”
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions by Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa

“…GMSMFO enhances population diversity and avoids local optima through Gaussian mutation (GM), while the shrink mechanism (SM) improves exploration–exploitation balance. Validated on the congress on evolutionary computation (CEC2020) benchmark suite (dimensions 30 and 50), GMSMFO demonstrated superior performance compared to other optimization algorithms. …”
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Enhancing Fuzzy C-Means Clustering with a Novel Standard Deviation Weighted Distance Measure by Ahmed Husham Mohammed, Marwan Abdul Hameed Ashour

“…It was proven  through the experimental results that  the proposed distance measure Weighted Euclidean distance  had the advantage over improving the work of the HFCM algorithm through the criterion (Obj_Fun, Iteration, Min_optimization, good fit clustering and overlap) when (c = 2,3) and according to the simulation results, c = 2 was chosen to form groups for the real data, which contributed to determine the best objective function (23.93, 22.44, 18.83) at degrees of fuzzing (1.2, 2, 2.8), while according to the degree of fuzzing (m = 3.6), the objective function for Euclidean Distance (ED) was the lowest, but the criteria were (Iter. = 2, Min_optimization = 0 and )  which confirms that (WED) is the best.…”
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Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model by Guowei Li, Gang Tang, Jingyu Zhang, Qun Sun, Xiangjun Liu

“…The data show that the optimized root mean square error (RMSE) value under level 5 sea conditions is 0.01265 compared to 0.01673 before optimization, and the optimized RMSE value under level 6 sea conditions is 0.01140 compared to 0.01479 before optimization, which demonstrates that the error between the predicted value and the actual value of the model decreases. …”
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SC-PA: A spot-checking model based on Stackelberg game theory for improving peer assessment by Jia Xu, Panyuan Yang, Teng Xiao, Pin Lv, Minghe Yu, Ge Yu

“…In particular, to ensure the utilities of both of the teachers and students, an algorithm that computes the optimal allocation plan for review resources is carefully designed based on Stackelberg equilibrium. …”
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Three-Dimensional Aerodynamic Optimization of Single-Layer Reticulated Cylindrical Roofs Subjected to Mean Wind Loads by Bingbing San, Chen Xu, Ye Qiu

“…This paper presents a procedure to optimize the rise-to-span ratio of single-layer reticulated cylindrical roofs to improve their aerodynamic performance, by coupling the optimization method with computational fluid dynamics (CFD) and finite element analysis (FEA). …”
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Sperm swarm optimization for many objective power flow problems with enhanced performance evaluation in power systems by Wulfran Fendzi Mbasso, Ambe Harrison, Pradeep Jangir, Idriss Dagal, Hossam Kotb, Njimboh Henry Alombah, Raman Kumar, Aseel Smerat, Emmanuel Fendzi Donfack, Saad F. Al-Gahtani, Z. M. S. Elbarbary

“…Abstract This research presents an advanced optimization framework motivated from biological sources using the Sperm Swarm Optimization (SSO) algorithm to specifically deal with the Many-Objective Optimal Power Flow (MaO-OPF) problem in power systems. …”
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Exploring optimal combinations of multi-frequency polarimetric SAR observations to estimate forest above-ground biomass by Yongjie Ji, Fuxiang Zhang, Wangfei Zhang, Lei Zhao, Kunpeng Xu, Jianmin Shi, Guoran Huang, Qian Jing, Lu Wang, Feifei Yang

“…Taking advantage of available X-, C-, L-, and P-band quad-polarimetric SAR images of airborne or spaceborne for the test site located at Genhe national forest scientific field station, we used a Genetic Algorithm and Support Vector Regression optimization algorithm (GA-SVR) to explore the sensitivity of polarimetric observations at various frequencies to forest AGB and effectiveness of AGB retrievals using single-frequency, dual-frequency, triple-frequency, and quad-frequency SAR observation combinations. …”
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A correlation-based binary particle swarm optimization method for feature selection in human activity recognition by Huaijun Wang, Ruomeng Ke, Junhuai Li, Yang An, Kan Wang, Lei Yu

“…In existing works, for simplification purposes, feature selection algorithms are mostly based on the assumption of feature independence. …”
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Combining non-Monotone trust rregion method with a new adaptive radius for unconstrained optimization problems by Seyed Hamzeh Mirzaei, Ali Ashrafi

“…Purpose: One of the most effective methods for solving unconstrained optimization problems is the trust region method. …”
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Metaheuristic Optimization of Fractional Order Incremental Conductance (FO-INC) Maximum Power Point Tracking (MPPT) by Hossam Hassan Ammar, Ahmad Taher Azar, Raafat Shalaby, M. I. Mahmoud

“…This paper seeks to improve the photovoltaic (PV) system efficiency using metaheuristic, optimized fractional order incremental conductance (FO-INC) control. …”
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Novel Fast Super Twisting for Dynamic Performance Enhancement of Double-Fed Induction-Generator-Based Wind Turbine: Stability Proof and Steady State Analysis by Belgacem Kheira, Atig Mebarka, Abdelli Houaria, Mezouar Abdelkader

“…To overcome these shortcomings and optimize the efficiency and performance of this technique, a new method is suggested for the super-twisting algorithm (STA). …”
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Current state and prospects of development of energy-optimal control systems for 2ES6 electric locomotives by S. G. Istomin, K. I. Domanov, A. P. SHATOKHIN, I. N. Denisov

“…The researchers show that the most feasible way to build real-time dynamic models of energy-optimal locomotive motion for such smart system is to use data from the automated workstation of a freight locomotives motion recorder and auto-drive, as this is the data that contains accurate geographic coordinates to synchronise measurements on trips in a particular section.Discussion and conclusion. …”
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Development of compact mechanism for lithium-ion battery venting gas fires using Cantera ordinary differential equation neural network algorithm by Mengjie Li, Hao Hu, Li Lu, Huangwei Zhang

“…This integration allows for the optimization of a wide range of chemical reactions, improving both the precision and versatility of reaction mechanism development. …”
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Design of intelligent optimization of sports strategy and training decision support system based on deep reinforcement learning by Hua Xu, Bing Lin, Long Liu

“…The data is preprocessed by a sliding window average filter algorithm to eliminate noise and outliers. The system adopts the DQN (Deep Q-Network) architecture and applies dual DQN technology to improve model stability. …”
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Adaptive optimization decision system for plate-fin heat Exchangers: An integrated approach to enhancing efficiency and performance by Na Sun, Shuai Zhang, Nan Li, Zijian Li, Meng He, Zhengchun Shen, Ke Wang, Xiaoyong Guo, Wen-Quan Tao

“…The GSA module uses the Sobol method to evaluate the impact of design variables on performance. The optimization module employs the Newton-Raphson-based optimizer (NRBO) and the multi-strategy improved grey wolf optimization algorithm (MIGWO). …”
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Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks by Saad Said Alqahtany, Asadullah Shaikh, Ali Alqazzaz

“…To test the new FS technique and decide on an optimal set of features based on the accuracy achieved and the feature taking filters, the most recent FS approach relies on the NF-ToN-IoT dataset. …”
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Enhancing Efficiency and Reducing the Carbon Footprint of Cloud-Based Healthcare Applications through Optimal Data Preprocessing by El Aziz Btissam, Eddabbah Mohammed, Laaziz Yassin

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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…In addition, we developed a novel hybrid feature selection model that combined correlation filtering with mutual information, Cross-Validation (CV) along with Recursive Feature Eliminatio (RFE) (R, and stability selection to identify the most impactful features. Moreover, This study develops the proposed EffiXNet, a more refined version of EfficientNet augmented with self-attention mechanisms, dynamic convolutions, improved normalization methods, and Sparrow Search Optimization Algorithm for hyperparameter optimization. …”
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