The Path Tracking Control of Unmanned Surface Vehicles Based on an Improved Non-Dominated Sorting Genetic Algorithm II-Based Multi-Objective Nonlinear Model Predictive Control Meth... by Yunzhe Guo, Qidan Zhu, Jinyou Mou

“…Since the cost function of the MPC algorithm includes multiple objective terms, a multi-objective model predictive control algorithm is employed to improve overall control performance. …”
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Prediction of Vehicle Interior Wind Noise Based on Shape Features Using the WOA-Xception Model by Yan Ma, Hongwei Yi, Long Ma, Yuwei Deng, Jifeng Wang, Yudong Wu, Yuming Peng

“…The key hyperparameters of the Xception model are adaptively optimized using the whale optimization algorithm to improve the prediction accuracy and generalization ability of the model. …”
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Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR by FENG Yu, WU Yunxing, GU Wenjing, PANG Qiong, GU Yanchang, CHEN Siyu

“…The random forest algorithm and whale optimization algorithm were introduced in the construction of the prediction model of concrete dam deformation based on WOA-RFR to improve the prediction accuracy and model performance. …”
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Deploying UAV-based detection of bridge structural deterioration with pilgrimage walk optimization-lite for computer vision by Jui-Sheng Chou, Chi-Yun Liu, Pin-Jun Guo

“…The YOLOv7 model, fine-tuned with the Pilgrimage Walk Optimization (PWO)-Lite algorithm, achieved the highest accuracy, recording a 65.6 % mAP50 on the testing set. …”
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A Hybrid-A* Path Planning Method Based on Equal Step Hierarchical Expansion by CHEN Xinpeng, XU Biao, HU Manjiang, QIN Zhaobo, BIAN Yougang

“…This method can be applied to unstructured road scenes to plan a safe, smooth and driveable path for intelligent vehicles that meets the vehicle kinematic constraints of vehicle operation. First by improving the way of child node development and the reasonable design of the heuristic function and cost function, quick search of guarantee algorithm is a relatively smooth driving path. …”
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Surfactants Adsorption onto Algerian Rock Reservoir for Enhanced Oil Recovery Applications: Prediction and Optimization Using Design of Experiments, Artificial Neural Networks, and... by Kahina Imene Benramdane, Mohamed El Moundhir Hadji, Mohamed Khodja, Nadjib Drouiche, Bruno Grassl, Seif El Islam Lebouachera

“…Unlike traditional data acquisition methods, this approach enables a methodical and structured exploration of adsorption behavior while reducing the number of required experiments, leading to improved prediction accuracy, optimization, and cost-effectiveness. …”
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Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

“…The methodology involves a formation-specific modelling approach, where separate ELM models are trained for each formation using surface drilling parameters such as weight on bit (WOB), rotary speed (RPM), and flow rate. Metaheuristic algorithms optimize the ELM’s weights and biases to improve predictive performance. …”
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Forecasting the daily evaporation by coupling the ensemble deep learning models with meta-heuristic algorithms and data pre-processing in dryland by Tonglin Fu, Dong Wang, Jing Jin

“…To achieve this purpose, the Convolutional neural network (CNN) was integrated with Bidirectional long short-term memory network (BiLSTM) as main estimating module, and the Sparrow search algorithm (SSA) was employed to search the optimal hyperparameters of CNN-BiLSTM. …”
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Disturbance Observer-Based Bio-Inspired LQR Optimization for DC Motor Speed Control by Mubarak Badamasi Aremu, Mohammed Abdel-Nasser, Nezar M. Alyazidi, and Sami El-Ferik

“…The controller maximizes the capabilities of the integral linear quadratic regulator (ILQR) framework, fine-tuned using state-of-the-art particle swarm optimization (PSO) techniques and a well-defined cost function alongside other bio-inspired algorithms. …”
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A Two-echelon Model of Location-routing Problem for Optimizing Relief Operations in Natural Disasters by Hossein Jamali, Mehdi Kabiri Naeini, Zeynab Elahi

“…The goal is to determine the optimal locations for relief bases and routing relief teams to minimize relief time and cost at both levels.  …”
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Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks by K. Lakshmi Prabha, Hanan Abdullah Mengash, Hamed Alqahtani, Randa Allafi

“…The GJO algorithm fine-tunes the hyperparameters of MARL to improve generalization across different WSN configurations. …”
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UAV Path Planners in Complex Environments: A Multi-Dimensional Perturbation Based on Artificial Bee Colony by Xuzhao Chai, Guanhao Zhou, Haoyu Wang, Ping Liu, Zhongyun Liu, Chao Li, Li Yan

“…In the onlooker bee phase, the curvature-guided elite neighbor search strategy is used to prioritizes high-curvature waypoints, enhancing optimization efficiency in complex terrain. Furthermore, path costs are independently modeled in the horizontal and vertical directions. …”
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Parallel strategies for a multi-criteria GRASP algorithm Estratégias de paralelização para um algoritmo GRASP multicritério by Dalessandro Soares Vianna, José Elias Claudio Arroyo, Pedro Sampaio Vieira, Thiago Ribeiro de Azeredo

“…In this problem, a vector of costs is defined for each edge of the graph and the goal is to find all the efficient or Pareto optimal spanning trees (Pareto-optimal solutions). …”
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Heuristically enhanced multi-head attention based recurrent neural network for denial of wallet attacks detection on serverless computing environment by Sarah A. Alzakari, Mohammad Alamgeer, Abdullah M. Alashjaee, Monir Abdullah, Khalid Nazim Abdul Sattar, Asma Alshuhail, Ahmad A. Alzahrani, Abdulwhab Alkharashi

“…The detection and classification of DoW attacks, the multi-head attention-based bi-directional gated recurrent unit (MHA-BiGRU) model is utilized. Eventually, the improved secretary bird optimizer algorithm (ISBOA)-based hyperparameter choice process is accomplished to optimize the detection results of the MHA-BiGRU model. …”
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A Feasibility Study of a Virtual Power Line Device to Improve Hosting Capacity in Renewable Energy Sources by Seong-Eun Rho, Sung-Moon Choi, Joong-Seon Lee, Hyun-Sang You, Seung-Ho Lee, Dae-Seok Rho

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Energy, exergy and economic optimization application of integrated dual-loop organic rankine cycle: A case of shopping mall power plant by Muhammed Taha TOPCU, Alperen TOZLU, Kadir BİLEN

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Enhancing Smart Microgrid Resilience and Virtual Power Plant Profitability Through Hybrid IGWO-PSO Optimization With a Three-Phase Bidding Strategy by T. Yuvaraj, T. Sengolrajan, Natarajan Prabaharan, K. R. Devabalaji, Akie Uehara, Tomonobu Senjyu

“…A hybrid improved grey wolf optimization-particle swarm optimization (IGWO-PSO) algorithm is developed to solve this complex optimization problem. …”
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Energy management for microgrids integrating renewable sources and hybrid electric vehicles by Wanying Liu, Chunqing Rui, Zilin Liu, Jinxin Chen

“…The primary objective is to minimize operational costs and environmental emissions, framed as an optimization problem. …”
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A New Approach to Environmental Economic Dispatch Using Multiobjective Differential Evolution: A Case Study by Carine Nogueira Santino, Jorge Laureano Moya Rodriguez, Cristiano Hora De Oliveira Fontes

“…In electricity generation systems, problems related to economic and environmental dispatch represent potential for improvement in generating plants. The Environmental Economic Dispatch (EED) consists of minimizing the cost of generation and emission of pollutants such as CO2, SO2 and NOx. …”
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Artificial intelligence-optimized shield parameters for soft ground tunneling in urban environment: A case study of Bangkok MRT Blue Line by Sahatsawat Wainiphithapong, Chana Phutthananon, Sompote Youwai, Pitthaya Jamsawang, Phattarawan Malaisree, Ochok Duangsano, Pornkasem Jongpradist

“…This integrated framework, which combines the non-dominated sorting genetic algorithm (NSGA-II) with LSTM neural networks, is applied to MOO to identify the optimal SOPs, while accounting for their influence on S variation as a time-series over 11 timesteps, as considered in this study. …”
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