Smart deep learning model for enhanced IoT intrusion detection by Faisal S. Alsubaei

“…The optimized SNN model also performed well on the NSL-KDD dataset with an accuracy of 99.0% and an AUC of 1.00. …”
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PCA-Kriging-Based Oscillating Jet Actuator Optimization and Wing Separation Flow Control by Qixiang Sun, Wanbo Wang, Jiaxin Pan

“…The optimization objectives are to improve the sweeping uniformity and range of the jet. …”
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A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model by Zhen Yang, Junli Li, Liwei Yang, Hejiang Chen

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Optimal Configuration Method for Electric-thermo-hydrogen System Considering Safety Risks by Chouwei NI, Yang CHEN, Xuesong ZHANG, Da LIN, Kaijian DU, Jian CHEN

“…Furthermore, the safety risk coefficient is used to convert the safety risk of the hydrogen storage tank into the objective function. The optimal configuration model of the ETHS is then established with the system investment cost, operation cost, and safety risk as optimization objectives, and the tabu chaotic quantum particle swarm optimization (TCQPSO) algorithm is employed to solve the model. …”
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Optimizing Route Planning via the Weighted Sum Method and Multi-Criteria Decision-Making by Guanquan Zhu, Minyi Ye, Xinqi Yu, Junhao Liu, Mingju Wang, Zihang Luo, Haomin Liang, Yubin Zhong

“…Secondly, this study compares seven heuristic algorithms—the genetic algorithm (GA), particle swarm optimization (PSO), the tabu search (TS), genetic-particle swarm optimization (GA-PSO), the gray wolf optimizer (GWO), and ant colony optimization (ACO)—to solve the TOPSIS model, with GA-PSO performing the best. …”
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An Innovative Indoor Localization Method for Agricultural Robots Based on the NLOS Base Station Identification and IBKA-BP Integration by Jingjing Yang, Lihong Wan, Junbing Qian, Zonglun Li, Zhijie Mao, Xueming Zhang, Junjie Lei

“…Next, the collected received signal strength indication (RSSI) data are processed using Kalman filtering and Min-Max normalization, suppressing signal fluctuations and accelerating the gradient descent convergence of the distance measurement model. Finally, the improved black kite algorithm (IBKA) is enhanced with tent chaotic mapping, a lens imaging reverse learning strategy, and the golden sine strategy to optimize the weights and biases of the BP neural network, developing an RSSI-based ranging algorithm using the IBKA-BP neural network. …”
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Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm by Lilin Li, Qing He, Hua Wang, Wensheng Wang

“…The DIWPSO algorithm dynamically adjusts the inertia weight to balance global exploration and local exploitation, optimizing SVM parameters for improved performance. …”
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A configuration and scheduling optimization method for integrated energy systems considering massive flexible load resources by Zihao Wang, Shenhua Wang, Hanwen Ni, Junyin Wang, Jun Zhang

“…Additionally, an enhanced Kepler Optimization Algorithm (EKOA) was proposed, incorporating chaos mapping and adaptive learning rate strategies to improve search scope, convergence speed, and solution efficiency. …”
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Multi-Objective Optimization of Daylighting–Thermal Performance in Cold-Region University Library Atriums: A Parametric Design Approach by Yunong Gao, Shuting Zhao, Yong Huang, Hui Pan

“…In this paper, we take the library project in the cold region as a practical case, use the measured data to support the simulation experiment, combine the parametric platform and multi-objective coupling optimization algorithm to carry out digital modeling, and explore the dynamic relationship between the atrium light, heat environment, and the value of energy consumption under the influence of a variety of parameters. …”
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Optimization of Vessel Traffic Scheduling in a Compound Channel of an Estuarine Port with Opposing Distribution of Inner Anchorages and Terminals by Bolin Yin, Chengji Liang, Yu Wang, Xiaojie Xu, Yue Zhang

“…The entropy-weighted technique for order preference by similarity to ideal solution (TOPSIS) is used to calculate the objective weights of the Pareto frontier and identify the optimal solution. Experimental results show that the proposed model and algorithm yield optimal port entry and exit scheduling solutions. …”
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Advanced Computational Methods for Mitigating Shock and Vibration Hazards in Deep Mines Gas Outburst Prediction Using SVM Optimized by Grey Relational Analysis and APSO Algorithm by Xiang Wu, Zhen Yang, Dongdong Wu

“…Moreover, adaptive particle swarm optimization (APSO) was used to optimize the penalty factor and kernel parameters of the support vector machine to improve the global search ability and avoid the occurrence of the local optimal solutions. …”
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SMA-YOLO: An Improved YOLOv8 Algorithm Based on Parameter-Free Attention Mechanism and Multi-Scale Feature Fusion for Small Object Detection in UAV Images by Shenming Qu, Chaoxu Dang, Wangyou Chen, Yanhong Liu

“…Using YOLOv8n as the baseline, SMA-YOLO is evaluated on the VisDrone2019 dataset, achieving a 7.4% improvement in mAP@0.5 and a 13.3% reduction in model parameters, and we also verified its generalization ability on VAUDT and RSOD datasets, which demonstrates the effectiveness of our approach.…”
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Cooperative Sleep and Energy-Sharing Strategy for a Heterogeneous 5G Base Station Microgrid System Integrated with Deep Learning and an Improved MOEA/D Algorithm by Ming Yan, Tuanfa Qin, Wenhao Guo, Yongle Hu

“…This paper proposes a cooperative sleep and energy-sharing strategy for heterogeneous 5G base station microgrid (BSMG) systems, utilizing deep learning and an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D). …”
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Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy by J. Logeshwaran, Shobhit K. Patel, Om Prakash Kumar, Fahah Ahmed Al-Zahrani

“…Data was successfully routed using a hybrid Quantum Genetic Algorithm and Ant Colony Optimization (QGA-ACO) approach. …”
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Robust Optimization of Active Distribution Networks Considering Source-Side Uncertainty and Load-Side Demand Response by Renbo Wu, Shuqin Liu

“…The iCCG algorithm improves the computational efficiency by 35.2% compared with the traditional CCG algorithm, which verifies the effectiveness of the model in coping with the uncertainties and improving the economy and robustness.…”
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Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization by Vasavi Chithanuru, Mangayarkarasi Ramaiah

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Coordinated multi-objective optimization scheduling for electric vehicle swapping station cluster and grid by Xiang Liao, Ziyu Zheng, Beibei Qian, Haiwei Wang, Dianling Zhan, Junyi Shi, Chaoshun Li, Wei Huang

“…Meanwhile, an electricity mutual assistance strategy between EVBESS is proposed to improve overall energy efficiency. Subsequently, an improved many-objective optimization algorithm based on multiple update strategies (G-MOMUS) is used to solve the model. …”
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DGCA3QM: DESIGN OF A DUAL GENETIC ALGORITHM BASED AUTOREGRESSION MODEL FOR CORRELATIVE PREDICTION OF AIR QUALITY METRICS by Harna M. Bodele, G. M. Asutkar, Kiran G. Asutkar

“…Due to incorporation of dual bioinspired optimizers with autoregressive correlation, the model is able to improve prediction accuracy by 8.5%, precision by 4.9%, recall by 1.5%, while reducing computational delay by 3.4% when compared with standard air quality analysis models. …”
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Model Predictive Control of Nonlinear System Based on GA-RBP Neural Network and Improved Gradient Descent Method by Youming Wang, Didi Qing

“…A model predictive control (MPC) method based on recursive backpropagation (RBP) neural network and genetic algorithm (GA) is proposed for a class of nonlinear systems with time delays and uncertainties. …”
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Evolutionary Synthesis of High-Capacity Reconfigurable Multilayer Road Networks Using a Multiagent Hybrid Clustering-Assisted Genetic Algorithm by Andranik S. Akopov, Levon A. Beklaryan

“…To achieve this goal, a new multiagent hybrid clustering-assisted genetic algorithm (MA-HCAGA). The proposed algorithm combines the use of binary-coded crossovers and mutations as genetic operators, and biobjective discrete particle swarm optimization (BODPSO) techniques to improve the evolutionary search process. …”
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