Modeling and Optimization of Co-TDMA in Next-Generation Wi-Fi Systems Based on Optimal Transportation Theory by Junxiong Zhang, Weimin Wu, Xiaohu Ge, Yingzhuang Liu

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Multiobjective optimization of suspension bridges via coupled modeling and dual population multiobjective particle swarm optimization by Peiling Yang, Jianhua Deng, Anli Wang

“…The algorithm divides the population into two parts, using the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization algorithm (MOPSO) for solving, with improvements to enhance the algorithm’s performance. …”
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Thermodynamic optimization of a multi-generation system integrating gas turbine-helium reactor cycle with hydrogen production and desalination by Yang Wang, Yuliang Chen, Zhen Su

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Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) by Norazman Shahar, Muhammad Amir As'Ari, Tan Tian Swee, Nurul Fathia Ghazali

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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

“…A new data generation method based on a design of experiments (DOE) approach has been developed to improve the accuracy of adsorption modeling using artificial neural networks (ANNs). …”
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An Assessment of High-Order-Mode Analysis and Shape Optimization of Expansion Chamber Mufflers by Min-Chie CHIU, Ying-Chun CHANG

“…Using an eigenfunction (higher-order-mode analysis), a four-pole system matrix for evaluating acoustic performance (STL) is derived. To improve the acoustic performance of the expansion chamber muffler, three kinds of expansion chamber mufflers (KA-KC) with different acoustic mechanisms are introduced and optimized for a targeted tone using a genetic algorithm (GA). …”
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Adaptive energy loss optimization in distributed networks using reinforcement learning-enhanced crow search algorithm by S. Bharath, A. Vasuki

“…Unlike traditional methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard Crow Search Algorithm (CSA), which suffer from premature convergence and limited adaptability to real-time variations, Reinforcement Learning Enhanced Crow Search Algorithm (RL-CSA) which is proposed in this research work solves network reconfiguration optimization problem and minimize energy losses. …”
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Optimized solar PV integration for voltage enhancement and loss reduction in the Kombolcha distribution system using hybrid grey wolf-particle swarm optimization by Awot Getachew Abera, Tefera Terefe Yetayew, Assen Beshr Alyu

“…A hybrid optimization approach combining Particle Swarm Optimization and Grey Wolf Optimization algorithms is proposed for determining optimal sizing and placement of PV-based DGs. …”
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An improved ant colony optimization strategy for dual-objective high-speed train scheduling by Hui Zhao, Jiahuan Zhang, Haixing Li, Dong Li

“…Then, an improved ant colony optimization algorithm is proposed to solve the model. …”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

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Diverse solitons wave structures for coupled NLSEs in birefringent fibers with higher nonlinearities using the modified extended mapping algorithm by Karim K. Ahmed, Njah A. Alsahafi, Hamdy M. Ahmed, Salah Boulaaras, M. S. Osman

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A multi-strategy improved snake optimizer and its application to SVM parameter selection by Hong Lu, Hongxiang Zhan, Tinghua Wang

“…Nevertheless, SO has the shortcomings of weak population initialization, slow convergence speed in the early stage, and being easy to fall into local optimization. To address these problems, an improved snake optimizer algorithm (ISO) was proposed. …”
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Research on improving the ranging accuracy of ships with stereo vision through Kalman filter optimization. by Zhongbo Peng, Jie Han, Liang Tong, Lumeng Wang, Dan Liu, Shijie Sun

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CFD-based aerodynamic optimization of the fairing for a high-speed elevator by Xiawei Shen, Aimin Wang, Wanbing Liu, Rongyang Wang

“…The cross-section of the fairing is parameterized by NURBS curves; then, the Latin experimental design method is used to generate test sample points, a mathematical model is formulated utilizing the response surface model approximation, and global optimization is conducted through the application of a multi-island genetic algorithm. …”
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Optimizing Q-Learning for Automated Cavity Filter Tuning: Leveraging PCA and Neural Networks by Aghanim Amina, Otman Oulhaj, Oukaira Aziz, Lasri Rafik

“…This paper presents a reinforcement learning-based approach to automate the tuning of a 6thorder combline bandpass filter, operating at 941 MHz, using a Q-learning algorithm. To reduce complexity, only two tuning screws are considered in the optimization. …”
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Parameter Optimization of Milling Process for Surface Roughness Constraints by GUO Bin, YUE Caixu, ZHANG Anshan, JIANG Zhipeng, YUE Daxun, QIN Yiyuan

“… In the milling process of 6061 aluminum considering the requirement of controlling the surface roughness of workpiece, artificially selected milling parameters may be conservative, resulting in low material removal rate and high manufacturing cost.Taking the surface roughness as the constraint condition and the maximum material removal rate as the goal, the surface roughness regression model is established based on extreme gradient boosting (XGBOOST) with the spindle speed, feed speed and cutting depth as the optimization objects.The milling parameters of spindle speed, feed speed and cutting depth are optimized by genetic algorithm.The optimal milling parameters are obtained by using the multi objective optimization characteristics of genetic algorithm.It can be seen from the four groups of optimization results that the maximum change of surface roughness is only 0.048μm, while the minimum material removal rate increases by 2458.048mm3/min.While achieving surface roughness, the processing efficiency is improved, and the manufacturing costs are reduced, resulting in good optimization effects, which has a certain guiding role in the actual processing.…”
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Optimizing Assembly Error Reduction in Wind Turbine Gearboxes Using Parallel Assembly Sequence Planning and Hybrid Particle Swarm-Bacteria Foraging Optimization Algorithm by Sydney Mutale, Yong Wang, De Tian

“…The methodology results in a 38% reduction in total assembly errors, improving both process accuracy and efficiency. Specifically, the PSBFO algorithm reduced errors from an initial value of 50 to a final value of 5 across 20 iterations, with components such as the low-speed shaft and planetary gear system showing the most substantial reductions. …”
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Load forecasting of microgrid based on an adaptive cuckoo search optimization improved neural network by Liping Fan, Pengju Yang

“…Finally, the weights and biases of the forecasting model were optimized by the improved cuckoo search algorithm. …”
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Optimization method for educational resource recommendation combining LSTM and feature weighting by Meixia Yang

“…Finally, the bidirectional long short-term memory network algorithm is used for encoding iteration to minimize data omission and improve data interactivity, achieving accurate recommendation of educational resources. …”
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On the Use of an Improved Artificial Fish Swarm Algorithm-Backpropagation Neural Network for Predicting Dam Deformation Behavior by Bo Dai, Hao Gu, Yantao Zhu, Siyu Chen, E. Fernandez Rodriguez

“…The hybrid model’s preciseness is verified against the radial displacements of a pendulum in a concrete arch dam and simulations of four models: statistical model, BP-ANN optimized by genetic algorithm (GA), particle swarm optimization (PSO), and AFSA. …”
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