Optimization of multi-structural parameters in metamaterials based on the DGN co-simulation method. by Shangyang Jin, Fuxing Chen, Jie Bai, Bingfei Liu

“…Then the global algorithm is combined with the local algorithm to solve the problem of poor convergence of the global optimization algorithm while ensuring the optimization quality of the local optimization algorithm. …”
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APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…Therefore, it is reasonable to model the problem of improving network scalable degree as minimizing network coupling degree,and it is feasible to improve network scalable degree by reducing network coupling degree.2)The upper limit of computational complexity of the proposed algorithm is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">O</mi><mo stretchy="false">(</mo><mi>K</mi><mi>N</mi><msub> <mi>log</mi> <mn>2</mn> </msub> <mi>N</mi><mo>+</mo><msup> <mi>k</mi> <mn>2</mn> </msup> <mo>+</mo><mi>N</mi><mi>N</mi><msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo stretchy="false">)</mo></math></inline-formula>,while that of directly solving the optimization problem is<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup> <mi>N</mi> <mrow> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>u</mtext> </msub> <mi>K</mi></mrow> </msup> <mo stretchy="false">)</mo></math></inline-formula>.3)For theoretical analysis of the network scalable degree,take Fig.3 as an example.If AP2 changes,12 APs in Fig. 3(a)are affected and the network scalable degree is η₂=0.51,while 4 APs in Fig.3(c)are affected and the network scalable degree is η₂=0.79.4)Fig.5 shows the simulation results of network scalable degree.Compared with the traditional strategy,the network scalable degree is improved by 9.59% with 4.43% user rate loss.Compared with the strategy in[10],the network scalable degree is improved by 22.15% with 4.99% user rate loss. 5) The algorithm parameters, the threshold β₀of overlap rate and the upper limit number N₀of AP associated, effect the performance.As shown in Fig.6,with β₀or N₀decreases,η increases and the total user rate decreases. …”
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Networked Sensor-Based Adaptive Traffic Signal Control for Dynamic Flow Optimization by Xinhai Wang, Wenhua Shao

“…This method integrates the Webster algorithm with a proportional–integral–derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. …”
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COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment by Lili Jiang, Liu Yang, Yang Huang, Yi Wu, Huixian Li, XiYan Shen, Meng Bi, Lin Hong, Yiting Yang, Zuping Ding, Wenjie Chen

“…In order to detect high error rate and poor convergence of the water ecological chemical oxygen demand (COD) prediction model, combining the limit learning machine (ELM) model and whale optimization algorithm, CAWOA is improved by the sin chaos search strategy, while the ELM optimizes the parameters of the algorithm to improve convergence speed, thus improving the generalization performance of the ELM. …”
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Optimized Integral Super-Twisting Sliding Mode Control for Acute Leukemia Therapy by Muhammad Munir Butt, Azhar Iqbal Kashif Butt

“…These improvements highlight the potential of ISTSMC in optimizing chemotherapy administration, ensuring better patient outcomes while minimizing side effects.…”
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Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm by Zhuang Liu, Ning Yang, Jiaxing Fu, Huanqing Cai, Xuebei Wei

“…An S-shaped characteristic function-based nonlinear evolution factor is employed to balance global exploration and local exploitation of the optimal path, while a Weibull flight operator mutation strategy is designed to enable the algorithm to escape from locally optimal paths, enrich the search space, and improve convergence accuracy. …”
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Optimizing Fairness and Spectral Efficiency With Shapley-Based User Prioritization in Semantic Communication by Moirangthem Tiken Singh, Adnan Arif, Rabinder Kumar Prasad, Bikramjit Choudhury, Chandan Kalita, Sikdar Md. S. Askari

“…Notably, as the number of channels increases, S-SE stabilizes while fairness continues to improve, approaching optimal levels in diverse system configurations. …”
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Optimal scheduling of BIES with multi-energy flow coupling based on deep RL by XIA Xuhua, YANG Jiandi, SHI Yongtao

“…Building integrated energy systems （BIESs） can enhance energy efficiency ratio （EER） and reduce carbon emissions while meeting diverse user-side load demands. To further improve the energy dispatch capability of BIES， this paper proposes a low-carbon economic and optimal dispatch method for BIES with multi-energy flow coupling based on deep reinforcement learning （deep RL）. …”
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Deep Reinforcement Learning-Based Motion Control Optimization for Defect Detection System by Yuhuan Cai, Liye Zhao, Xingyu Chen, Zhenjun Li

“…A composite reward mechanism is introduced to mitigate potential motor instability, while CP-MPA is utilized to optimize the performance of the proposed m-TD3 composite controller. …”
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Two-Stage Integrated Optimization Design of Reversible Traction Power Supply System by Xiaodong Zhang, Wei Liu, Qian Xu, Zhuoxin Yang, Dingxin Xia, Haonan Liu

“…The parallel cheetah algorithm is employed to solve this complex optimization problem. …”
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A comprehensive review of artificial intelligence approaches for smart grid integration and optimization by Malik Ali Judge, Vincenzo Franzitta, Domenico Curto, Andrea Guercio, Giansalvo Cirrincione, Hasan Ali Khattak

“…The advanced metaheuristic algorithms are a good addition to the literature, they are still in emerging stages and their performance can further be improved. …”
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Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review by Ivan P. Malashin, Dmitry A. Martysyuk, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…Studies have leveraged hybrid models, including Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU) networks for emission control and Neutrosophic Fuzzy Optimization (NFO) for uncertainty handling. While existing models demonstrate improvements in predictive accuracy and optimization effectiveness, challenges remain in model generalization, computational complexity, and real-time adaptability. …”
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Decentralized Multi-Robot Navigation Based on Deep Reinforcement Learning and Trajectory Optimization by Yifei Bi, Jianing Luo, Jiwei Zhu, Junxiu Liu, Wei Li

“…Additionally, it introduces safety constraints through an artificial potential field (APF) to optimize these trajectories. Additionally, a constrained nonlinear optimization method further refines the APF-adjusted paths, resulting in the development of the GNN-RL-APF-Lagrangian algorithm. …”
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Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective by Zheng Shi, Lu Yan, Yingying Hu, Yao Wang, Wenping Qin, Yan Liang, Haibo Zhao, Yongming Jing, Jiaojiao Deng, Zhi Zhang

“…The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. …”
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Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers by Firman Aziz, Mutia Maulida, Jafar Jafar, Nurafni Shahnyb, Norma Nasir, Ampauleng Ampauleng

“…This study presents the development and optimization of an Ensemble Least Squares (ELS) algorithm to enhance the classification of potential deposit customers. …”
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A Novel Hybrid Metaheuristic MPA-PSO to Optimize the Properties of Viscous Dampers by Elmira Shemshaki, Mohammad Hasan Haddad, Mohammadreza Mashayekhi, Seyyed Meisam Aghajanzadeh, Ali Majdi, Ehsan Noroozinejad Farsangi

“…The results show that the hybrid algorithm has demonstrated significant performance improvement compared to the independent methods in identifying optimal values. …”
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Artificial intelligence for smart irrigation: Reducing water consumption and improving agricultural output by Arlanova A. A., Hojamkuliyeva B. A., Babanazarov N. Sh., Arlanov M. S.

“…The study investigates the application of artificial intelligence for optimizing irrigation systems in agriculture, aiming to reduce water losses and improve production efficiency. …”
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Predicting compressive strength of concrete at elevated temperatures and optimizing its mixture proportions by Jinjun Xu, Han Wang, Wenjun Wu, Lang Lin, Yong Yu

“…The Cuckoo search algorithm was then employed to optimize mix designs, balancing high-temperature strength, cost and sustainability. …”
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Hybrid Optimization Machine Learning Framework for Enhancing Trust and Security in Cloud Network by Himani Saini, Gopal Singh, Amrinder Kaur, Sunil Saini, Niyaz Ahmad Wani, Vikram Chopra, Zahid Akhtar, Shahid Ahmad Bhat

“…For resource allocation, the framework employs the Time-aware modified best fit decreasing (T-MBFD) algorithm, which adapts to fluctuating workloads. Key input parameters for T-MBFD include available resources, job size, and time constraints, while output parameters focus on optimized resource distribution and minimizing wastage. …”
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Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving by Xiaohua Gu, Taifu Li, Zhiqiang Liao, Liping Yang, Ling Nie

“…It firstly employs the general regression neural network (GRNN) algorithm to obtain the best model of the beam pumping system, and secondly searches the optimal operation parameters with improved strength Pareto evolutionary algorithm (SPEA2). …”
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