Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks. by Jihong Wang, Hao Ni, Yiyang Ge, Shuo Li

“…To be specific, ions motion optimization algorithm is leveraged to automatically determine the optimal number of clusters and form basic clustering structure. …”
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Deep Reinforcement Learning-Based Joint Routing and Capacity Optimization in an Aerial and Terrestrial Hybrid Wireless Network by Zhe Wang, Hongxiang Li, Eric J. Knoblock, Rafael D. Apaza

“…Specifically, the Dueling Double Deep Q-Network (D3QN) structure is constructed to learn an optimal policy through trial and error. …”
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RETRACTED ARTICLE: Multiclass skin lesion classification using deep learning networks optimal information fusion by Muhammad Attique Khan, Ameer Hamza, Mohammad Shabaz, Seifeine Kadry, Saddaf Rubab, Muhammad Abdullah Bilal, Muhammad Naeem Akbar, Suresh Manic Kesavan

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Data-Driven Optimization Method for Recurrent Neural Network Algorithm: Greenhouse Internal Temperature Prediction Model by Kwang Cheol Oh, Sunyong Park, Seok Jun Kim, La Hoon Cho, Chung Geon Lee, Dae Hyun Kim

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Topology Optimization of the Network with Renewable Energy Sources Generation Based on a Modified Adapted Genetic Algorithm by A. M. Bramm, A. I. Khalyasmaa, S. A. Eroshenko, P. V. Matrenin, N. A. Papkova, D. A. Sekatski

“…The article presents an adaptive genetic algorithm developed by the authors, which makes it possible to optimize the topology of a power network with distributed generation. …”
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Identification of Key Modules of Lung Cancer in Gene Regulatory Network using Greedy Modularity Optimization Approach by Zahra Sadat Mirghadery, Mehrdad Kargari, Mostafa Akhavan-Safar

“…In this method, first, using gene expression data and regulatory interactions, a lung cancer regulatory network is constructed. Then, using a greedy modularity optimization approach, communities related to lung cancer are identified. …”
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Optimizing soybean variety selection for the Pan-African Trial network using factor analytic models and envirotyping by Maurício S. Araújo, João P. S. Pavan, André A. Stella, Bruno F. Fregonezi, Natally F. Lima, Erica P. Leles, Michelle F. Santos, Peter Goldsmith, Godfree Chigeza, Brian W. Diers, José B. Pinheiro

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Security Performance Analysis of Downlink Double Intelligent Reflecting Surface Non-Orthogonal Multiple Access Network for Edge Users by Nguyen Thai Anh, Nguyen Hoang Viet, Dinh-Thuan Do, Adão Silva

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Study on distributed and dynamic resource management for delay-sensitive sensor network by Wei LIU, Jun LIU

“…The delay-aware dynamic resource management problem was investigated in sensor network,with a focus on resource allocation among the sensors and power control along the time.By taking account of average delay requirements and power constraints,the considered problem was formulated into a stochastic optimization problem.Inspired by Lyapunov optimization theory,the intractable stochastic optimization problem was transformed into a tractable deterministic optimization problem,which was a mixed-integer resource management problem.By exploiting the specific problem structure,the mixed-integer resource management problem was equivalently transformed into a single variable problem,and the cooperative distributed method was present to effectively solve it with guaranteed global optimality.Finally,a dynamic resource management algorithm was proposed to solve the original stochastic optimization problem.Simulation results show the performance of the proposed dynamic algorithm and reveal that there exists a fundamental tradeoff between delay requirements and power consumption.…”
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CFD-based optimization of dynamic cyclones with variable vortex length using GMDH artificial neural network by Hamed Safikhani, Somayeh Davoodabadi Farahani, Lakhbir Singh Brar, Faroogh Esmaeili

“…These models are developed using artificial neural networks based on the Group Method of Data Handling (GMDH). …”
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A DQN-Based Algorithm for Operational Optimization of Freight Trains in Long Steep Downhill Sections by HE Zhiyu, LI Yinan, LI Hui, JI Zhijun

“…This study proposes a deep Q-network (DQN) based intelligent curve generation algorithm for operational optimization in these sections. …”
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Age of Information Minimization in Multicarrier-Based Wireless Powered Sensor Networks by Juan Sun, Jingjie Xia, Shubin Zhang, Xinjie Yu

“…We formulate this optimization problem as a multi-stage stochastic optimization program. …”
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Robust Expansion Planning of Electric Vehicle Charging System and Distribution Networks by Yue Xiang, Ping Xue, Yanliang Wang, Lixiong Xu, Wang Ma, Miadreza Shafie-khah, Junlong Li, Junyong Liu

“…To address this issue, a collaborative planning scheme of the EV charging system and distribution networks that combines economic and reliability benefits is proposed, which is formulated as a two-stage distributionally robust optimization model considering source-load uncertainties. …”
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Two-Criteria Technique for the Resource-Saving Computing in the Fog and Edge Network Tiers by A. B. Klimenko

“…Based on the proposed model of the two-criteria optimization problem, a method was proposed for resource saving in the edge and foggy layers of the network. …”
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Computationally Efficient Design of an LNA Input Matching Network Using Automatic Differentiation by Kiran A. Shila

“…The input matching network consists of a non-uniform suspended stripline transformer, directly optimized with AD-provided gradients. …”
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Joint optimization of edge computing and caching in NDN by Yu ZHANG, Min CHENG

“…Named data networking (NDN) is architecturally easier to integrate with edge computing as its routing is based on content names and its nodes have caching capabilities.Firstly, an integrated framework was proposed for implementing dynamic coordination of networking, computing and caching in NDN.Then, considering the variability of content popularity in different regions, a matrix factorization-based algorithm was proposed to predict local content popularity, and deep reinforcement learning was used to solve the the problem of joint optimization for computing and caching resource allocation and cache placement policy with the goal of maximizing system operating profit.Finally, the simulation environment was built in ndnSIM.The simulation results show that the proposed scheme has significant advantages in improving cache hit rate, reducing the average delay and the load on the remote servers.…”
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Reliable heuristic mapping method for survivable virtual network by Qiang ZHU, Hui-qiang WANG, Chun-guang MA, Guang-sheng FENG, Hong-wu LV

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Accelerated Decentralized Load Balancing in Multi-Agent Networks by Victoria Erofeeva, Oleg Granichin, Elena Volodina

“…Decentralized load balancers are gaining in popularity because they offer scalability, resilience, and the ability to handle high-demand workloads in distributed network systems. In practice, decentralized algorithms face such network issues as connection losses, dropped packets during data transmission, network latency. …”
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An Optimized 1-D CNN-LSTM Approach for Fault Diagnosis of Rolling Bearings Considering Epistemic Uncertainty by Onur Can Kalay

“…A physics-guided method that adopts fault characteristics frequencies was used to calculate an optimal input size (sample length). Moreover, grid search was utilized to optimize (1) the number of epochs, (2) batch size, and (3) dropout ratio and further enhance the efficacy of the proposed 1-D CNN-LSTM network. …”
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Improved grey wolf optimization algorithm based service function chain mapping algorithm by Yue ZHANG, Junnan ZHANG, Xiaochun WU, Chen HONG, Jingjing ZHOU

“…With the rise of new Internet applications such as the industrial Internet, the Internet of vehicles, and the metaverse, the network’s requirements for low latency, reliability, security, and certainty are facing severe challenges.In the process of virtual network deployment, when using network function virtualization technology, there were problems such as low service function chain mapping efficiency and high deployment resource overhead.The node activation cost and instantiation cost was jointly considered, an integer linear programming model with the optimization goal of minimizing the average deployment network cost was established, and an improved grey wolf optimization service function chain mapping (IMGWO-SFCM) algorithm was proposed.Three strategies: mapping scheme search based on acyclic KSP algorithm, mapping scheme coding and improvement based on reverse learning and nonlinear convergence were added to the standard grey wolf optimization algorithm to form this algorithm.The global search and local search capabilities were well balanced and the service function chain mapping scheme was quickly determined by IMGWO-SFCM.Compared with the comparison algorithm, IMGWO-SFCM reduces the average deployment network cost by 11.86% while ensuring a higher service function chain request acceptance rate.…”
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