Large-scale post-disaster user distributed coverage optimization based on multi-agent reinforcement learning by Wenjun XU, Silei WU, Fengyu WANG, Lan LIN, Guojun LI, Zhi ZHANG

“…In order to quickly restore emergency communication services for large-scale post-disaster users, a distributed intellicise coverage optimization architecture based on multi-agent reinforcement learning (RL) was proposed, which could address the significant differences and dynamics of communication services caused by a large number of access users, and the difficulty of expansion caused by centralized algorithms.Specifically, a distributed k-sums clustering algorithm considering service differences of users was designed in the network characterization layer, which could make each unmanned aerial vehicle base station (UAV-BS) adjust the local networking natively and simply, and obtain states of cluster center for multi-agent RL.In the trajectory control layer, multi-agent soft actor critic (MASAC) with distributed-training-distributed-execution structure was designed for UAV-BS to control trajectory as intelligent nodes.Furthermore, ensemble learning and curriculum learning were integrated to improve the stability and convergence speed of training process.The simulation results show that the proposed distributed k-sums algorithm is superior to the k-means in terms of average load efficiency and clustering balance, and MASAC based trajectory control algorithm can effectively reduce communication interruptions and improve the spectrum efficiency, which outperforms the existing RL algorithms.…”
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Large-scale post-disaster user distributed coverage optimization based on multi-agent reinforcement learning by Wenjun XU, Silei WU, Fengyu WANG, Lan LIN, Guojun LI, Zhi ZHANG

“…In order to quickly restore emergency communication services for large-scale post-disaster users, a distributed intellicise coverage optimization architecture based on multi-agent reinforcement learning (RL) was proposed, which could address the significant differences and dynamics of communication services caused by a large number of access users, and the difficulty of expansion caused by centralized algorithms.Specifically, a distributed k-sums clustering algorithm considering service differences of users was designed in the network characterization layer, which could make each unmanned aerial vehicle base station (UAV-BS) adjust the local networking natively and simply, and obtain states of cluster center for multi-agent RL.In the trajectory control layer, multi-agent soft actor critic (MASAC) with distributed-training-distributed-execution structure was designed for UAV-BS to control trajectory as intelligent nodes.Furthermore, ensemble learning and curriculum learning were integrated to improve the stability and convergence speed of training process.The simulation results show that the proposed distributed k-sums algorithm is superior to the k-means in terms of average load efficiency and clustering balance, and MASAC based trajectory control algorithm can effectively reduce communication interruptions and improve the spectrum efficiency, which outperforms the existing RL algorithms.…”
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An intelligence technique for route distance minimization to store and marketize the crop using computational optimization algorithms by Saikat Banerjee, Abhoy Chand Mondal

“…This research aims to develop connectivity across many cold storage facilities utilizing the traveling salesperson problem algorithm. Various computational intelligence algorithms such as Greedy Algorithm, Simulated Annealing, 2-opt Algorithm, Particle Swarm Optimization, and Ant Colony Optimization are employed to determine the minimum route. …”
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Sequential Routing-Loading Algorithm for Optimizing One-Door Container Closed-Loop Logistics Operations by Paulina Kus Ariningsih, Titi Iswari, Kevin Djoenneady Poetra, Yoon Mac Kinley Aritonang

“…The improvement algorithm is tested in big data set with the input of the vehicle routing problem with time windows (VRP-TW) using the solution optimization of the Simulated Annealing process with restart point procedure (SA-R) for the routing optimization and Genetic Algorithm (GA) to optimize the container loading algorithm. …”
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Constrained MPC for a linear system to track time-varying reference signals by Renlong WU, Nobutaka WADA

“…This paper presents a model predictive control algorithm under input and state constraints. …”
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The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports. by Guomei Cui, Chuanjun Wang

“…Specifically, by introducing adaptive feature selection and ensemble learning methods, the decision tree algorithm effectively improves the recognition ability of the model for different athletes and sports states, thus reducing the over-fitting phenomenon and improving the generalization ability. …”
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Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm by Zhenning Yuan, Jong Han Lee, Sai Zhang

“…Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. …”
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Optimizing an LSTM Self-Attention Architecture for Portuguese Sentiment Analysis Using a Genetic Algorithm by Daniel Parada, Alexandre Branco, Marcos Silva, Fábio Mendonça, Sheikh Mostafa, Fernando Morgado-Dias

“…To address this complexity, a discrete genetic algorithm was used to find an optimal configuration, selecting the layer types, placement of self-attention, dropout rate, and model dimensions and shape. …”
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QELPS Algorithm: A Novel Dynamic Optimization Technology for Quantum Circuits Scheduling Engineering Problems by Zuoqiang Du, Xingjie Li, Hui Li

“…Meanwhile, FJOSA employs a cross-layer optimization strategy that combines heuristic algorithms with cost functions to improve gate scheduling at a global level. …”
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Research on Audit Risk Prediction in Enterprise Management Based on Optimized BP Neural Network Algorithm by Wang Mingming

“…Under the development of enterprise management intelligence, there are more and more studies on the identification and evaluation of audit risks, in order to accurately identify enterprise audit risks, enterprises have created an audit risk identification model with artificial intelligence algorithm as the core, which aims to identify enterprise audit risks with high quality and significantly improve audit efficiency. …”
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Research on Stability Optimization for Automatic Train Operation of Heavy-haul Trains of Baoshen Railway by FU Shangyuan

“…During the departing stage, the optimization algorithm effectively improved operational stability on steep grades, and increased the departing speed by about 11.1%. …”
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Enhanced Network Traffic Classification Using Bayesian-Optimized Logistic Regression and Random Forest Algorithm by Manisankar Sannigrahi, R. Thandeeswaran

“…Bayesian optimization is employed to systematically fine-tune the model’s hyperparameters, thereby improving accuracy and efficiency by concentrating on promising areas of the hyperparameter space and avoiding unnecessary evaluations. …”
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Optimal Design of Linear Quadratic Regulator for Vehicle Suspension System Based on Bacterial Memetic Algorithm by Bala Abdullahi Magaji, Aminu Babangida, Abdullahi Bala Kunya, Péter Tamás Szemes

“…The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. …”
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A Surrogate-Assisted Gray Prediction Evolution Algorithm for High-Dimensional Expensive Optimization Problems by Xiaoliang Huang, Hongbing Liu, Quan Zhou, Qinghua Su

“…The proposed algorithm improves optimization efficiency by combining the macro-predictive ability of the even gray model in GPE for population update trends and the predictive ability of surrogate models to synergistically guide population searches in promising directions. …”
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Optimizing Server Load Distribution in Multimedia IoT Environments through LSTM-Based Predictive Algorithms by Somaye Imanpour, AhmadReza Montazerolghaem, Saeed Afshari

“…The findings from the simulations indicate that the proposed approach enhances the optimization and management of IoT networks, resulting in improved service quality, reduced operational costs, and increased productivity.…”
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Optimal Scheduling of Hydro-photovoltaic Complementary Systems Based on Multi-objective Moth-flame Algorithm by LI Ze-hong, YUAN Xiao-feng, XIAO Peng, ZHANG Tai-heng, QIN Hui

“…The combination of these two led to the development of a new high-performance multi-objective evolutionary algorithm: R-IMOMFO. A multi-objective optimization scheduling model for hydro-photovoltaic complementary systems was established, considering both power generation benefits and capacity benefits, and the model was solved using the R-IMOMFO algorithm. …”
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Sentiment analysis using long short term memory and amended dwarf mongoose optimization algorithm by Haisheng Deng, Ahmed Alkhayyat

“…To enhance the performance long short-term memory (LSTM), the model was optimized using the amended dwarf mongoose optimization (ADMO) algorithm, leading to improvements in the hyperparameters. …”
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Improved method for a pedestrian detection model based on YOLO by Yanfei LI, Chengyi DONG

“…Experimental validation revealed significant performance improvements over the original YOLOv8n model. This enhanced architecture achieved 7.2% and 9.2% increases in mAP0.5 and mAP0.5:0.95 metrics respectively for dense pedestrian detection, with corresponding improvements of 7.6% and 8.7% observed in actual farmland working environments. …”
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ACM-YOLOv10: Research on Classroom Learning Behavior Recognition Algorithm Based on Improved YOLOv10 by Beichen Qin, Haoyan Hu, Shaowen Du

“…Additionally, generalization experiments conducted on another SB dataset confirm that the improved algorithm model possesses good generalization performance.…”
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LIGHTWEIGHT DESIGN OF THE BASE FOR RECIPROCATING PISTON DIAPHRAGM PUMP BASE ON MULTI-OBJECTIVE OPTIMIZATION ALGORITHM by MA WenSheng, FENG ZhiWei, SHEN Yan, SUN ZhongZhi, NIU PeiYu, LIU ChunChuan, LI FangZhong, CHEN Tao

“…The lightweight design of diaphragm pump base structure has an important impact on the processing and production of diaphragm pump.Based on the research of the frame structure of a certain type of diaphragm pump,the equivalent model was established for the actual working environment and the finite element analysis was carried out.The design variables were defined according to analysis results to improve the calculation efficiency.The uniform test design method was adopted for the test design,and the relationship between the design variables and the stress and deformation was calculated through simulation fitting.The lightweight optimization mathematical model was established for the diaphragm pump base structure by using the multi-objective optimization algorithm.On the premise of meeting the performance requirements,some materials were reasonably configured,and the stress,deformation and natural frequency of the diaphragm pump base structure were as small as possible.The frame structure after the lightweight optimization design was simulated and analyzed,and compared with the structure before optimization.The results show that the structural properties of the engine base remain unchanged after lightweight,and the weight is reduced from 25372 kg to 24582 kg,with a weight reduction of 790 kg.The weight reduction effect is good,the maximum stress value is reduced by 45.1%,and the maximum deformation is reduced by 12.3%.The optimization effect is remarkable,which provides a basic support for the finite element analysis and lightweight optimization design of the new diaphragm pump structure.…”
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