Optimal EVs Charge Station Allocation Considering Residents Dispersion Using a Genetic Algorithm and Weighted K-Means by Rafael Monteagudo, Edgardo D. Castronuovo, Ramon Barber

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Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice by Guolin SUN, Ruijie OU, Guisong LIU

“…Based on the requirements of ultra-low latency services for emergency Internet-of-things (EIoT) applications,a multi-slice network architecture for ultra-low latency emergency IoT was designed,and a general methodology framework based on resource reservation,sharing and isolation for multiple slices was proposed.In the proposed framework,real-time and automatic inter-slice resource demand prediction and allocation were realized based on deep reinforcement learning (DRL),while intra-slice user resource allocation was modeled as a shape-based 2-dimension packing problem and solved with a heuristic numerical algorithm,so that intra-slice resource customization was achieved.Simulation results show that the resource reservation-based method enable EIoT slices to explicitly reserve resources,provide a better security isolation level,and DRL could guarantee accuracy and real-time updates of resource reservations.Compared with four existing algorithms,dueling deep Q-network (DQN) performes better than the benchmarks.…”
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Designing a Multiobjective Human Resource Scheduling Model Using the Tabu Search Algorithm by Roghayeh Mirataollahi olya, Seyed Ahmad Shayannia, Mohammad Mehdi movahedi

“…In this research, considering the high importance of these issues, the problems of scheduling and allocation of manpower in a real place are solved. To this end, the metaheuristic Tabu search algorithm is used with the aim of minimizing the duration of activity and the presence of all manpower. …”
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A FPGA Accelerator of Distributed A3C Algorithm with Optimal Resource Deployment by Fen Ge, Guohui Zhang, Ziyu Li, Fang Zhou

“…In addition, the resource wastage problem caused by the distributed training characteristics of A3C algorithms and the resource allocation problem affected by the imbalance between the computational amount of inference and training need to be carefully considered when designing accelerators. …”
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Educational resources recommendation algorithm based on GMF-MLP-NeuMF prediction model by Rui Wang, Qiusheng Cui, Yezhi Xiao, Jinfeng Du, Ruicheng Yue

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Algorithmic Approach of Majority Voting With Agents’ Inclusiveness for Facility Resource Matching by Peash Ranjan Saha, Salimur Choudhury, Kai Salomaa

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A traffic-awareness dynamic resource allocation scheme based on multi-objective optimization in multi-beam mobile satellite communication systems by YuanZhi He, YiZhen Jia, XuDong Zhong

“…Since the dynamic resource allocation problem is formulated as NP-hard, a new traffic-aware dynamic resource allocation (TADRA) algorithm is proposed. …”
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Spatially optimized allocation of water and land resources based on multi-dimensional coupling of water quantity, quality, efficiency, carbon, food, and ecology by Yingbin Wang, Haiqing Wang, Jiaxin Sun, Peng Qi, Wenguang Zhang, Guangxin Zhang

“…The COM-WL model integrates our improved genetic algorithm, PAEA-NSGAⅢ, with the landscape allocation model, GridLOpt. …”
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Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems by LIU Qingli, LI Xiaoyu, LI Rui

“…The method was adopted in a three-stage strategy, firstly, an RF beamformer was constructed to reduce the hardware cost and total power consumption through a small number of RF chains; secondly, a baseband precoder was designed using the effective channel state information; and finally, a two-tier deep reinforcement learning architecture was designed and applied to realize dynamic discrete bandwidth and continuous power resource allocation. Experimental results show that the proposed joint optimization method significantly improves the throughput and energy efficiency of the system compared with the single-stage all-digital precoding and hybrid precoding equal resource allocation methods and the particle swarm optimization-based resource allocation algorithm.…”
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Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks by Muhammad Naqqash Tariq, Jingyu Wang, Salman Raza, Mohammad Siraj, Majid Altamimi, Saifullah Memon

“…However, the growing number of UAVs and smart devices causing a major difficulty in the devising an effective scheme for the task offloading and resource allocation in multi-UAV-aided MEC networks. …”
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Joint Resource Allocation and Power Control in Rate-Splitting Multiple Access-Based Integrated Terrestrial and Non-Terrestrial Networks With HAP Assistance by Thong-Nhat Tran, Young Jeon, Heejung Yu, Taejoon Kim

“…The non-convex JRPS problem is reformulated as a linear program and solved using an iterative successive convex approximation algorithm that ensures local optimality. Additionally, a heuristic resource allocation and power control method is also proposed to provide an effective initialization for JRPS and to serve as a performance baseline. …”
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Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks by Apostolos Avranas, Philippe Ciblat, Marios Kountouris

“…Our method can, for instance, achieve with 13% less power and bandwidth resources the same user satisfaction rate as a myopic algorithm using knapsack optimization.…”
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Beamforming and resource optimization in UAV integrated sensing and communication network with edge computing by Bin LI, Sicong PENG, Zesong FEI

“…To address the dependence of traditional integrated sensing and communication network mode on ground infrastructure, the unmanned aerial vehicle (UAV) with edge computing server and radar transceiver was proposed to solve the problems of high-power consumption, signal blocking, and coverage blind spots in complex scenarios.Firstly, under the conditions of satisfying the user’s transmission power, radar estimation information rate and task offloading proportion limit, the system energy consumption was minimized by jointly optimizing UAV radar beamforming, computing resource allocation, task offloading, user transmission power, and UAV flight trajectory.Secondly, the non-convex optimization problem was reformulated as a Markov decision process, and the proximal policy optimization method based deep reinforcement learning was used to achieve the optimal solution.Simulation results show that the proposed algorithm has a faster training speed and can reduce the system energy consumption effectively while satisfying the sensing and computing delay requirements.…”
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Initialization Methods for FPGA-Based EMT Simulations by Xin Ma, Xiao-Ping Zhang

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Modelled Energy Cost Minimization Solution for Wireless Rechargeable Sensor Networks by Musa Ahmed, Dominic Saaityo NYITAMEN, Fatai Olatunde Adunola, Sagir Lawan

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A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks by Nouri Omheni, Anis Amiri, Faouzi Zarai

“…Next, the Q‐Learning algorithm is used to design a resource allocation algorithm based on RL. …”
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Mode selection and resource optimization for UAV-assisted cellular networks by Daquan FENG, Canjian ZHENG, Xiangqi KONG

“…The resource allocation and optimization scheme was studied in a coexistence scenario of unmanned aerial vehicle (UAV) and cellular communication network.To improve spectrum efficiency of the system, UAV users could reuse the cellular spectrum resources to access the network through full duplex or half duplex device-to-device technique.Additionally, a joint access control, mode selection, power control and resource allocation optimization problem was formulated to maximize the overall throughput of the network while ensuring quality of service requirements for both UAV users and ground cellular users.Specifically, the phase 1 method in the convex optimization was adopted for access control and feasibility check, and then the convex and concave procedure (CCCP) iterative algorithm was used to solve the power control problem for feasible UAV user pairs.By using this local optimum value, the original optimization problem can be simplified into a weighted maximization problem.Finally, the Kuhn-Munkres (KM) algorithm was used to match the optimal channel resources and obtain the global optimal throughput value of the system.Numerical results show that the proposed scheme can significantly improve the performance of system.…”
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A Robust Multi-Objective Evolutionary Framework for Artificial Island Construction Scheduling Under Dynamic Constraints by Tianju Zheng, Liping Sun, Mingwei Li, Guangyao Yuan, Shuqi Li

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A Continuous Monitoring and Reallocation Method for Successful Decisions in Change Management by Fotios Panagiotopoulos, Vassilios Chatzis

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Improving Streaming Capacity in Multi-Channel P2P VoD Systems via Intra-Channel and Cross-Channel Resource Allocation by Yifeng He, Ling Guan

“…We demonstrate in the simulations that the correlated-channel P2P VoD systems with both intra-channel and cross-channel resource allocation can obtain a higher average streaming capacity compared to the independent-channel P2P VoD systems with only intra-channel resource allocation.…”
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