Optimizing music course scheduling with real number encoding and chaos genetic algorithm by Shu Li

“…Traditional scheduling systems usually use simple algorithms or manual intervention, resulting in low efficiency and uneven resource allocation. …”
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QoS Aware Task Scheduling Using Hybrid Genetic Algorithm in Cloud Computing by Keyvan Atbaee Tabary, Homayun Motameni, Behnam Barzegar, Ebrahim Akbari, Hossien Shirgahi, Mehran Mokhtari

“…PSO-SMPIA and GA-SMPIA algorithms are presented for task scheduling and resource allocation with the aim of reducing makespan and total execution time, and increasing resource utilization in CC. …”
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Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration by Ghulam Abbas, Zhi Wu, Aamir Ali

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Service chain deployment algorithms for deterministic end-to-end delay upper bound by Ze’nan WANG, Jiao ZHANG, Shuo WANG, Tao HUANG, F.Richard Yu

“…To solve the problem that the current service chain deployment algorithms cannot guarantee the delay of each packet passing through the service chain (SC), a SC deployment algorithm for deterministic end-to-end delay upper bound was proposed.First, the end-to-end delay bound of the SC was derived based on network calculus.Then, the deterministic end-to-end delay bound of the SC was achieved by collaboratively optimizing the routing of SC and the resource allocation of the virtual network function nodes in the SC.The experimental results show that the proposed algorithm can effectively improve the volume of accepted SC while guaranteeing that the end-to-end delay of each packet satisfies the delay requirements.…”
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Fair and efficient opportunistic interference alignment algorithm based on round-robin scheduling by Xian-zhong XIE, Hua-bing LU, Zhao-yuan SHI

“…Opportunistic interference alignment (OIA) algorithm was proposed for the practical implementation of interference alignment (IA).A fair and efficient OIA algorithm was presented for the unfairness in resource allocation and high dependence of tremendous users in the existing OIA algorithms.Firstly,the users with the best channel was selected in the primary cell based on round-robin scheduling after the coordinate cluster was determined.Then,the interference from the primary users was eliminated by skillfully designing the useful signal spaces in the subordinate cells.Furthermore,the users with the minimum interference leakage was selected in the subordinate cells.Finally,the fairness performance was theoretically analyzed.Simulation results show that both the sum-rate and fairness of the proposed algorithm are significantly higher than that of the conventional algorithm with less interference leakage.Besides,the users can achieve a quick access.…”
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Innovative application of particle swarm algorithm in the improvement of digital enterprise management efficiency by Shengnan Zhang

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Joint beam hopping and coverage control optimization algorithm for multibeam satellite system by Guoliang XU, Feng TAN, Yongyi RAN, Feng CHEN

“…To improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimization problem with the objective maximizing the system throughput and minimizing the packet loss rate of the MBS.Secondly, the MBS environment was characterized as a multi-dimensional matrix, and the objective problem was modelled as a Markov decision process considering stochastic communication requirements.Finally, the objective problem was solved by combining the powerful feature extraction and learning capabilities of deep reinforcement learning.In addition, a single-intelligence polling multiplexing mechanism was proposed to reduce the search space and convergence difficulty and accelerate the training of BHCC.Compared with the genetic algorithm, the simulation results show that BHCC improves the throughput of MBS and reduces the packet loss rate of the system, greedy algorithm, and random algorithm.Besides, BHCC performs better in different communication scenarios compared with a deep reinforcement learning algorithm, which do not consider the adaptive beam coverage.…”
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A multi-greedy spectrum auction algorithm for cognitive small cell networks by Feng Zhao, Bo Liu, Hongbin Chen

“…The proposed algorithm can achieve conflict-free spectrum allocations that maximize the utility and spectrum allocation efficiency. …”
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Program Scheduling With Multi-Skill and External Resource Coordination Consideration Using Improved NSGA-II: Case Study by Heng Zhang, Jingbo Zhou, Huaying Ruan, Yixuan Qin

“…Technology-driven startups often experience fluctuations in business volume and their delivery peak exceeds the conventional functional department’s human resource staffing. Resource allocation is crucial for ensuring timely completion of project deliverables within resource constraints. …”
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New design paradigm for federated edge learning towards 6G:task-oriented resource management strategies by Zhiqin WANG, Jiamo JIANG, Peixi LIU, Xiaowen CAO, Yang LI, Kaifeng HAN, Ying DU, Guangxu ZHU

“…Methods: Using the federated learning network architecture, this paper analyzes the resource allocation and user scheduling schemes: 1. …”
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Short-term scheduling optimization of battery electric buses in the context of sustainable energy resources under uncertainty by Muhammad Ahmad Iqbal, Ismail I. Almaraj

“…The primary goal is to allocate BEB to the best CSs while focusing on increasing overall profit by serving the grid and passenger needs effectively. …”
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Reducing hydrogen consumption in hybrid electric vehicles using aquila optimization algorithm by A.M. Nassef, H.E. Ghadbane, E.T. Sayed, H. Rezk

“…Implementing an energy management strategy to enhance the performance of fuel cell electric vehicles by optimizing the allocation of power among different energy sources is a crucial engineering problem. …”
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Routing algorithm for heterogeneous computing force requests based on computing first network by ZHANG Gang, LI Xi

“…The experiment verifies that algorithm has been optimized by an average of 8.85%, 15.51%, and 17.03% in terms of transmission success rate, convergence delay ratio, and load balancing compared to the IGAGCT algorithm and RBDQN algorithm, and 10.41%, 16.5%, and 16.81%, respectively from three aspects: heterogeneous request success rate, algorithms convergence delay rate, and load error rate of computing first networks.…”
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Research on energy-efficient physical-layer secure transmission mechanism in decode-and-forward cooperative networks by Dong WANG, Yong-cheng LI, Bo BAI, Man-xi WANG

“…The maximization problem of secure energy efficiency (EE) in decode-and-forward relay networks was investigated considering the power and energy constraints in physical-layer secure transmission.An iterative algorithm for power allocation was proposed based on fractional programming and DC (difference of convex functions) programming.This algorithm jointly allocated power for source and relay nodes to achieve energy-efficient secure transmission,subject to the peak power constraint of each node and the minimum secrecy rate requirement of the system.Simulation results demonstrate that the propose algorithm can improve the secure EE significantly compared with the conventional secrecy rate maximization strategy.…”
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Comparative Performance Evaluation of Scheduling Algorithms using 5G‑Air‑Simulator by Moaath Saleh Abdulrahman, Buthaina Mosa Omran

“…With the spread of new applications, efficient scheduling algorithms are mandatory to handle the allocation of the limited spectrum resources to various types of traffic and to guarantee the requirements of the quality of service and the quality of experience informed by users. …”
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A New Multiobjective A∗ Algorithm With Time Window Applied to Large Airports by Bosheng Ba, Ye Yu, Ruixin Wang, Jean-Baptiste Gotteland, Yunqi Gao

“…This study proposes a multiobjective A∗ algorithm with time windows which takes into account the allocation of resources on airport taxiways and introduces factors such as turning angles, dynamic turning speeds, and dynamic characteristics of the ground operations. …”
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The Evaluation of Online Education Course Performance Using Decision Tree Mining Algorithm by Yongxian Yang

“…The decision tree mining algorithm can classify the data, grasp the teaching process of the teacher, and analyze the overall performance of the students, so as to realize the dynamic management of the educational administration and help the educational administration personnel to make the right decision, with more reasonable allocation of resources. …”
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Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty by M. Siva Ramkumar, Jaganathan Subramani, M. Sivaramkrishnan, Arunkumar Munimathan, Goh Kah Ong Michael, Mohammad Mukhtar Alam

“…Abstract Research in industrial grid energy management is essential due to increasing energy demands, rising costs, and the integration of renewable sources. Efficient energy management can reduce operational costs, enhance grid stability, and optimize resource allocation. …”
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Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement by Jing Wang, Yanru Wang, Dan Yuan, Yuxiang Que, Weichao Huang, Yuan Wei

“…At the same time, in order to adapt to the changes in the surrounding environment of the object and establish good discrimination with similar objects, this paper proposes a dynamic mask strategy to optimize the attention allocation mechanism and finally employs an object template update mechanism to improve the adaptability of the model by comparing the spatio-temporal information of successive frames to update the object template in time, further enhancing its tracking performance in complex scenes. …”
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Adaptive Resource Optimization for IoT-Enabled Disaster-Resilient Non-Terrestrial Networks using Deep Reinforcement Learning by Fathe Jeribi, R. John Martin

“…Additionally, unmanned aerial vehicles (UAVs) are used to provide optimal coverage for IoT nodes in disaster areas, with coverage optimization achieved through the non-linear smooth optimization (NLSO) algorithm. Furthermore, we develop the multi-variable double deep reinforcement learning (MVD-DRL) framework for resource management, which addresses congestion and transmission power of IoT nodes to enhance network performance by maximize successful connections. …”
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