Efficient planning and optimization of integrated energy system considering double uncertainty of source and load by LI Aiwu, DI Liang, DONG Jie, TIAN Zhe, WANG Yi, NIU Jide

“…The design process of integrated energy system will face the uncertainty of renewable energy generation and energy demand, and the risk of sub-optimal decision will be introduced when the deterministic method is used for design. In this paper, an efficient planning and optimization model for integrated energy systems considering dual source-load uncertainty was proposed. …”
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Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid by Xin Shen, Yijing Zhang, Jiahao Li, Yitao Zhao, Jianlin Tang, Bin Qian, Xiaoming Lin

“…In addition, a novel sort replay actor critic technique is proposed, leveraging the deep deterministic policy gradient algorithm and sort experience replay to enhance control efficiency and robustness. …”
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Energy efficient control of indoor environments under time-varying multi-parameter uncertainty by Jianhao ZHAO, Hua SONG, Xinyuan NAN, Xin CAI

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Efficient Robot Manipulation via Reinforcement Learning with Dynamic Movement Primitives-Based Policy by Shangde Li, Wenjun Huang, Chenyang Miao, Kun Xu, Yidong Chen, Tianfu Sun, Yunduan Cui

“…The proposed method naturally integrates a DMP-based policy into the actor–critic framework of the traditional RL approach Deep Deterministic Policy Gradient (DDPG) and derives the corresponding update formulas to learn the networks that properly decide the parameters of DMPs. …”
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Comparing variable neighbourhood search algorithms for the direct aperture optimisation in radiotherapy by Mauricio Moyano, Keiny Meza-Vasquez, Gonzalo Tello-Valenzuela, Nicolle Ojeda-Ortega, Carolina Lagos, Guillermo Cabrera-Guerrero

“…The DAO problem seeks to generate a set of deliverable aperture configurations and a corresponding set of radiation intensities. This method accounts for physical and delivery time limitations, facilitating the creation of clinically appropriate treatment programs. …”
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Digital Twin and TD3-Enabled Optimization of xEV Energy Management in Vehicle-to-Grid Networks by Irum Saba, Abdulraheem H. Alobaidi, Sultan Alghamdi, Muhammad Tariq

“…This paper addresses these challenges by proposing an advanced ESS framework that integrates digital twin (DT) technology with the twin-delayed deep deterministic policy gradient (TD3) algorithm, a state-of-the-art reinforcement learning method derived from the deep deterministic policy gradient (DDPG). …”
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Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis by Ben Gabrielson, Hanlu Yang, Trung Vu, Vince Calhoun, Tulay Adali

“…These methods’ efficiencies are often due to their randomized natures; however, deterministic methods can provide better approximations, and can perform feature selection, highlighting a meaningful subset that well-represents the entire tensor. …”
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Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning by Zheng-Kai Ding, Qi-Ming Fu, Jian-Ping Chen, Hong-Jie Wu, You Lu, Fu-Yuan Hu

“…This method can minimise energy consumption while satisfying occupants' thermal comfort. …”
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A novel approach to pseudorandom number generation using Hamiltonian conservative chaotic systems by Vinod Patidar, Tanu Singh

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Decentralized Voltage and Var Control of Active Distribution Network Based on Parameter-Sharing Deep Reinforcement Learning by Binqiao Zhang, Changlin He

“…This work introduces a Parameter Sharing - twin-delay deep deterministic policy gradient (PS-TD3) method for carrying out decentralized voltage and var control in active distribution networks. …”
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Tracking Control of CSTRs Based on Improved OU Noise and the TD3 Algorithm by Hongyan Shi, Xiaofei Wu, Guogang Wang

“…To address this challenge, this study proposes a novel deep reinforcement learning (DRL) approach that integrates an improved Ornstein-Uhlenbeck (IOU) noise into the twin delayed deep deterministic policy gradient (TD3) algorithm. This method is applied to the tracking control of continuous stirred tank reactors (CSTRs). …”
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RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning by Yang Jing, Li Weiya

“…Traditional path planning methods such as genetic algorithms, Dijkstra's algorithm, and Floyd's algorithm typically rely on deterministic search strategies, which can lead to local optima and lack global search capabilities in dynamic settings. …”
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Hybrid lion and exponential PSO-based metaheuristic clustering approach for efficient dynamic data stream management by M. Ananthi, K. Valarmathi, A. Ramathilagam, R. Praveen

“…It adopted different methods of stochastic optimization and deterministic clustering techniques for centring the clusters in an optimal manner. …”
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Spectral clustering-based energy-efficient resource allocation algorithm in heterogeneous cellular ultra-dense network by Xue WANG, Jing LIU, Jiani SUN, Jizhen ZHANG, Zhihong QIAN

“…In order to solve problems of high power consumption, spectrum shortage and low energy efficiency in the ultra-intensive 5G mobile communication scenario, a resource allocation algorithm based on the maximum energy efficiency for the two-layer heterogeneous cellular non-orthogonal multiple access network was proposed.The original NP-hard optimization problem on the downlink communication link of ultra-dense scene was divided into two subproblem, such as frequency resource allocation and power allocation, which became a deterministic constraint optimization problem.The frequency resource allocation scheme of different user groups was obtained by using base station clustering based on the improved k-means algorithm and users grouping based on spectral clustering algorithm.The fraction of energy efficiency optimization was transformed into a solvable continuous convex optimization problem and power distribution was realized by Dinkelbach method, and the Lagrange multiplier iterative algorithm, respectively.Jointly optimize system energy efficiency in terms of base station clustering, user grouping, resource block allocation and power allocation, which minimized the inter-cluster interference and intra-cluster interference of the base station efficiently.The simulation results show that the proposed algorithm is better on energy efficiency and computational efficiency compared with existing algorithms.…”
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Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance by Ali El Romeh, Seyedali Mirjalili

“…AMET combines the deterministic structure of Coordinated Multi-Robot Exploration (CME) with the adaptive search capabilities of the Multi-Objective Salp Swarm Algorithm (MSSA) to achieve a balanced trade-off between exploration efficiency and mapping accuracy. …”
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EADRL: Efficiency-aware adaptive deep reinforcement learning for dynamic task scheduling in edge-cloud environments by J. Anand, B. Karthikeyan

“…Experimental evaluations demonstrate that EADRL achieves significant improvements over existing benchmark methods, including DRL-based approaches such as Double DQN (DDQN), Deep Deterministic Policy Gradient (DDPG), and Server Real-Time Performance Deep Reinforcement Learning (SRP-DRL), as well as heuristic-based methods like Best-Fit, Random, and Earliest Idle Time First (EITF). …”
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Disrupted topologic efficiency of white matter structural connectome in migraine: a graph-based connectomics study by Yanliang Mei, Dong Qiu, Zhonghua Xiong, Xiaoshuang Li, Peng Zhang, Mantian Zhang, Xue Zhang, Yaqing Zhang, Xueying Yu, Zhaoli Ge, Zhe Wang, Binbin Sui, Yonggang Wang, Hefei Tang

“…However, there is a paucity of research employing graph theory analysis to explore changes in the whole brain structural networks in patients with CM and EM. Methods The individual structural brain connectome of 60 patients with CM, 34 patients with EM, and 39 healthy control participants were constructed by using deterministic diffusion-tensor tractography. …”
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Linearized MILP Model With Improved Soft Actor-Critic Algorithm for Dynamic and Efficient Active Distribution Network Planning by Jinlin Liao, Guilian Wu, Jia Lin

“…Finally, the feasibility and efficiency of the proposed method are verified by comparing the proposed MILP model and the traditional nonlinear planning model for distributed networks through multi-scenario case analysis. …”
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Highly durable and energy‐efficient probabilistic bits based on h‐BN/SnS2 interface for integer factorization by Joon‐Kyu Han, Jun‐Young Park, Shania Rehman, Muhammad Farooq Khan, Moon‐Seok Kim, Sungho Kim

“…Recent advancements in probabilistic computing approaches have demonstrated significant potential for addressing these problems more efficiently than conventional deterministic computing methods. …”
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Optimizing Energy Consumption and Latency in IoT Through Edge Computing in Air–Ground Integrated Network With Deep Reinforcement Learning by Vitou That, Kimchheang Chhea, Jung-Ryun Lee

“…Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.…”
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