Efficient preparation of the AKLT State with Measurement-based Imaginary Time Evolution by Tianqi Chen, Tim Byrnes

“…In this article, we propose a method to prepare the ground state of the Affleck-Lieb-Kennedy-Tasaki (AKLT) model deterministically using a measurement-based imaginary time evolution (MITE) approach. …”
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Maximizing theoretical and practical storage capacity in single-layer feedforward neural networks by Zane Z. Chou, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller, Jean-Marie C. Bouteiller

“…This work offers a foundational framework for maximizing storage efficiency in neural network systems and supports the development of data-efficient, sustainable AI.…”
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Optimization research on UAV semantic communication system based on SVD-MADRL by Yibo Yang, Yahe Tan, Liu Liu

“…The effectiveness of the two models designed for research is relatively high, both superior to traditional methods, providing a theoretical basis for optimizing the overall performance of unmanned aerial vehicle communication. …”
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Research of efficiency of the algorithms for finding solutions based on the use of functional semantic networks by Mironova M.N.

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An Efficient Procedure for Inserting Buffers to Generate Robust Berth Plans in Container Terminals by Yangcan Wu, Lixin Miao

“…Such a method is highly versatile and compatible with various solutions to berth allocation problem with different objectives. …”
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Evaluating the Efficiency of Nature-Inspired Algorithms for Finite Element Optimization in the ANSYS Environment by Antonino Cirello, Tommaso Ingrassia, Antonio Mancuso, Giuseppe Marannano, Agostino Igor Mirulla, Vito Ricotta

“…Nature-inspired metaheuristics have proven effective for addressing complex structural optimization challenges where traditional deterministic or gradient-based methods often fall short. …”
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Some research results by risk-inform approaches for npp safety and operational efficiency by Yu. А. Komarov

“…In the article the per-spective problems of further development risk-oriented approach (ROA) for the grounding and realization of measures on increase of safety and operational efficiency of NPP are considered. Unlike the traditional approach for the ROA, mean due the definition of probabilistic and/or deterministic methods of risk parameters, as criterion functions essence and the measure of the estimation are defined by the solution of specific problem in nuclear field. …”
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Efficient Software Development Effort Estimation Approaches for Improving Scalability in the Training Phase by Ho Le Thi Kim Nhung, Petr Silhavy, Radek Silhavy

“…While clustering can address this complexity, standard methods often rely on random initial centers, leading to inconsistent and less precise results. …”
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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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Optimization Research on Energy Management Strategies and Powertrain Parameters for Plug-In Hybrid Electric Buses by Lufeng Wang, Juanying Zhou, Jianyou Zhao

“…Subsequent to this, a combined multi-layer powertrain optimization method based on Genetic Algorithm-Optimal Adaptive Control of Motor Efficiency-Particle Swarm Optimization (GOP) is 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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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

“…Energy efficient control of thermal comfort has been already an important part of residential heating, ventilation, and air conditioning (HVAC) systems. …”
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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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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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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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A DRL-based optimization method for microgrid operation by ZENG Lei, DING Quan, CHEN Xiaoyu, YUE Xianya

“…Third， a penalty term for high-proportion erroneous actions is incorporated into the reward function to constrain the output of each device within a reasonable range， mitigating the risk of insufficient safety guarantees inherent in reinforcement learning methods. Finally， simulation results demonstrate that， compared to the deep deterministic policy gradient （DDPG） algorithm， the proposed method achieves superior economic efficiency and stability， with economic costs closer to those of ideal deterministic optimization methods.…”
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