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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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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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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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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Cumulative Distribution-Based Method for Pavement Performance Modeling by Chenhao Tu, Wenya Ye, Rui Zhang, Xudong Qian, Qun Yang

“…Predicting future pavement conditions accurately and efficiently helps determine the optimal maintenance time, select appropriate measures, and allocate rehabilitation funds effectively. …”
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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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PATH PLANNING AND OBSTACLE AVOIDANCE METHODS FOR AUTONOMOUS MOBILE ROBOTS by Ihor Berizka

“…In the field, these approaches are divided into classical or deterministic and non-deterministic or heuristic methods. …”
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Parameter uncertainties for imperfect surrogate models in the low-noise regime by Thomas D Swinburne, Danny Perez

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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

“…Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. …”
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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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The CADIS-DXTRAN method in NECP-MCX for Radiation Shielding Simulations by Huang Zhanpeng, Cao Liangzhi, He Qingming, Wu Hongchun

“…The results of the labyrinth problem reveal that the CADIS-DXTRAN method is highly efficient with a Figure of Merit (FOM) value 45 times that of DXTRAN and 17 times that of the CADIS method. …”
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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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Assessment of Transmission Reliability Margin: Existing Methods and Challenges and Future Prospects by Uchenna Emmanuel Edeh, Tek Tjing Lie, Md Apel Mahmud

“…A key factor in ATC determination, the Transmission Reliability Margin (TRM), accounts for uncertainties like load variations, generation fluctuations, and network dynamics. The traditional deterministic TRM methods often fail to capture the stochastic nature of modern grids, leading to inaccurate estimations. …”
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Study on incentive mechanism of reward and punishment on work efficiency of PCB welder based on recurrence quantification analysis and electroencephalogram signals by Zhang Qian, Mingyue Guo, Fuwang Wang

“…By comparing the EEG signals of workers with and without reward and punishment incentives (control group vs. experimental group), key features such as deterministic (DET) and average diagonal line length (DLL) are extracted to reveal how incentives regulate work efficiency. …”
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White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction by Qian Zheng, Kefu Guo, Yinghui Meng, Jiaofen Nan, Lin Xu

“…Background. The deterministic fiber tracking method has the advantage of high computational efficiency and good repeatability, making it suitable for the noninvasive estimation of brain structural connectivity in clinical fields. …”
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Research on Vehicle Path Planning Method with Time Windows in Uncertain Environments by Ying Cong, Kai Zhu

“…Two uncertainty models, based on the expected value and chance-constrained criteria, are developed, and their deterministic forms are derived using the inverse distribution method. …”
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Precise robust control method for charging manipulator considering dynamic disturbances by Sihua Liu, Xiaodong Zhu, Kai Kang, Li Wan, Yong Wang

“…Second, a single-joint Markov Decision Process (MDP) model was constructed, and a path dynamic planning method for the manipulator based on the back-propagation Deep Deterministic Policy Gradient (DDPG) was proposed. …”
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Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive control by Farhat Mahmood, Rajesh Govindan, Tareq Al-Ansari

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