Size optimization method of the Watt-II six-bar mechanism based on particle swarm optimization by H. Ye, H. Ye, Z. Niu, Z. Niu, D. Han, D. Han, X. Su, X. Su, W. Chen, W. Chen, G. Tu, G. Tu, T. Zhu, T. Zhu

“…<p>Aiming at the difficult problem of comprehensive scale design of the six-bar mechanism in engineering practice, kinematic and dynamic analysis and modeling of the Watt-II six-bar mechanism were carried out and combined with the particle swarm optimization (PSO) algorithm, the size optimization model of the Watt-II six-bar mechanism was established, and the size optimization of the Watt-II six-bar mechanism was completed. …”
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An empirical study of the naïve REINFORCE algorithm for predictive maintenance by Rajesh Siraskar, Satish Kumar, Shruti Patil, Arunkumar Bongale, Ketan Kotecha, Ambarish Kulkarni

“…Abstract Reinforcement Learning (RL) is a biologically inspired, autonomous machine learning method. RL algorithms can help generate optimal predictive maintenance (PdM) policies for complex industrial systems. …”
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FM‐YOLOv8:Lightweight gesture recognition algorithm by Fanghai Li, Xitai Na, Jinshuo Shi, Qingbin Sun

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Provident garbage collection algorithm for SSD storage system by Xuezhen TU, Zhenjiang Huang, Zhengguang CHEN

“…A predictive based proactive garbage collection algorithm was proposed.Firstly,the data was separated according to different heat factors,then the upper and lower predictions were performed on the number of different types of page allocation requests (PAR) that would be reached in the future.While satisfying the page allocation request lower prediction,the PAR upper prediction requirement was maximally satisfied,the WA problem was optimized,and invalid effective data migration was reduced,thereby maximizing the garbage collection utility.A mathematical model was defined for this problem,and an algorithm for obtaining the approximate optimal solution was given.The applicable scenario of the model was analyzed.The practical results show that the algorithm can obtain the maximum benefit and can significantly improve the performance of SSD and reduce the cost.…”
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Application of Genetic Algorithms in Nonlinear Heat Conduction Problems by Muhammad Bilal Kadri, Waqar A. Khan

“…Genetic algorithms are employed to optimize dimensionless temperature in nonlinear heat conduction problems. …”
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Information entropy based match field cutting algorithm by Peng-hao SUN, Ju-long LAN, Shao-jun ZHANG, Jun-fei LI

“…With the increasing diversity of network functions,packet classification had a higher demand on the number of match fields and depth of match table,which placed a severe burden on the storage capacity of hardware.To ensure the efficiency of matching process while at the same time improve the usage of storage devices,an information entropy based cutting algorithm on match fields was proposed.By the analysis on the redundancy of match fields and distribution pattern in a rule set,a match field cutting model was proposed.With the mapping of matching process to the process of entropy reduction,the complexity of optimal match field cutting was reduced from NP-hard to linear complexity.Experiment results show that compared to existing schemes,this scheme can need 40% less TCAM storage space,and on the other side,with the growing of table size,the time complexity of this algorithm is also far less than other algorithms.…”
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A practical guide to optimizing industrial thermal spraying through comparative multi-objective optimization by Wolfgang Rannetbauer, Simon Hubmer, Carina Hambrock, Ronny Ramlau

“…This paper applies three multi-objective optimization algorithms to determine optimal process parameters for high-velocity oxygen fuel (HVOF) thermal spraying. …”
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Algorithms for Railway Embedded Control Devices for Safety Manoeuvres by Beinaroviča Anna, Gorobetz Mikhail, Alps Ivars

“…This study is dedicated to solve manoeuvres making task while working on the station with no marshalling hump. …”
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Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems by Martins Akugbe Arasomwan, Aderemi Oluyinka Adewumi

“…A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. …”
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EULER AND EXPONENTIAL ALGORITHM IN VISCOELASTIC ANALYSES OF LAMINATED GLASS by Jaroslav Schmidt, Alena Zemanová

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A Vehicle Path Planning Algorithm: QDDG-RRT by Ruixin Zhang, Qing Xu, Kai Sun, Yi Liu, Ximming Zhu, Guo Zhang, Xiang Cheng

“…The Rapidly-exploring Random Tree (RRT) algorithm, while widely used, suffers from slow search speeds, numerous inflection points, and redundant operations. …”
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Routing and Scheduling in Time-Sensitive Networking by Evolutionary Algorithms by Zengkai Wang, Weizhi Liao, Xiaoyun Xia, Zijia Wang, Yaolong Duan

“…In this paper, we propose a novel routing and scheduling approach for TSN based on evolutionary algorithm. Specifically, we introduce a flow grouping method that leverages the greatest common divisor to optimize flow aggregation. …”
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Trajectory Compression Algorithm via Geospatial Background Knowledge by Yanqi Fang, Xinxin Sun, Yuanqiang Zhang, Jumei Zhou, Hongxiang Feng

“…At the same compression ratio, the proposed algorithm reduces the average water depth error by approximately 99.1% compared to the Douglas–Peucker (DP) algorithm, while also addressing the common problem of compressed trajectories potentially intersecting with obstacles in traditional trajectory compression methods.…”
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Road damage detection based on improved YOLO algorithm by Luyao Ma, Ming Chen

“…The dual-branch attention mechanisms enhance feature representation in channel and spatial dimensions, while the GIoU loss optimizes bounding box regression—yielding notable improvements, particularly in small object detection and bounding box localization accuracy. …”
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Multi-Level Particle System Modeling Algorithm with WRF by Julong Chen, Bin Wang, Rundong Gan, Xuepeng Mou, Shiping Yang, Ling Tan

“…Based on the multi-scale mean-shift clustering algorithm, Adaptive Kernel Density Estimation (AKDE) is introduced to map density to bandwidth, achieving adaptive adjustment of clustering bandwidth while reducing computational resources and improving cloud hierarchy. …”
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Feature-based enhanced boosting algorithm for depression detection by Muhammad Sadiq Rohei, Kasturi Dewi Varathan, Shivakumara Palaiahnakote, Nor Badrul Anuar

“…Thus, this study has developed a novel feature-based enhanced boosting algorithm (F-EBA). The proposed model covers two pipelines, the feature engineering pipeline which improves the quality of features by picking up the most relevant features while the classification pipeline uses an ensemble approach designed to boost/elevate the model’s performances. …”
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Feature engineering through two-level genetic algorithm by Aditi Gulati, Armin Felahatpisheh, Camilo E. Valderrama

“…This study highlights the utility of evolutionary algorithms to generate feature sets that enhance the performance of interpretable machine learning models.…”
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Using machine learning algorithms to predict colorectal cancer by Xingjian Xiao, Bo Hong, Kubra Maqsood, Xiaohan Yi, Guoqun Xie, Hailei Zhao, Bo Sun, Jianying Mao, Shiyou Liu, Xianglong Xu

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Dynamic appliance scheduling and energy management in smart homes using adaptive reinforcement learning techniques by Poonam Saroha, Gopal Singh, Umesh Kumar Lilhore, Sarita Simaiya, Monish Khan, Roobaea Alroobaea, Majed Alsafyani, Hamed Alsufyani

“…These dynamics are difficult for traditional systems to handle, but new developments in reinforcement learning and optimization may be able to help. The paper introduces a novel Demand Response (DR) method that integrates a Self-Adaptive Puma Optimizer Algorithm (SAPOA) with a Multi-Objective Deep Q-Network (MO-DQN), improving smart home energy consumption, cost, and user preferences management. …”
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Structure and dynamics analysis of brain functional hypernetworks based on the null models by Chen Cheng, Yao Li, Chunyan Wang, Yanli Yang, Hao Guo

“…Specifically, first, based on the original brain functional hypernetwork, this article proposed the optimized hyper dK-series algorithm with hyperedges to construct null models that preserved the different node attributes and hyperedge attributes of the original brain functional hypernetwork, respectively. …”
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