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Efficient Computation of Collatz Sequence Stopping Times: A Novel algorithmic Approach

IEEE ACCESS 2025年 13卷 41210-41220页

作者： Getachew, Eyob Solomon Assefa, Beakal Gizachew Addis Ababa Inst Technol Sch Informat Technol & Engn Addis Ababa 1000 Ethiopia

The Collatz conjecture, which posits that any positive integer will eventually reach 1 through a specific iterative process, is a classic unsolved problem in mathematics. This research focuses on designing an efficient algorithm to compute the stopping time of numbers in the Collatz sequence, achieving significant computational improvements. By leveraging structural patterns in the Collatz tree, the proposed algorithm minimizes redundant operations and optimizes computational steps. Unlike prior methods, it efficiently handles extremely large numbers without requiring advanced techniques such as memoization or parallelization. Experimental evaluations confirm computational efficiency improvements of approximately 28% over state-of-the-art methods. These findings underscore the algorithm's scalability and robustness, providing a foundation for future large-scale verification of the conjecture and potential applications in computational mathematics.

关键词： Computational efficiency Encryption Approximation algorithms Visualization Probabilistic logic optimization Complexity theory Watermarking Time complexity Testing algorithm optimization bitwise operations Collatz conjecture Collatz tree computational mathematics high-performance algorithms logarithmic complexity memoization parallel computing stopping time computational complexity sequence analysis and large-scale verification

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Optimizing the compliance third-party supervision workflow of involved enterprises using artificial intelligence ant colony algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-14页

作者： Chen, Danqi Jia, Weichen Chen, Qi Chen, Jianing Li, Zhi NingboTech Univ Sch Media & Law Ningbo 315000 Peoples R China

This article optimized the compliance third-party supervision workflow of the involved enterprises based on the artificial intelligence ant colony optimization (ACO) algorithm. The basic principles and application advantages of ACO were introduced, and a heuristic information matrix was defined using ACO to optimize the data collection and analysis stage of the compliance third-party supervision workflow. During the experimental phase, a feasibility analysis was conducted on the optimization of third-party supervision workflows for compliance by ACO involved enterprises through simulation experiments. The experiments were evaluated from four aspects: data quality, model performance, scheme effectiveness, and supervision effectiveness. Among the metrics data for the ACO-optimized test set were 0.03 and 0.025 for MSE (Mean Square Error) and Gamma, 0.8, 0.78, 0.79, and 0.88 for Accuracy, Recall, F1 Score, and AUC-ROC (Area Under the Curve-Receiver Operating Characteristic), and 0.28, 0.4, 0.88, and 0.12 for CER (Cost-Effectiveness Ratio), NPV (Net Present Value), SCR (Supervision Coverage Rate), and CRC (Compliance Rate Change), respectively. The experimental results showed that, in terms of data quality, model performance, scheme effectiveness, and supervision effectiveness, the evaluation indicators of the compliance third-party supervision workflow of the involved enterprises optimized using ACO were superior to those without ACO optimization.

关键词： Ant colony optimization Artificial intelligence Third-party supervision Involved enterprises algorithm optimization

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Real-time performance evaluation and optimization of electrical substation equipment inspection algorithm based on distributed computing

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 1878-1887页

作者： Zhang, Guoliang Du, Zexu Zhang, Yi Chen, Jiangqi Zhang, Xi State Grid Smart Grid Res Inst Co Ltd State Grid Lab Grid Adv Comp & Applicat Beijing 102209 Peoples R China

Substation equipment inspection faces problems such as large amounts of data, various information types, complex analysis and calculations, and low resource integration. Distributed computing system uses networks to realize computer connectivity and collaboration, achieving in-depth sharing of computing resources, software resources, information resources, and communication resources. Therefore, firstly, this article introduces segmentation method for load balancing and recommendation calculation based on path search;secondly, an algorithm model for substation equipment inspection based on distributed computing is constructed;then, the article analyzes and discusses multi-core cluster task allocation, heterogeneous task scheduling framework, fragmented transmission mechanism, and vertical expansion mechanism;finally, case analysis and performance evaluation are carried out. Distributed computing has significant advantages in the inspection algorithm of power substation equipment. It can efficiently process large-scale data, improve computing power, ensure data redundancy and fault tolerance, provide good scalability, optimize resource utilization, and support remote data processing. By integrating multi-source data, distributed computing improves the accuracy and comprehensiveness of the algorithm, enhances security, supports complex algorithms such as machine learning and deep learning, and realizes real-time monitoring and early warning. The results show that the proposed algorithm can improve the efficiency of distributed parallel computing and analysis of substation equipment inspections and can simulate, evaluate, and screen substation equipment failure scenarios that may cause serious consequences within an effective time.

关键词： substation equipment inspection distributed computing performance evaluation algorithm optimization

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optimization of parallel random forest algorithm based on distance weight

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1951-1963页

作者： Wang, Qinge Chen, Huihua Cent South Univ Sch Civil Engn Changsha Peoples R China

In order to overcome the problems of long execution time and low parallelism of existing parallel random forest algorithms, an optimization method for parallel random forest algorithm based on distance weights is proposed. The concept of distance weights is introduced to optimize the algorithm. Firstly, the training sample data are extracted from the original data set by random selection. Based on the extracted results, a single decision tree is constructed. The single decision tree is grouped together according to different grouping methods to form a random forest. The distance weights of the training sample data set are calculated, and then the weighted optimization of the random forest model is realized. The experimental results show that the execution time of the parallel random forest algorithm after optimization is 110 000 ms less than that before optimization, and the operation efficiency of the algorithm is greatly improved, which effectively solves the problems existing in the traditional random forest algorithm.

关键词： Distance weights parallel algorithm random forest algorithm algorithm optimization

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Feature Selection for Cross-Scene Hyperspectral Image Classification via Improved Ant Colony optimization algorithm

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IEEE ACCESS 2022年 10卷 102992-103012页

作者： Yu, Youhua Xie, Xiaolan Tang, Yigang Liu, Yarong Guilin Univ Technol Coll Informat Sci & Engn Guilin 541000 Peoples R China Guangxi Key Lab Embedded Technol & Intelligent Guilin 541000 Peoples R China

Hyperspectral images (HSIs) generally contain a large amount of spectral bands (features), and the redundant information in them will cause the Hughes phenomenon in the classification process. And feature extraction and feature selection are the two main existing methods to effectively reduce the redundancy of spectral information in the field of HSIs classification. Compared with feature extraction methods, feature selection methods can preserve most of the features of the original HSIs data without losing their valuable details. However, most existing feature selection methods based on single scene (domain) perform poorly in some scenes (domains) with insufficient labeled samples. Therefore, how to adopt an efficient feature selection method to select the optimal feature subsets of source scene and target scene and use the sample information of source scene to assist in the classification of target scene so as to improve the classification accuracy of images in the target scene as much as possible is still very challenging. In order to solve the above problem, this paper proposes a new cross-scene algorithm: Improved Ant Colony optimization algorithm-Based Cross-Scene Feature Selection algorithm (IMACO-CSFS). In order to obtain more accurate feature subsets of the two scenes, IMACO-CSFS proposes a priority sorting-based ant colony strategy to make the subsequent search process focus on the global optimal solution (optimal feature subset) found in the previous iteration. In addition, in order to further accelerate the convergence speed of the global optimal solution, an ant colony strategy based on elite ants is proposed in IMACO-CSFS to more efficiently obtain the optimal feature subsets of the two scenes for training the classifier. Furthermore, this paper simultaneously considers overall classification accuracies of the optimal feature subsets for both scenes and dynamically adjusts their scale to ensure the consistency of the selected features between the two s

关键词： Feature extraction Ant colony optimization Support vector machines Hyperspectral imaging optimization Convergence Classification algorithms Image analysis Cross-scene IMACO-FS hyperspectral image cross-scene feature selection algorithm optimization

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algorithm/Hardware Co-optimization for Sparsity-Aware SpMM Acceleration of GNNs

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2023年第12期42卷 4763-4776页

作者： Gao, Yingxue Gong, Lei Wang, Chao Wang, Teng Li, Xi Zhou, Xuehai Univ Sci & Technol China Dept Comp Sci Hefei 230026 Anhui Peoples R China

In recent years, graph neural networks (GNNs) have achieved impressive performance in various application fields by extracting information from graph-structured data. It contains extensive feature aggregation operations and has become a performance bottleneck, which can be abstracted as a specialized sparse-dense matrix multiplication (SpMM) operation. Previous works have leveraged the inner product or outer product to accelerate the feature aggregation process. However, inefficient execution leads to extremely unbalanced workloads and extensive intermediate data, hampering the performance of previous processors. So in this article, we demonstrate an algorithm/hardware co-optimization chance to enhance SpMM acceleration for GNNs. First, the algorithm part develops a dataflow-efficient SpMM algorithm that integrates three optimization methods to mitigate computation and memory access inefficiencies. Specifically, 1) the proposed equal-value partition method achieves fine-grained data partition and enables load balancing during data movement;2) after observing the vertex aggregation phenomenon, a vertex-clustering optimization method is presented to enable significant data locality;and 3) the adaptive dataflow based on Gustavson's algorithm is further implemented to enable the efficient distribution of sparse elements and improves computing resource utilization. Then, the hardware part features the proposed SpMM algorithm and customizes SDMA, a flexible and efficient accelerator to boost SpMM acceleration, which follows the adaptive dataflow to eliminate sparsity and explore the regular parallelism dimension. Finally, we prototype SDMA on the Xilinx Alveo U280 FPGA accelerator card. The results demonstrate that SDMA achieves 5.68x-14.68x energy efficiency over the previous GPU implementations deployed on the Nvidia GTX 1080Ti and 1.32x higher throughput over the state-of-the-art FPGA prototype.

关键词： algorithm optimization graph neural networks (GNNs) hardware acceleration sparse-dense matrix-multiplication (SpMM)

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An Integrated Solution to Improve the Environment Awareness and Path Planning Efficiency of Smart Wheelchairs 5th

An Integrated Solution to Improve the Environment Awareness ...

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5th China Intelligent Robotics Academic Conference

作者： Liu, Chunfang Bin Lu Shang, Yan Beijing Univ Technol Beijing 10 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Given the important role and significance of smart wheelchairs in helping elderly peoplemove and improving their navigation ability in complex environments with dynamic obstacles, this paper studies a comprehensive method based on laser vision mapping and navigation to improve the environment perception and navigation ability of smart wheelchairs. We use V-LOAM for environmental mapping and mix visual and liDAR data for high-precision map construction. For global path planning, we improve the A* algorithm, introduce heuristic function optimization and path smoothing, and improve the planning efficiency and path quality. For local obstacle avoidance, the dynamic window algorithm (DWA) is used to respond to environmental changes in real time to ensure safety. The results of the experiment demonstrate that this approach excels in constructing maps, planning global paths, and avoiding local obstacles. Furthermore, it enhances the navigation capabilities of intelligent wheelchairs within dynamic environments. In the future, we will further optimize and expand the application scenario to verify its wide applicability and robustness.

关键词： Obstacle Escaping Path Planning algorithm optimization

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Analysis of Network Coverage optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm algorithm

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IEEE ACCESS 2020年 8卷 42864-42876页

作者： Feng, Yingying Zhao, Shasha Liu, Hui Fuyang Normal Univ Coll Informat Engn Fuyang 236041 Peoples R China

There is a certain energy loss in the process of wireless sensor network information collection. Moreover, the current network protocols and network coverage methods are not sufficient to effectively reduce system energy consumption. In order to improve the operating efficiency and service life of wireless sensor networks, this study analyzes the classic LEACH protocol, summarizes the advantages and disadvantages, and proposes a targeted clustering method based on the K-means algorithm. At the same time, in order to maximize the network coverage and minimize the energy consumption on the basis of ensuring the quality of service, a wireless sensor network coverage optimization method based on an improved artificial fish swarm algorithm was proposed. In addition, a controlled experiment is designed to analyze the effectiveness and practical effects of the proposed algorithm. The experimental results show that the method proposed in this paper has certain advantages over traditional methods and can provide theoretical references for subsequent related research.

关键词： Feedback K-means clustering artificial fish swarm algorithm network coverage algorithm optimization

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An efficient algorithm for CCHP system sizing and an operational optimization model based on LP

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2015年 25卷 189-196页

作者： Qin, Chaokui Tang, Jixu Zhang, Yang TONGJI Univ Sch Mech Engn Shanghai 201804 Peoples R China

The economic benefit gained from a combined cooling heating and power (CCHP) system depends on many factors. It is essential to optimize the system configuration and operational scheme as early in the design stage as possible. In this paper, a sizing optimization model (SOM) focused on the minimum annual total cost (ATC) and an operational optimization model (OOM) focused on the minimum annual energy charge (AEC) were established. The former was used to determine the optimal capacity of a CCHP system, whereas the latter was used to provide the optimal schemes. Both the SOM and OOM contain large numbers of hourly linear programming (LP) models, and the solution processes of both models proved to be quite time-consuming when the conventional iterative method was used. In this study, a piecewise elimination method was proposed for hourly LP models, and a graphical method in which the iteration was converted to an algebraic matrix was introduced to greatly improve the computational efficiency. The results demonstrated that the calculation time was reduced from 84.496 s for the conventional solution to 0.017 s for the graphical method in the OOM. The CCHP system SOM contains an OOM, and the calculation time was only 0.41 s when using the graphical method, whereas the conventional iterative method took 2367 s. The impact of gas price upon the optimal operational schemes and the equipment capacity was discussed for a building application in Shanghai. The improved efficiency of the calculation method could be helpful to compare different schemes and to perform sensitivity analyses. (C) 2015 Elsevier B.V. All rights reserved.

关键词： CCHP Linear programming Graphical method algorithm optimization Sizing optimization Operational scheme optimization

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optimization of adaptive media player algorithm for smart TV terminal

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期37卷 5925-5934页

作者： Qi, Bo Harbin Normal Univ Sch Media Harbin 150080 Heilongjiang Peoples R China

The emergence of resource conflicts and overload control problems during the playback of smart TV terminals has brought many obstacles to the operation of smart TV terminals, which has seriously affected the user experience of smart TV terminal users. In this regard, the adaptive media playback algorithm is optimized for smart TV terminals. This method performs dynamic priority preemptive scheduling on exclusive resources according to resource characteristics and application priorities to optimize resource allocation and improve media playback. The feedback control algorithm is used to perform QoS scheduling on shared resources until QoS proportional fairness is achieved, and QoS proportional compression is used to eliminate resource overload. Finally, a DASH server on Apache and implements an analog DASH client using Python are built. In order to verify the performance of the algorithm, the research results show that the adaptive media playback algorithm has the overload control capability, which only solves the resource conflict and improves the response performance under heavy load of the system, and the algorithm consumes only 4.5% of the overall system QoS. Compared with the existing methods, it is about 30% lower, which is more suitable for resource scheduling of smart TV terminals. The research in this paper shows that the adoption of QoS scheduling mechanism contributes to the optimization of media playback resources and allocation ratio, thus making the playback process of smart TV terminals, which provides a reference for the optimization of smart TV terminal playback.

关键词： Smart TV terminal media playback algorithm optimization

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