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Zeitschriftenartikel zum Thema „Algorithms“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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1

Sun, Yuqin, Songlei Wang, Dongmei Huang, Yuan Sun, Anduo Hu, and Jinzhong Sun. "A multiple hierarchical clustering ensemble algorithm to recognize clusters arbitrarily shaped." Intelligent Data Analysis 26, no. 5 (2022): 1211–28. http://dx.doi.org/10.3233/ida-216112.

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As a research hotspot in ensemble learning, clustering ensemble obtains robust and highly accurate algorithms by integrating multiple basic clustering algorithms. Most of the existing clustering ensemble algorithms take the linear clustering algorithms as the base clusterings. As a typical unsupervised learning technique, clustering algorithms have difficulties properly defining the accuracy of the findings, making it difficult to significantly enhance the performance of the final algorithm. AGglomerative NESting method is used to build base clusters in this article, and an integration strateg

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Moret, Bernard M. E., and Henry D. D. Shapiro. "Algorithms and Experiments: The New (and Old) Methodology." JUCS - Journal of Universal Computer Science 7, no. (5) (2001): 434–46. https://doi.org/10.3217/jucs-007-05-0434.

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The last twenty years have seen enormous progress in the design of algorithms, but little of it has been put into practice. Because many recently developed algorithms are hard to characterize theoretically and have large running_time coefficients, the gap between theory and practice has widened over these years. Experimentation is indispensable in the assessment of heuristics for hard problems, in the characterization of asymptotic behavior of complex algorithms, and in the comparison of competing designs for tractable problems. Implementation, although perhaps not rigorous experimentation, wa

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Gościniak, Ireneusz, and Krzysztof Gdawiec. "Visual Analysis of Dynamics Behaviour of an Iterative Method Depending on Selected Parameters and Modifications." Entropy 22, no. 7 (2020): 734. http://dx.doi.org/10.3390/e22070734.

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There is a huge group of algorithms described in the literature that iteratively find solutions of a given equation. Most of them require tuning. The article presents root-finding algorithms that are based on the Newton–Raphson method which iteratively finds the solutions, and require tuning. The modification of the algorithm implements the best position of particle similarly to the particle swarm optimisation algorithms. The proposed approach allows visualising the impact of the algorithm’s elements on the complex behaviour of the algorithm. Moreover, instead of the standard Picard iteration,

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Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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 One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorit

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Omar, Hoger K., Kamal H. Jihad, and Shalau F. Hussein. "Comparative analysis of the essential CPU scheduling algorithms." Bulletin of Electrical Engineering and Informatics 10, no. 5 (2021): 2742–50. http://dx.doi.org/10.11591/eei.v10i5.2812.

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CPU scheduling algorithms have a significant function in multiprogramming operating systems. When the CPU scheduling is effective a high rate of computation could be done correctly and also the system will maintain in a stable state. As well as, CPU scheduling algorithms are the main service in the operating systems that fulfill the maximum utilization of the CPU. This paper aims to compare the characteristics of the CPU scheduling algorithms towards which one is the best algorithm for gaining a higher CPU utilization. The comparison has been done between ten scheduling algorithms with present

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Hairol Anuar, Siti Haryanti, Zuraida Abal Abas, Norhazwani Mohd Yunos, et al. "Comparison between Louvain and Leiden Algorithm for Network Structure: A Review." Journal of Physics: Conference Series 2129, no. 1 (2021): 012028. http://dx.doi.org/10.1088/1742-6596/2129/1/012028.

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Abstract In the real network, there must be a large and complex network. The solution to understand that kind of network structure is using the community detection algorithms. There are a lot of other algorithms out there to perform community detection. Each of the algorithms has its own advantages and disadvantages with different types and scale of complex network. The Louvain has been experimented that shows bad connected in community and disconnected when running the algorithm iteratively. In this paper, two algorithm based on agglomerative method (Louvain and Leiden) are introduced and rev

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Gangavane, Ms H. N. "A Comparison of ABK-Means Algorithm with Traditional Algorithms." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (2017): 614–21. http://dx.doi.org/10.31142/ijtsrd2197.

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Jay, Kishore Sahani, and Kumar Yadav Arvind. "The Bees Algorithms in Optimization: An Overview." MATHEMATICS EDUCATION LV, no. 3, September 2021 (2021): 20–28. https://doi.org/10.5281/zenodo.7275730.

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            Metaheuristic algorithms have become powerful tools for modeling and optimization. In this article, we provide an overview of Bee Algorithms and their applications. We will briefly introduce algorithms such as bee algorithms, virtual bee algorithm, artificial bee algorithm, bee mating algorithm, etc. We also briefly the main characteristics of these algorithms and outline some recent applications of these algorithms. 

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Belazi, Akram, Héctor Migallón, Daniel Gónzalez-Sánchez, Jorge Gónzalez-García, Antonio Jimeno-Morenilla, and José-Luis Sánchez-Romero. "Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization." Mathematics 10, no. 7 (2022): 1166. http://dx.doi.org/10.3390/math10071166.

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The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficie

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Luan, Yuxuan, Junjiang He, Jingmin Yang, Xiaolong Lan, and Geying Yang. "Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning." International Journal of Intelligent Systems 2023 (November 10, 2023): 1–21. http://dx.doi.org/10.1155/2023/1666735.

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When solving real-world optimization problems, the uniformity of Pareto fronts is an essential strategy in multiobjective optimization problems (MOPs). However, it is a common challenge for many existing multiobjective optimization algorithms due to the skewed distribution of solutions and biases towards specific objective functions. This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address this limitation. Our algorithm utilizes uniform initialization and self-organizing map (SOM) to enhance population diversity and

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Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (2001): 127–46. http://dx.doi.org/10.1162/106365601750190370.

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Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5

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SHAH, I. "DIRECT ALGORITHMS FOR FINDING MINIMAL UNSATISFIABLE SUBSETS IN OVER-CONSTRAINED CSPs." International Journal on Artificial Intelligence Tools 20, no. 01 (2011): 53–91. http://dx.doi.org/10.1142/s0218213011000036.

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In many situations, an explanation of the reasons behind inconsistency in an overconstrained CSP is required. This explanation can be given in terms of minimal unsatisfiable subsets (MUSes) of constraints. This paper presents algorithms for finding minimal unsatisfiable subsets (MUSes) of constraints in overconstrained CSPs with finite domains and binary constraints. The approach followed is to generate subsets in the subset space, test them for consistency and record the inconsistent subsets found. We present three algorithms as variations of this basic approach. Each algorithm generates subs

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Wadhai, Prajwal Ashok. "Algolizer Using ReactJS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30733.

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The Algorithm Visualizer Project is an interactive and educational tool designed to illustrate various algorithms' functionality and efficiency through visual representations. Algorithms are fundamental to computer science, but their abstract nature can be challenging to comprehend. This project aims to bridge that gap by providing a user-friendly interface that visually demonstrates algorithms in action. The visualizer offers a platform where users can select from a range of algorithms, such as sorting (e.g., Bubble Sort, Merge Sort). Each algorithm is showcased step-by-step, allowing users t

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Edam, Salaheldin, Doaa Abubakr, Roaa Rahma, and Roaa Yagoub. "Comparative evaluation of localization range-free algorithms in wireless sensor networks." International Journal of Engineering, Science and Technology 16, no. 2 (2024): 1–10. http://dx.doi.org/10.4314/ijest.v16i2.1.

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Localization in wireless sensor networks is essential not only for determining the location but also for routing, managing density, tracking, and a wide range of other communication network functions. There are two main categories of localization algorithms in wireless sensor networks: range-based algorithms and range-free techniques. Localization based on range-free algorithms has benefits in terms of requiring less hardware and energy, making it cost-efficient. This paper examines the impact of beacon nodes on range-free localization algorithms. The findings indicate that ADLA has intermedia

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A. Baker, Shatha, and Ahmed S. Nori. "Comparison of the Randomness Analysis of the Modified Rectangle Block Cipher and Original algorithm." NTU Journal of Pure Sciences 1, no. 2 (2022): 10–21. http://dx.doi.org/10.56286/ntujps.v1i2.185.

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In recent years, different encryption lightweight algorithms have been suggested to protect the security of data transferred across the IoT network. The symmetric key ciphers play a significant role in the security of devices, in particular block ciphers. the RECTANGLE algorithm amongst the current lightweight algorithms. Rectangle algorithm does have good encryption efficacy but the characteristics of confusion and diffusion that a cipher needed are lacking from this algorithm. Therefore, by improving the algorithm confusion and diffusion properties, we expanded Rectangle utilizing a 3D ciphe

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Wang, Linlin, Xin Zhang, Huilong Zheng, et al. "A Butterfly Algorithm That Combines Chaos Mapping and Fused Particle Swarm Optimization for UAV Path Planning." Drones 8, no. 10 (2024): 576. http://dx.doi.org/10.3390/drones8100576.

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Effective path planning is essential for autonomous drone flight to enhance task efficiency. Many researchers have applied swarm intelligence algorithms to drone path planning. For instance, the traditional Butterfly Optimization Algorithm (BOA) has been used for this purpose. However, traditional BOA faces challenges such as slow convergence and susceptibility to being trapped in local optima. An Improved Butterfly Optimization Algorithm (IBOA) has been developed to identify optimal routes to address these limitations. Initially, ICMIC mapping is utilized to establish the butterfly community,

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Siva Shankar, S., K. Maithili, K. Madhavi, and Yashwant Singh Bisht. "Evaluating Clustering Algorithms: An Analysis using the EDAS Method." E3S Web of Conferences 430 (2023): 01161. http://dx.doi.org/10.1051/e3sconf/202343001161.

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Data clustering is frequently utilized in the early stages of analyzing big data. It enables the examination of massive datasets encompassing diverse types of data, with the aim of revealing undiscovered correlations, concealed patterns, and other valuable information that can be leveraged. The assessment of algorithms designed for handling large-scale data poses a significant research challenge across various fields. Evaluating the performance of different algorithms in processing massive data can yield diverse or even contradictory results, a phenomenon that remains insufficiently explored.

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Toleushova, A. T., D. M. Uypalakova, and A. B. Imansakipova. "SIGNATURE RECOGNITION ALGORITHMS. BEZIER ALGORITHM." Bulletin of Shakarim University. Technical Sciences, no. 3(7) (February 10, 2023): 47–53. http://dx.doi.org/10.53360/2788-7995-2022-1(5)-7.

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This article focuses on improving the human and machine interface, which should ensure efficient processing of data and knowledge in simple, fast and accessible ways. One of the ways to organize it is the introduction of the manuscript (entering text, drawings, drawings, etc.). Handwritten signatures can be considered as handwritten words, but they are more suitable for drawings, because the signer tries to make his signature unique, using not only his first and last names, but also additional graphic elements. Creating a signature is quite simple, although it is impossible to reproduce the re

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Michael, James Bret, and Jeffrey Voas. "Algorithms, Algorithms, Algorithms." Computer 53, no. 11 (2020): 13–15. http://dx.doi.org/10.1109/mc.2020.3016534.

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Alaa, A. Alomoush, Rahman A. Alsewari Abdul, Z. Zamli Kamal, Alrosan Ayat, Alomoush Waleed, and Alissa Khalid. "Enhancing three variants of harmony search algorithm for continuous optimization problems." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2343–49. https://doi.org/10.11591/ijece.v11i3.pp2343-2349.

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Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the

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Wang, Yi, and Kangshun Li. "A Lévy Flight-Inspired Random Walk Algorithm for Continuous Fitness Landscape Analysis." International Journal of Cognitive Informatics and Natural Intelligence 17, no. 1 (2023): 1–18. http://dx.doi.org/10.4018/ijcini.330535.

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Heuristic algorithms are effective methods for solving complex optimization problems. The optimal algorithm selection for a specific optimization problem is a challenging task. Fitness landscape analysis (FLA) is used to understand the optimization problem's characteristics and help select the optimal algorithm. A random walk algorithm is an essential technique for FLA in continuous search space. However, most currently proposed random walk algorithms suffer from unbalanced sampling points. This article proposes a Lévy flight-based random walk (LRW) algorithm to address this problem. The Lévy

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Saeed, Ayesha, Ali Husnain, Anam Zahoor, and Mehmood Gondal. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science and Technology 12, no. 4 (2024): 1–9. http://dx.doi.org/10.55524/ijircst.2024.12.4.1.

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The Graph Coloring Problem (GCP) is a significant optimization challenge widely suitable to solve scheduling problems. Its goal is to specify the minimum colors (k) required to color a graph properly. Due to its NP-completeness, exact algorithms become impractical for graphs exceeding 100 vertices. As a result, approximation algorithms have gained prominence for tackling large-scale instances. In this context, the Cat Swarm algorithm, a novel population-based metaheuristic in the domain of swarm intelligence, has demonstrated promising convergence properties compared to other population-based

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Luo, Mao, Ningning He, Xinyun Wu, Caiquan Xiong, and Wanghao Xu. "A Four-Label-Based Algorithm for Solving Stable Extension Enumeration in Abstract Argumentation Frameworks." Applied Sciences 14, no. 17 (2024): 7656. http://dx.doi.org/10.3390/app14177656.

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In abstract argumentation frameworks, the computation of stable extensions is an important semantic task for evaluating the acceptability of arguments. The current approaches for the computation of stable extensions are typically conducted through methodologies that are either label-based or extension-based. Label-based algorithms operate by assigning labels to each argument, thus reducing the attack relations between arguments to constraint relations among the labels. This paper analyzes the existing two-label and three-label enumeration algorithms for stable extensions through case studies.

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Barbulescu, L., A. E. Howe, L. D. Whitley, and M. Roberts. "Understanding Algorithm Performance on an Oversubscribed Scheduling Application." Journal of Artificial Intelligence Research 27 (December 28, 2006): 577–615. http://dx.doi.org/10.1613/jair.2038.

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The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, we can relate characteristics of the best algorithms to characteristics of the application. In particular, we find that plateaus dominate the search spaces (thus favoring algorithms that make larger changes to solutions) and that some randomization in exploration is critical to good performance (due to the lack of gradient informa

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Birnie, Claire, Kit Chambers, Doug Angus, and Anna L. Stork. "On the importance of benchmarking algorithms under realistic noise conditions." Geophysical Journal International 221, no. 1 (2020): 504–20. http://dx.doi.org/10.1093/gji/ggaa025.

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SUMMARY Testing with synthetic data sets is a vital stage in an algorithm’s development for benchmarking the algorithm’s performance. A common addition to synthetic data sets is White, Gaussian Noise (WGN) which is used to mimic noise that would be present in recorded data sets. The first section of this paper focuses on comparing the effects of WGN and realistic modelled noise on standard microseismic event detection and imaging algorithms using synthetic data sets with recorded noise as a benchmark. The data sets with WGN underperform on the trace-by-trace algorithm while overperforming on a

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Yang, Jianwei, Lingmei Jiang, Shengli Wu, Gongxue Wang, Jian Wang, and Xiaojing Liu. "Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI." Remote Sensing 11, no. 8 (2019): 977. http://dx.doi.org/10.3390/rs11080977.

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Launched on 15 November 2017, China’s FengYun-3D (FY-3D) has taken over prime operational weather service from the aging FengYun-3B (FY-3B). Rather than directly implementing an FY-3B operational snow depth retrieval algorithm on FY-3D, we investigated this and four other well-known snow depth algorithms with respect to regional uncertainties in China. Applicable to various passive microwave sensors, these four snow depth algorithms are the Environmental and Ecological Science Data Centre of Western China (WESTDC) algorithm, the Advanced Microwave Scanning Radiometer for Earth Observing System

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Kang, Tae-Won, Jin-Gu Kang, and Jin-Woo Jung. "A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning." Sensors 21, no. 21 (2021): 7425. http://dx.doi.org/10.3390/s21217425.

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This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is applied to the path created by RRT-connect and six environmental maps were used for the verification. It was visually and quantitatively confirmed that, in all maps, not only path lengths but also the piecewise linear shape were decreased compared to the path generate

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Zhang, Chuang, Yue-Han Pei, Xiao-Xue Wang, Hong-Yu Hou, and Li-Hua Fu. "Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm." PLOS ONE 18, no. 6 (2023): e0287573. http://dx.doi.org/10.1371/journal.pone.0287573.

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To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm’s search diversity, local pioneering ability, and convergenc

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Ahmad, Yasir, Mohib Ullah, Rafiullah Khan, et al. "SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks." Complexity 2020 (December 12, 2020): 1–9. http://dx.doi.org/10.1155/2020/6695032.

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Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This study proposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorit

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Xu, Chenyang, and Benjamin Moseley. "Learning-Augmented Algorithms for Online Steiner Tree." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8744–52. http://dx.doi.org/10.1609/aaai.v36i8.20854.

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This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the online Steiner tree problem in this model for both directed and undirected graphs. Steiner tree is known to have strong lower bounds in the online setting and any algorithm’s worst-case guarantee is far from desirable. This paper considers algorithms that predict which terminal arrives online. The predictions may be incorrect and the algorithms’ performance is parameterized by the number of incorrectly predicted terminal

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Trofymenko, Olena, Yuliia Prokop, Olena Chepurna, and Mykola Korniichuk. "A PERFORMANCE COMPARISON OF SORTING ALGORITHMS IN DIFFERENT PROGRAMMING LANGUAGES." Cybersecurity: Education, Science, Technique 1, no. 21 (2023): 86–98. http://dx.doi.org/10.28925/2663-4023.2023.21.8698.

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Sorting, as one of the basic algorithms, has a wide range of applications in software development. As the amount of processed data grows, the need for fast and efficient data sorting increases significantly. There are many sorting algorithms and their extensions. However, choosing the best and most versatile among them is impossible. All these algorithms have their specifics, which determine the scope of their effective use. Therefore, the problem of deciding the optimal algorithm for certain specific conditions is relevant. This choice is often a non-trivial task, and an unsuccessful choice o

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Rahman, Chnoor M., and Tarik A. Rashid. "Dragonfly Algorithm and Its Applications in Applied Science Survey." Computational Intelligence and Neuroscience 2019 (December 6, 2019): 1–21. http://dx.doi.org/10.1155/2019/9293617.

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One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic al

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Benbouzid, Bilel. "Unfolding Algorithms." Science & Technology Studies 32, no. 4 (2019): 119–36. http://dx.doi.org/10.23987/sts.66156.

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Predictive policing is a research field whose principal aim is to develop machines for predicting crimes, drawing on machine learning algorithms and the growing availability of a diversity of data. This paper deals with the case of the algorithm of PredPol, the best-known startup in predictive policing. The mathematicians behind it took their inspiration from an algorithm created by a French seismologist, a professor in earth sciences at the University of Savoie. As the source code of the PredPol platform is kept inaccessible as a trade secret, the author contacted the seismologist directly in

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Hewawasam, Hasitha, Yousef Ibrahim, and Gayan Kahandawa. "A Novel Optimistic Local Path Planner: Agoraphilic Navigation Algorithm in Dynamic Environment." Machines 10, no. 11 (2022): 1085. http://dx.doi.org/10.3390/machines10111085.

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This paper presents a novel local path planning algorithm developed based on the new free space attraction (Agoraphilic) concept. The proposed algorithm is capable of navigating robots in unknown static, as well as dynamically cluttered environments. Unlike the other navigation algorithms, the proposed algorithm takes the optimistic approach of the navigation problem. It does not look for problems to avoid, but rather for solutions to follow. This human-like decision-making behaviour distinguishes the new algorithm from all the other navigation algorithms. Furthermore, the new algorithm utilis

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Castelo, Noah, Maarten W. Bos, and Donald Lehmann. "Let the Machine Decide: When Consumers Trust or Distrust Algorithms." NIM Marketing Intelligence Review 11, no. 2 (2019): 24–29. http://dx.doi.org/10.2478/nimmir-2019-0012.

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AbstractThanks to the rapid progress in the field of artificial intelligence algorithms are able to accomplish an increasingly comprehensive list of tasks, and often they achieve better results than human experts. Nevertheless, many consumers have ambivalent feelings towards algorithms and tend to trust humans more than they trust machines. Especially when tasks are perceived as subjective, consumers often assume that algorithms will be less effective, even if this belief is getting more and more inaccurate.To encourage algorithm adoption, managers should provide empirical evidence of the algo

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Abdul Qader, Raghad Abdul Hadi, and Auday H. Saeed AL-Wattar. "A Review of the Blowfish Algorithm Modifications in Terms of Execution Time and Security." Technium: Romanian Journal of Applied Sciences and Technology 4, no. 9 (2022): 89–101. http://dx.doi.org/10.47577/technium.v4i9.7452.

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Data has become increasingly popular for advanced digital content transmission. Researchers are concerned about the protection of data. The transmission of digital data over a network has made multimedia data vulnerable to various threats, including unauthorized access and network hacking. As a result, the data must be protected with encryption methods based on symmetric encryption algorithms, which will ensure the data security. The Blowfish encryption algorithm is one of the most well-known cryptographic algorithms. However, each of the current algorithms has its own set of advantages and di

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Yan, Shaoqiang, Weidong Liu, Xinqi Li, Ping Yang, Fengxuan Wu, and Zhe Yan. "Comparative Study and Improvement Analysis of Sparrow Search Algorithm." Wireless Communications and Mobile Computing 2022 (August 31, 2022): 1–15. http://dx.doi.org/10.1155/2022/4882521.

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To solve the problem that the emerging sparrow search algorithm (SSA) lacks systematic comparison and analysis with other classical algorithms, this paper first introduces the principle of the sparrow search algorithm and then describes the mathematical model and algorithm description of the sparrow search algorithm. By comparing several classical intelligent algorithms with particle swarm optimization (PSO), differential evolution (DE), and gray wolf optimizer (GWO), the sparrow search algorithm’s theory and model are systematically compared and analyzed, and the advantages and disadvantages

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Priyadarshini, Ishaani. "Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems." Biomimetics 9, no. 3 (2024): 130. http://dx.doi.org/10.3390/biomimetics9030130.

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In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning alg

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Zhang, Fan, and Zhongsheng Chen. "A Novel Reinforcement Learning-Based Particle Swarm Optimization Algorithm for Better Symmetry between Convergence Speed and Diversity." Symmetry 16, no. 10 (2024): 1290. http://dx.doi.org/10.3390/sym16101290.

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This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling

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Zhang, Jun. "Efficiency of large integer multiplication algorithms: A comparative study of traditional methods and Karatsuba's algorithm." Applied and Computational Engineering 69, no. 1 (2024): 30–36. http://dx.doi.org/10.54254/2755-2721/69/20241470.

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The large integer multiplication is the basis of many computer science algorithms, ranging from cryptography to complex calculations in various scientific fields. Contemporary society excessively depends on complex computing tasks. Hence, the need for good algorithms is becoming increasingly apparent as well. This text gives the reader an in-depth knowledge of the multiplication algorithms of large integers by contrasting traditional algorithms with the new Algorithm developed by Karatsuba. This research methodology involves a comparative analysis of the components using an advanced analysis f

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Kim, Yong-Hyuk, Zong Woo Geem, and Yourim Yoon. "Population-Based Redundancy Control in Genetic Algorithms: Enhancing Max-Cut Optimization." Mathematics 13, no. 9 (2025): 1409. https://doi.org/10.3390/math13091409.

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The max-cut problem is a well-known topic in combinatorial optimization, with a wide range of practical applications. Given its NP-hard nature, heuristic approaches—such as genetic algorithms, tabu search, and harmony search—have been extensively employed. Recent research has demonstrated that harmony search can outperform genetic algorithms by effectively avoiding redundant searches, a strategy similar to tabu search. In this study, we propose a modified genetic algorithm that integrates tabu search to enhance solution quality. By preventing repeated exploration of previously visited solution

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Bhagya Sri, Mukku, Rachita Bhavsar, and Preeti Narooka. "String Matching Algorithms." International Journal Of Engineering And Computer Science 7, no. 03 (2018): 23769–72. http://dx.doi.org/10.18535/ijecs/v7i3.19.

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To analyze the content of the documents, the various pattern matching algorithms are used to find all the occurrences of a limited set of patterns within an input text or input document. In order to perform this task, this research work used four existing string matching algorithms; they are Brute Force algorithm, Knuth-Morris-Pratt algorithm (KMP), Boyer Moore algorithm and Rabin Karp algorithm. This work also proposes three new string matching algorithms. They are Enhanced Boyer Moore algorithm, Enhanced Rabin Karp algorithm and Enhanced Knuth-Morris-Pratt algorithm. Findings: For expe

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Jubair, Mohammed Ahmed, Salama A. Mostafa, Aida Mustapha, Zirawani Baharum, Mohamad Aizi Salamat, and Aldo Erianda. "A Multi-Agent K-Means Algorithm for Improved Parallel Data Clustering." JOIV : International Journal on Informatics Visualization 6, no. 1-2 (2022): 145. http://dx.doi.org/10.30630/joiv.6.1-2.934.

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Due to the rapid increase in data volumes, clustering algorithms are now finding applications in a variety of fields. However, existing clustering techniques have been deemed unsuccessful in managing large data volumes due to the issues of accuracy and high computational cost. As a result, this work offers a parallel clustering technique based on a combination of the K-means and Multi-Agent System algorithms (MAS). The proposed technique is known as Multi-K-means (MK-means). The main goal is to keep the dataset intact while boosting the accuracy of the clustering procedure. The cluster centers

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Vasyluk, Andrii, та Taras Basyuk. "Synthesis System Оf Algebra Algorithms Formulas". Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 9 (10 червня 2021): 11–22. http://dx.doi.org/10.23939/sisn2021.09.011.

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In the article the authors have developed a mathematical support for the process of generating subject unitherms of formulas of algebra of algorithms. The analysis of features of construction of formulas of algebra of algorithms as a result of which it was found out, that today, subsystems with realization of processes of generation of subject unitherms on the basis of abstract unitherms with the subsequent adaptation of formulas are not realized in known systems that served as stimulus to intellectual analysis formulas of algebra of algorithms. It is described that the synthesis of algebra fo

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Muzakir, Ari, Hadi Syaputra, and Febriyanti Panjaitan. "A Comparative Analysis of Classification Algorithms for Cyberbullying Crime Detection: An Experimental Study of Twitter Social Media in Indonesia." Scientific Journal of Informatics 9, no. 2 (2022): 133–38. http://dx.doi.org/10.15294/sji.v9i2.35149.

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Purpose: This research aims to identify content that contains cyberbullying on Twitter. We also conducted a comparative study of several classification algorithms, namely NB, DT, LR, and SVM. The dataset we use comes from Twitter data which is then manually labeled and validated by language experts. This study used 1065 data with a label distribution, namely 638 data with a non-bullying label and 427 with a bullying label.Methods: The weighting process for each word uses the bag of word (BOW) method, which uses three weighting features. The three-word vector weighting features used include uni

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BARMAK, OLEXANDER, PAVLO RADIUK, MARYNA MOLCHANOVA, and OLENA SOBKO. "APPROACHES TO PRACTICAL ANALYSIS OF COMPUTING ALGORITHMS." Herald of Khmelnytskyi National University 303, no. 6 (2021): 102–5. http://dx.doi.org/10.31891/2307-5732-2021-303-6-102-105.

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The present work proposes a practical approach to determining the main types of algorithms, depending on their effectiveness in the appearance of the software code. Examples of analysis of the software code for computational complexity are given in the order of reducing the efficiency supplied as (in asymptotic designations): O(1), O(LogN), O(N), O(NlogN), O(N2), O(N2), O(N2), O(N3). The research task was to analyze the software code and specific conditions in which the algorithm refers to a particular type of computational complexity. The aim of analyzing the complexity of algorithms is to fi

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Singh, Surya Partap, Amitesh Srivastava, Suryansh Dwivedi, and Mr Anil Kumar Pandey. "AI Based Recruitment Tool." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2815–19. http://dx.doi.org/10.22214/ijraset.2023.52193.

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Abstract: In this study, the researchers narrowed their focus to the application of algorithmic decision-making in ranking job applicants. Instead of comparing algorithms to human decision-makers, the study examined participants' perceptions of different types of algorithms. The researchers varied the complexity and transparency of the algorithm to understand how these factors influenced participants' perceptions. The study explored participants' trust in the algorithm's decision-making abilities, fairness of the decisions, and emotional responses to the situation. Unlike previous work, the st

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Wen, Xiaodong, Xiangdong Liu, Cunhui Yu, et al. "IOOA: A multi-strategy fusion improved Osprey Optimization Algorithm for global optimization." Electronic Research Archive 32, no. 3 (2024): 2033–74. http://dx.doi.org/10.3934/era.2024093.

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<abstract><p>With the widespread application of metaheuristic algorithms in engineering and scientific research, finding algorithms with efficient global search capabilities and precise local search performance has become a hot topic in research. The osprey optimization algorithm (OOA) was first proposed in 2023, characterized by its simple structure and strong optimization capability. However, practical tests have revealed that the OOA algorithm inevitably encounters common issues faced by metaheuristic algorithms, such as the tendency to fall into local optima and reduced populat

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Wei, Wei, Liang Liu, Zhong Qin Hu, and Yu Jing Zhou. "Rigid Medical Image Registration Based on Genetic Algorithms and Mutual Information." Applied Mechanics and Materials 665 (October 2014): 712–17. http://dx.doi.org/10.4028/www.scientific.net/amm.665.712.

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With the variety of medical imaging equipment’s application in the medical process,medical image registration becomes particularly important in the field of medical image processing,which has important clinical diagnostic and therapeutic value. This article describes the matrix conversion method of the rigid registration model, the basic concepts and principles of the mutual information algorithm ,the basic idea of genetic algorithms and algorithm’s flow , and the application of the improved genetic algorithms in practice. The rigid registration of two CT brain bones images uses mutual informa

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Mansouri, Taha, Ahad Zare Ravasan, and Mohammad Reza Gholamian. "A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering." International Journal of Data Warehousing and Mining 10, no. 3 (2014): 1–14. http://dx.doi.org/10.4018/ijdwm.2014070101.

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One of the most widely used algorithms to solve clustering problems is the K-means. Despite of the algorithm's timely performance to find a fairly good solution, it shows some drawbacks like its dependence on initial conditions and trapping in local minima. This paper proposes a novel hybrid algorithm, comprised of K-means and a variation operator inspired by mutation in evolutionary algorithms, called Noisy K-means Algorithm (NKA). Previous research used K-means as one of the genetic operators in Genetic Algorithms. However, the proposed NKA is a kind of individual based algorithm that combin

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