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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 (June 30, 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 (September 30, 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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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 recording speed. The signature has long been used to certify the authenticity of documents and verify (authenticate) an individual. In principle, the signature examination is used during the forensic examination. Signature recognition can be carried out by sequential verification of the signature to each known person. The signature recognition methodology includes a verification methodology and processing of verification results. One of the modern areas of interface improvement is the development and research of software for signature recognition and visualization. The advent of modern computer input tools has led to the emergence of a new type of online signature describing the signature creation process, not the result. Moreover, not only the coordinates of points on the line, but also a sequence of vectors of parameter values for each of the values of pressure, direction and speed of movement, the angle of adaptation of the pen and the signature time.
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Lian, Jian, Yan Zhang, and Cheng Jiang Li. "An Efficient K-Shortest Paths Based Routing Algorithm." Advanced Materials Research 532-533 (June 2012): 1775–79. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1775.
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We present an efficient K-shortest paths routing algorithm for computer networks. This Algorithm is based on enhancements to currently used link-state routing algorithms such as OSPF and IS-IS, which are only focusing on finding the shortest path route by adopting Dijkstra algorithm. Its desire effect to achieve is through the use of K-shortest paths algorighm, which has been implemented successfully in some fileds like traffic engineering. The correctness of this Algorithm is discussed at the same time as long as the comparison with OSPF.
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Wisam, Abdulelah Qasim. "A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOLF OPTIMIZATION ALGORITHM." International Journal of Artificial Intelligence and Applications (IJAIA) 11, January (February 28, 2020): 31–44. https://doi.org/10.5281/zenodo.3690787.
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In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
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Ciric, Vladimir, Aleksandar Cvetkovic, Ivan Milentijevic, and Oliver Vojinovic. "All-Pairs Shortest Paths Algorithm for Regular 2D Mesh Topologies." JUCS - Journal of Universal Computer Science 22, no. (11) (November 1, 2016): 1437–55. https://doi.org/10.3217/jucs-022-11-1437.
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Motivated by the large number of vertices that future technologies will put in the front of path-search algorithms, and inspired by highly regular 2D mesh structures that exist in the domain applications, in this paper we propose a new allpairs shortest paths algorithm, for any given regular 2D mesh topology, with complexity Ο(|V|2), where |V| is the number of vertices in the graph. The proposed algorithm can achieve better runtime than other known algorithms at the cost of narrowing the scope of the graphs that it can process to the graphs with regular 2D topology. The algorithm is developed into formalism by algebraic transformations in tropical algebra of the well-known Floyd-Warshall's algorithm. First we prove the equivalency of the Floyd-Warshall's algorithm and its tropical algebraic representation, and put the transformations of the algorithm into the algebraic domain. Secondly, having in mind the structure of the target class of graphs, we transform the original algorithm in the algebraic domain and develop a simple, low-complexity iterative algorithm for all-pairs shortest paths calculation. Decreasing of computational complexity can contribute to better exploitation of the algorithm in the wide range of applications from hardware design in new emerging technologies to big data problems in information technologies.
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Shaw, Dr Shaik Mohiddin, Dr Dharmaiah Gurram, Hari Krishna Gurram, and Ramakrishna Gurram. "Transitive Closure Algorithm using Binary OR Operation: Primes Algorithm, GHK Algorithm." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 03, no. 02 (April 23, 2015): 01–05. http://dx.doi.org/10.9756/sijcsea/v3i2/03030100101.
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Huang, Yuan Jiang, and Jie Huang. "A New Feature Detection Algorithm Based on RANSAC." Advanced Materials Research 971-973 (June 2014): 1477–80. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1477.
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A improved RANSAC algorithm was introduced into the segmentation of LiDAR and r-radius point density was put forward to the estimation criterion,which aims to remove the discrete point outside the feature plane.an accurate registration is achieved by improving RANSAC algorithim after an analysis on the advantages and disadvantages of the algorithm for objects with many planar feature.The algorithm are implemented with VC++ and VTK platform,tested by real data collected on the test area,it verify the effectiveness and accuracy of the proposed algorithms.
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Deghbouch, Hicham, and Fatima Debbat. "Hybrid Bees Algorithm with Grasshopper Optimization Algorithm for Optimal Deployment of Wireless Sensor Networks." Inteligencia Artificial 24, no. 67 (February 20, 2021): 18–35. http://dx.doi.org/10.4114/intartif.vol24iss67pp18-35.
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This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithm’s accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption.
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Sami N. Hussein and Nazar K. Hussein. "Improving Moth-Flame Optimization Algorithm by using Slime-Mould Algorithm." Tikrit Journal of Pure Science 27, no. 1 (December 2, 2022): 99–109. http://dx.doi.org/10.25130/tjps.v27i1.86.
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The MFO algorithm is one of the modern optimization algorithms based on swarm intelligence, and the SMA algorithm is also one of the latest algorithms in the same field and has the advantages of fast convergence, high convergence accuracy, robust and robust. In this research paper, we introduce an optimized algorithm for MFO based on the SMA algorithm to get better performance using the features in the two algorithms, and two different algorithms are proposed in this field. The two predicted new algorithms were tested with standard test functions and the results were encouraging compared to the standard algorithms.
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Okazaki, Hiroyuki, Yosiki Aoki, and Yasunari Shidama. "Extended Euclidean Algorithm and CRT Algorithm." Formalized Mathematics 20, no. 2 (December 1, 2012): 175–79. http://dx.doi.org/10.2478/v10037-012-0020-2.
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Summary In this article we formalize some number theoretical algorithms, Euclidean Algorithm and Extended Euclidean Algorithm [9]. Besides the a gcd b, Extended Euclidean Algorithm can calculate a pair of two integers (x, y) that holds ax + by = a gcd b. In addition, we formalize an algorithm that can compute a solution of the Chinese remainder theorem by using Extended Euclidean Algorithm. Our aim is to support the implementation of number theoretic tools. Our formalization of those algorithms is based on the source code of the NZMATH, a number theory oriented calculation system developed by Tokyo Metropolitan University [8].
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Kulkarni, Anuj, Saish Padave, Satyam Shrivastava, and Mrs Vidya Kawtikwar. "Algorithm Visualizer." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 1818–23. http://dx.doi.org/10.22214/ijraset.2023.54837.
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Abstract: In recent years, computer science education has become increasingly important as technology continues to play a dominant role in our lives. The understanding of algorithms and their implementation is a crucial aspect of computer science education. Visualizing algorithms can be a powerful tool to help students understand and retain the concepts behind them. This paper presents a new algorithm visualizer that focuses on two main types of algorithms: sorting algorithms and graph pathfinding algorithms. The algorithm visualizer was created using React.js, a popular JavaScript library, and provides visualizations for various sorting algorithms, such as merge sort, quick sort, heap sort, and bubble sort. Additionally, the visualizer includes visualizations for graph pathfinding algorithms such as breadth-first search, depth-first search, and A*. The visualizer also includes mazes and patterns that can be solved using the pathfinding algorithms, allowing users to see the algorithms in action. The algorithm visualizer provides a user-friendly interface that allows users to step through the algorithms and see how they work. This interactive approach to learning algorithms provides a valuable resource for students and educators alike. The visualizer is also highly customizable, allowing users to adjust the speed and complexity of the algorithms to fit their needs. This paper provides a comprehensive overview of the design, implementation, and evaluation of the algorithm visualizer.
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Beth, T., and D. Gollman. "Algorithm engineering for public key algorithms." IEEE Journal on Selected Areas in Communications 7, no. 4 (May 1989): 458–66. http://dx.doi.org/10.1109/49.17708.
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Chen, Zheyuan, Jiani Lu, Yuqi Shang, and Diwen Xu. "Path planning algorithms of sweeping robots." Applied and Computational Engineering 41, no. 1 (February 22, 2024): 99–105. http://dx.doi.org/10.54254/2755-2721/41/20230716.
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Different categories of path planning algorithms for sweeping robot are introduced, including Dijkstra algorithm and A*Algorithm in Traditional path-planning Algorithm, PRM Algorithm and RRT Algorithm in sampling algorithm, and Ant Colony Optimization Algorithms and Genetic algorithms in Intelligent bionic algorithm. Each algorithm has its principles and features introduced. At the same time, several algorithms are compared, and summarized, each algorithm has its advantages and disadvantages, in the future development should be combined with their strengths to optimize the path planning algorithm of the sweeping robot.
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Yazdani, Maziar, and Fariborz Jolai. "Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm." Journal of Computational Design and Engineering 3, no. 1 (June 16, 2015): 24–36. http://dx.doi.org/10.1016/j.jcde.2015.06.003.
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Abstract During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.
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Ozkan, Ramazan, and Ruya Samli. "Flood algorithm: a novel metaheuristic algorithm for optimization problems." PeerJ Computer Science 10 (October 2, 2024): e2278. http://dx.doi.org/10.7717/peerj-cs.2278.
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Metaheuristic algorithms are an important area of research that provides significant advances in solving complex optimization problems within acceptable time periods. Since the performances of these algorithms vary for different types of problems, many studies have been and need to be done to propose different metaheuristic algorithms. In this article, a new metaheuristic algorithm called flood algorithm (FA) is proposed for optimization problems. It is inspired by the flow of flood water on the earth’s surface. The proposed algorithm is tested both on benchmark functions and on a real-world problem of preparing an exam seating plan, and the results are compared with different metaheuristic algorithms. The comparison results show that the proposed algorithm has competitive performance with other metaheuristic algorithms used in the comparison in terms of solution accuracy and time.
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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 (June 1, 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 performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
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Erdemir, Erkan. "Hybrid algorithm proposal for optimizing benchmarking problems: Salp swarm algorithm enhanced by arithmetic optimization algorithm." International Journal of Industrial Engineering Computations 14, no. 2 (2023): 309–22. http://dx.doi.org/10.5267/j.ijiec.2023.1.002.
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Metaheuristic algorithms are easy, flexible and nature-inspired algorithms used to optimize functions. To make metaheuristic algorithms better, multiple algorithms are combined and hybridized. In this context, a hybrid algorithm (HSSAOA) was developed by adapting the exploration phase of the arithmetic optimization algorithm (AOA) to the position update part of the salp swarm algorithm (SSA) of the leader salps/salps. And also, there have also been a few new additions to the SSA. The proposed HSSAOA was tested in three different groups using 22 benchmark functions and compared with 7 well-known algorithms. HSSAOA optimized the best results in a total of 16 benchmark functions in each group. In addition, a statistically significant difference was obtained compared to other algorithms.
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Peng, Qiang, Renjun Zhan, Husheng Wu, and Meimei Shi. "Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm." International Journal of Swarm Intelligence Research 15, no. 1 (August 16, 2024): 1–24. http://dx.doi.org/10.4018/ijsir.352061.
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Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). Wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail; A comparative analysis of their performance in solving different feature-based standard CEC test functions was conducted, with a focus on optimization ability and convergence speed, re-validating the unique characteristics of these two algorithms in searching. In the end, the future development trend and prospect of intelligent optimization algorithms was discussed, which is of great reference significance for the research and application of swarm intelligence optimization algorithms.
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Andriansyah, Andriansyah, and Prima Denny Sentia. "PENENTUAN RUTE KENDARAAN PADA SISTEM DISTRIBUSI LOGISTIK PASCA BENCANA (STUDI KASUS)." Jurnal Manajemen Industri dan Logistik 2, no. 1 (December 4, 2018): 79–89. http://dx.doi.org/10.30988/jmil.v2i1.28.
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The success indicators of disaster mitigation can be seen from the disaster logistics system. Effective and efficient distribution network can make a good disaster logistics system. The problem that related to the design of this network is the vehicle routing problem. The objective is determined optimal route of relief distribution from warehouse to victims with minimum time duration. The problem is solved by branch and bound, insertion heuristic, and local search algorithms. The results obtained by branch and bound and local search algorithm are optimal global. Time duration of vehicle using these algoritm is 1.0562 hours. However, computation time using branch and bound algorithm is very long until 22 hours while local search algorithm only takes 60 seconds. The insertion heuristic algorithm also produces a good solution. Time duration of vehicle using this algoritm is 1,1030 hours. This solution is local optimal, but the computation time is very short, only 0.001 seconds.
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Jiang, Dazhi, and Zhun Fan. "The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/474805.
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At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.
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Chen, Zhenpeng, Yuanjie Zheng, Xiaojie Li, Rong Luo, Weikuan Jia, Jian Lian, and Chengjiang Li. "Interactive Trimap Generation for Digital Matting Based on Single-Sample Learning." Electronics 9, no. 4 (April 17, 2020): 659. http://dx.doi.org/10.3390/electronics9040659.
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Image matting refers to the task of estimating the foreground of images, which is an important problem in image processing. Recently, trimap generation has attracted considerable attention because designing a trimap for every image is labor-intensive. In this paper, a two-step algorithm is proposed to generate trimaps. To use the proposed algorithm, users must only provide some clicks (foreground clicks and background clicks), which are employed as the input to generate a binary mask. One-shot learning technique achieves remarkable progress on semantic segmentation, we extend this technique to perform the binary mask prediction task. The mask is further used to predict the trimap using image dilation. Extensive experiments were performed to evaluate the proposed algorithm. Experimental results show that the trimaps generated using the proposed algorithm are visually similar to the user-annotated ones. Comparing with the interactive matting algorithms, the proposed algoritm is less labor-intensive than trimap-based matting algorithm and achieved more accuate results than scribble-based matting algorithm.
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Huang, Yuxuan, and Yiming Ren. "A new interpolation algorithm based on Hibbard-Laroche algorithm and its superiority." Applied and Computational Engineering 15, no. 1 (October 23, 2023): 119–33. http://dx.doi.org/10.54254/2755-2721/15/20230822.
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In order to optimize the possible problems and improvements in the existing color image restoration interpolation algorithms, we conduct research based on the existing bilinear interpolation method, cok algorithm and Hibbard-Laroche algorithm. Our method is to use our own comparison method to compare different types of images through three algorithms to find the advantages and disadvantages and to some extent combine the advantages of bilinear interpolation and Hibbard-Laroche algorithm to try to innovate a new algorithm to compare with the existing three algorithms. The results show that the existing three algorithms have their own advantages in different scenarios, and the new algorithm is superior to the existing algorithms in terms of clarity and color restoration accuracy in most scenarios. However, due to the large computational complexity, the operation speed is slow.
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Ababneh, Jehad. "Greedy particle swarm and biogeography-based optimization algorithm." International Journal of Intelligent Computing and Cybernetics 8, no. 1 (March 9, 2015): 28–49. http://dx.doi.org/10.1108/ijicc-01-2014-0003.
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Purpose – The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm. Design/methodology/approach – The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms. Findings – Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm. Originality/value – The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.
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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 (May 30, 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 algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.
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Triasih, Rina, Finny Fitry Yani, Diah Asri Wulandari, Betty Weri Yolanda Nababan, Muhammad Buston Ardiyamustaqim, Fransiska Meyanti, Sang Ayu Kompiyang Indriyani, Tiffany Tiara Pakasi, and Ery Olivianto. "Treatment-Decision Algorithm of Child TB: Evaluation of WHO Algorithm and Development of Indonesia Algorithm." Tropical Medicine and Infectious Disease 10, no. 4 (April 14, 2025): 106. https://doi.org/10.3390/tropicalmed10040106.
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Clinical algorithms for child tuberculosis (TB) are a valuable guide for healthcare workers to initiate treatment. We evaluated the agreement of pediatric TB diagnosis using the current Indonesia diagnostic algorithms with the 2022 WHO treatment decision algorithm and developed a new Indonesia algorithm for child TB based upon our findings and expert opinion. We conducted a retrospective study at 10 hospitals in Indonesia, involving children (0–10 years), who were evaluated for TB diagnosis in 2022. A panel of child TB experts used participants’ records to make a diagnosis using the 2022 WHO algorithm and the 2016 Indonesian algorithm. We assessed agreement between the diagnosis made by the attending doctor and those determined by the expert panel. A new Indonesia guideline was developed based on the findings and consensus of various stakeholders. Of 523 eligible children, 371 (70.9%) were diagnosed with TB by the attending doctors, 295 (56.4%) by the WHO algorithm, and 246 (47%) by the Indonesia algorithm. The Cohen’s Kappa of TB diagnosis was: attending doctor vs. WHO algorithm (0.27), attending doctor vs. Indonesia algorithm (0.45), and WHO algorithm vs. Indonesia algorithm (0.42). A review of both algorithms revealed challenges for implementation. An algorithmic approach for child TB diagnosis may not be universally applicable or implementable due to variable access to diagnostic tests and the wide variety of clinical presentations.
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Zhang, Zhaoyang. "Review on String-Matching Algorithm." SHS Web of Conferences 144 (2022): 03018. http://dx.doi.org/10.1051/shsconf/202214403018.
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String-matching algorithm is one of the most researched algorithms in computer science which has become an important factor in many technologies. This field aims at utilizing the least time and resources to find desired sequence of character in complex data content. The most classical and famous string-search algorithms are Knuth-Morris-Pratt (KMP) algorithm and Boyer-Moore (DM) algorithm. These two algorithms provide efficient heuristic jump rules by prefix or suffix. Bitap algorithm was the first to introduce bit-parallelism into string-matching field. Backward Non-Deterministic DAWG Matching (BNDM) algorithm is a modern practical algorithm that is an outstanding combination of theoretical research and practical application. Those meaningful algorithms play a guiding role in future research in string-search algorithm to improve the average performance of the algorithm and reduce resource consumption.
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Hiendro, Ayong. "Projectile-target search algorithm: a stochastic metaheuristic optimization technique." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 3772. http://dx.doi.org/10.11591/ijece.v9i5.pp3772-3778.
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This paper proposes a new stochastic metaheuristic optimization algorithm which is based on kinematics of projectile motion and called projectile-target search (PTS) algorithm. The PTS algorithm employs the envelope of projectile trajectory to find the target in the search space. It has 2 types of control parameters. The first type is set to give the possibility of the algorithm to accelerate convergence process, while the other type is set to enhance the possibility to generate new better projectiles for searching process. However, both are responsible to find better fitness values in the search space. In order to perform its capability to deal with global optimum problems, the PTS algorithm is evaluated on six well-known benchmarks and their shifted functions with 100 dimensions. Optimization results have demonstrated that the PTS algoritm offers very good performances and it is very competitive compared to other metaheuristic algorithms
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Ayong, Hiendro. "Projectile-target search algorithm: a stochastic metaheuristic optimization technique." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 3772–78. https://doi.org/10.11591/ijece.v9i5.pp3772-3778.
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This paper proposes a new stochastic metaheuristic optimization algorithm which is based on kinematics of projectile motion and called projectile-target search (PTS) algorithm. The PTS algorithm employs the envelope of projectile trajectory to find the target in the search space. It has 2 types of control parameters. The first type is set to give the possibility of the algorithm to accelerate convergence process, while the other type is set to enhance the possibility to generate new better projectiles for searching process. However, both are responsible to find better fitness values in the search space. In order to perform its capability to deal with global optimum problems, the PTS algorithm is evaluated on six well-known benchmarks and their shifted functions with 100 dimensions. Optimization results have demonstrated that the PTS algoritm offers very good performances and it is very competitive compared to other metaheuristic algorithms.
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Li, Yifeng, and Ying Tan. "Hierarchical Collaborated Fireworks Algorithm." Electronics 11, no. 6 (March 18, 2022): 948. http://dx.doi.org/10.3390/electronics11060948.
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The fireworks algorithm (FWA) has achieved significant global optimization ability by organizing multiple simultaneous local searches. By dynamically decomposing the target problem and handling each one with a sub-population, it has presented distinct property and applicability compared with traditional evolutionary algorithms. In this paper, we extend the theoretical model of fireworks algorithm based on search space partition to obtain a hierarchical collaboration model. It maintains both multiple local fireworks for local exploitation and one global firework for overall population distribution control. The implemented hierarchical collaborated fireworks algorithm is able to combine the advantages of both classic evolutionary algorithms and fireworks algorithms. Several experiments are provided for in-depth analysis and discussion on the proposed algorithm. The effectiveness of proposed strategy is demonstrated on the benchmark test suite from CEC 2020. Experimental results validate that the hierarchical collaborated fireworks algorithm outperforms former fireworks algorithms significantly and achieves similar results compared with state-of-the-art evolutionary algorithms.
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Mitsos, Alexander, Jaromił Najman, and Ioannis G. Kevrekidis. "Optimal deterministic algorithm generation." Journal of Global Optimization 71, no. 4 (February 13, 2018): 891–913. http://dx.doi.org/10.1007/s10898-018-0611-8.
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Abstract A formulation for the automated generation of algorithms via mathematical programming (optimization) is proposed. The formulation is based on the concept of optimizing within a parameterized family of algorithms, or equivalently a family of functions describing the algorithmic steps. The optimization variables are the parameters—within this family of algorithms—that encode algorithm design: the computational steps of which the selected algorithms consist. The objective function of the optimization problem encodes the merit function of the algorithm, e.g., the computational cost (possibly also including a cost component for memory requirements) of the algorithm execution. The constraints of the optimization problem ensure convergence of the algorithm, i.e., solution of the problem at hand. The formulation is described prototypically for algorithms used in solving nonlinear equations and in performing unconstrained optimization; the parametrized algorithm family considered is that of monomials in function and derivative evaluation (including negative powers). A prototype implementation in GAMS is provided along with illustrative results demonstrating cases for which well-known algorithms are shown to be optimal. The formulation is a mixed-integer nonlinear program. To overcome the multimodality arising from nonconvexity in the optimization problem, a combination of brute force and general-purpose deterministic global algorithms is employed to guarantee the optimality of the algorithm devised. We then discuss several directions towards which this methodology can be extended, their scope and limitations.
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Zeng, Yi, Shiqun Yin, Jiangyue Liu, and Miao Zhang. "Research of Improved FP-Growth Algorithm in Association Rules Mining." Scientific Programming 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/910281.
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Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical algorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces the efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved algorithms of FP-Growth algorithm—Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting steps, and uses another way to achieve). We compared two kinds of improved algorithms with FP-Growth algorithm. Experimental results show that Painting-Growth algorithm is more than 1050 and N Painting-Growth algorithm is less than 10000 in data volume; the performance of the two kinds of improved algorithms is better than that of FP-Growth algorithm.
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Ratseev, S. M., and O. I. Cherevatenko. "ON DECODING ALGORITHMS FOR GENERALIZED REED — SOLOMON CODES WITH ERRORS AND ERASURES." Vestnik of Samara University. Natural Science Series 26, no. 3 (May 6, 2020): 17–29. http://dx.doi.org/10.18287/2541-7525-2020-26-3-17-29.
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The article is devoted to the decoding algorithms for generalized Reed Solomon codes with errorsand erasures. These algorithms are based on Gao algorithm, Sugiyama algorithm, Berlekamp Massey algorithm (Peterson Gorenstein Zierler algorithm). The first of these algorithms belongs to syndrome-free decoding algorithms, the others to syndrome decoding algorithms. The relevance of these algorithms is that they are applicable for decoding Goppa codes, which are the basis of some promising post-quantum cryptosystems. These algorithms are applicable for Goppa codes over an arbitrary field, as opposed to the well-known Patterson decoding algorithm for binary Goppa codes.
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Liu, Zizhuo. "Investigation of progress and application related to Multi-Armed Bandit algorithms." Applied and Computational Engineering 37, no. 1 (January 22, 2024): 155–59. http://dx.doi.org/10.54254/2755-2721/37/20230496.
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This paper discusses four Multi-armed Bandit algorithms: Explore-then-Commit (ETC), Epsilon-Greedy, Upper Confidence Bound (UCB), and Thompson Sampling algorithm. ETC algorithm aims to spend the majority of rounds on the best arm, but it can lead to a suboptimal outcome if the environment changes rapidly. The Epsilon-Greedy algorithm is designed to explore and exploit simultaneously, while it often tries sub-optimal arm even after the algorithm finds the best arm. Thus, the Epsilon-Greedy algorithm performs well when the environment continuously changes. UCB algorithm is one of the most used Multi-armed Bandit algorithms because it can rapidly narrow the potential optimal decisions in a wide range of scenarios; however, the algorithm can be influenced by some specific pattern of reward distribution or noise presenting in the environment. Thompson Sampling algorithm is also one of the most common algorithms in the Multi-armed Bandit algorithm due to its simplicity, effectiveness, and adaptability to various reward distributions. The Thompson Sampling algorithm performs well in multiple scenarios because it explores and exploits simultaneously, but its variance is greater than the three algorithms mentioned above. Today, Multi-armed bandit algorithms are widely used in advertisement, health care, and website and app optimization. Finally, the Multi-armed Bandit algorithms are rapidly replacing the traditional algorithms; in the future, the advanced Multi-armed Bandit algorithm, contextual Multi-armed Bandit algorithm, will gradually replace the old one.
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Bai, Xiaotong, Yuefeng Zheng, Yang Lu, and Yongtao Shi. "Chain hybrid feature selection algorithm based on improved Grey Wolf Optimization algorithm." PLOS ONE 19, no. 10 (October 8, 2024): e0311602. http://dx.doi.org/10.1371/journal.pone.0311602.
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Hybrid feature selection algorithm is a strategy that combines different feature selection methods aiming to overcome the limitations of a single feature selection method and improve the effectiveness and performance of feature selection. In this paper, we propose a new hybrid feature selection algorithm, to be named as Tandem Maximum Kendall Minimum Chi-Square and ReliefF Improved Grey Wolf Optimization algorithm (TMKMCRIGWO). The algorithm consists of two stages: First, the original features are filtered and ranked using the bivariate filter algorithm Maximum Kendall Minimum Chi-Square (MKMC) to form a subset of candidate features S1; Subsequently, S1 features are filtered and sorted to form a candidate feature subset S2 by using ReliefF in tandem, and finally S2 is used in the wrapper algorithm to select the optimal subset. In particular, the wrapper algorithm is an improved Grey Wolf Optimization (IGWO) algorithm based on random disturbance factors, while the parameters are adjusted to vary randomly to make the population variations rich in diversity. Hybrid algorithms formed by combining filter algorithms with wrapper algorithms in tandem show better performance and results than single algorithms in solving complex problems. Three sets of comparison experiments were conducted to demonstrate the superiority of this algorithm over the others. The experimental results show that the average classification accuracy of the TMKMCRIGWO algorithm is at least 0.1% higher than the other algorithms on 20 datasets, and the average value of the dimension reduction rate (DRR) reaches 24.76%. The DRR reached 41.04% for 12 low-dimensional datasets and 0.33% for 8 high-dimensional datasets. It also shows that the algorithm improves the generalization ability and performance of the model.
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Tilahun, Surafel Luleseged, and Hong Choon Ong. "Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems." International Journal of Information Technology & Decision Making 14, no. 06 (November 2015): 1331–52. http://dx.doi.org/10.1142/s021962201450031x.
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Nature-inspired optimization algorithms have become useful in solving difficult optimization problems in different disciplines. Since the introduction of evolutionary algorithms several studies have been conducted on the development of metaheuristic optimization algorithms. Most of these algorithms are inspired by biological phenomenon. In this paper, we introduce a new algorithm inspired by prey-predator interaction of animals. In the algorithm randomly generated solutions are assigned as a predator and preys depending on their performance on the objective function. Their performance can be expressed numerically and is called the survival value. A prey will run towards the pack of preys with better surviving values and away from the predator. The predator chases the prey with the smallest survival value. However, the best prey or the prey with the best survival value performs a local search. Hence the best prey focuses fully on exploitation while the other solution members focus on the exploration of the solution space. The algorithm is tested on selected well-known test problems and a comparison is also done between our algorithm, genetic algorithm and particle swarm optimization. From the simulation result, it is shown that on the selected test problems prey-predator algorithm performs better in achieving the optimal value.
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Challenger, Moharram, Elif Haytaoglu, Gorkem Tokatli, Orhan Dagdeviren, and Kayhan Erciyes. "A Hybrid Distributed Mutual Exclusion Algorithm for Cluster-Based Systems." Mathematical Problems in Engineering 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/703414.
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Distributed mutual exclusion is a fundamental problem which arises in various systems such as grid computing, mobile ad hoc networks (MANETs), and distributed databases. Reducing key metrics like message count per any critical section (CS) and delay between two CS entrances, which is known as synchronization delay, is a great challenge for this problem. Various algorithms use either permission-based or token-based protocols. Token-based algorithms offer better communication costs and synchronization delay. Raymond's and Suzuki-Kasami's algorithms are well-known token-based ones. Raymond's algorithm needs onlyO(log2(N)) messages per CS and Suzuki-Kasami's algorithm needs just one message delivery time between two CS entrances. Nevertheless, both algorithms are weak in the other metric, synchronization delay and message complexity correspondingly. In this work, a new hybrid algorithm is proposed which gains from powerful aspects of both algorithms. Raysuz's algorithm (the proposed algorithm) uses a clustered graph and executes Suzuki-Kasami's algorithm intraclusters and Raymond's algorithm interclusters. This leads to have better message complexity than that of pure Suzuki-Kasami's algorithm and better synchronization delay than that of pure Raymond's algorithm, resulting in an overall efficient DMX algorithm pure algorithm.
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YE, DESHI, and QINMING HE. "WORST-CASE PERFORMANCE EVALUATION ON MULTIPROCESSOR TASK SCHEDULING WITH RESOURCE AUGMENTATION." International Journal of Foundations of Computer Science 22, no. 04 (June 2011): 971–82. http://dx.doi.org/10.1142/s0129054111008519.
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We study the worst-case performance of approximation algorithms for the problem of multiprocessor task scheduling on m identical processors with resource augmentation, whose objective is to minimize the makespan. In this case, the approximation algorithms are given k (k ≥ 0) extra processors than the optimal off-line algorithm. For on-line algorithms, the Greedy algorithm and shelf algorithms are studied. For off-line algorithm, we consider the LPT (longest processing time) algorithm. Particularly, we prove that the schedule produced by the LPT algorithm is no longer than the optimal off-line algorithm if and only if k ≥ m - 2.
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Han, Zhonghai, Haotian Sun, Junfu Huang, Jiejie Xu, Yu Tang, and Xintian Liu. "Path Planning Algorithms for Smart Parking: Review and Prospects." World Electric Vehicle Journal 15, no. 7 (July 20, 2024): 322. http://dx.doi.org/10.3390/wevj15070322.
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Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking.
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Brooks, Stephen P. "Algorithms AS 298: A Hybrid Optimization Algorithm." Applied Statistics 44, no. 4 (1995): 530. http://dx.doi.org/10.2307/2986143.
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Jobson, K. "International Psychopharmacology Algorithm Project: Algorithms in Psychopharmacology." International Journal of Psychiatry in Clinical Practice 1, sup1 (January 1997): S3. http://dx.doi.org/10.3109/13651509709024748.
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Chatain, Peter, Rocky Garg, and Lauren Tompkins. "Evolutionary Algorithms for Tracking Algorithm Parameter Optimization." EPJ Web of Conferences 251 (2021): 03071. http://dx.doi.org/10.1051/epjconf/202125103071.
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The reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. The most widely used and best performing tracking algorithms require significant geometry-specific tuning of the algorithm parameters to achieve best results. In this paper, we demonstrate the usage of machine learning techniques, particularly evolutionary algorithms, to find high performing configurations for the first step of tracking, called track seeding. We use a track seeding algorithm from the software framework A Common Tracking Software (ACTS). ACTS aims to provide an experimentindependent and framework-independent tracking software designed for modern computing architectures. We show that our optimization algorithms find highly performing configurations in ACTS without hand-tuning. These techniques can be applied to other reconstruction tasks, improving performance and reducing the need for laborious hand-tuning of parameters.
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S, Gajawada. "Lord Rama Devotees Algorithm: A New Human-Inspired Metaheuristic Optimization Algorithm." Advances in Robotic Technology 1, no. 1 (October 2, 2023): 1–2. http://dx.doi.org/10.23880/art-16000105.
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Several Human-Inspired Metaheuristic Optimization Algorithms were proposed in literature. But the concept of DevoteesInspired Metaheuristic Optimization Algorithms is not yet explored. In this article, Lord Rama Devotees Algorithm (LRDA) is proposed which is a new Devotees-Inspired Metaheuristic Optimization Algorithm.
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Wu, Hai Fei, and Tong Zhao. "A Study of MRife Algorithm and CSB Algorithm on Frequency Online Estimation." Applied Mechanics and Materials 128-129 (October 2011): 789–93. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.789.
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Online estimation of instantaneous frequencies is a method to treat information signals by following a set of certain algorithm for the purpose of extracting the sine wave signal frequency that is overwhelmed in the noise. Among several algorithms available nowadays, two are selected here-one is the modification of Rife (MRife) algorithm based on frequency-domain analysis; and the other is the correlation series based (CSB) algorithm. With simulation methods, the estimation error, stabilization time and computation load of these two algorithms are analyzed and compared respectively. Finally, these two algorithms are applied to estimate the vibration signal frequency of the diesel engine’s base under normal operating conditions. From the results, we come to the conclusion that MRife algorithm and CSB algorithm both have their specific advantages, whereas MRife algorithm is more applicable in the view of estimation error.
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Kaya, Ebubekir, Ceren Baştemur Kaya, Emre Bendeş, Sema Atasever, Başak Öztürk, and Bilgin Yazlık. "Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking." Biomimetics 8, no. 5 (September 1, 2023): 402. http://dx.doi.org/10.3390/biomimetics8050402.
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One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization algorithms. These algorithms are artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization. Mean squared error is used as the error metric, and the performances of the algorithms in different network structures are evaluated. Considering the results, a success ranking score is obtained for each algorithm. The three most successful algorithms in both training and testing processes are the firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm, respectively. The training error values obtained with these algorithms are 4.5 × 10−4, 1.6 × 10−3, and 2.3 × 10−3, respectively. The test error values are 4.6 × 10−4, 1.6 × 10−3, and 2.4 × 10−3, respectively. With these algorithms, effective results have been achieved in a low number of evaluations. In addition to these three algorithms, other algorithms have also achieved mostly acceptable results. This shows that the related algorithms are generally successful ANFIS training algorithms for maximum power point tracking.
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Nursania Simbolon and Yahfizham Yahfizham. "Studi Literartur Algoritma Pemograman Pada Pembelajaran Matematika." Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) 1, no. 4 (November 13, 2023): 147–57. http://dx.doi.org/10.59061/jentik.v1i4.510.
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An algorithm is an effective step or method used to solve a particular problem or task. Algorithms are designed to be executed in a methodical manner, structured As it is. logical in this way, thus enabling consistent and efficient problem solving. A few issues that could come up during the process of designing an algorithm include: 1. Inappropriate Structure, .2. Illogical Algorithm, .3. Difficulty in Solving Algorithms: Sometimes, solving algorithms can be difficult, especially if the problem at hand is complex. This can cause confusion and require extra time to design the right algorithm. Programming algorithms typically serve as a guide for computer programmers in designing and implementing software solutions. This algorithm must be clear, systematic, and can be implemented well in the chosen programming language. This research shows that It is crucial to deal with this matter in order to plan and design the algorithm carefully. This may involve modeling the problem, logical thinking, and testing the algorithm to ensure that the algorithm is working as intended. Additionally, in software development, teams often work together to solve problems and optimize algorithms. With good practice and experience, solving algorithms can become more efficient and effective. The following are several types of programming algorithms related to mathematics: 1. Basic Mathematical Operation Algorithms, 2. Search and Sorting Algorithms, 3. Graph Algorithms 4. Geometry Algorithms, 5. Cryptographic Algorithms, 6. Statistical Algorithms, 7. Machine Learning Algorithms, 8. Advanced Mathematical Algorithms. This study uses a library approach and is qualitative.
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Wheeler, Bradley J., and Hassan A. Karimi. "Enhancing Hyperspectral Anomaly Detection Algorithm Comparisons: Leveraging Dataset and Algorithm Characteristics." Remote Sensing 16, no. 20 (October 18, 2024): 3879. http://dx.doi.org/10.3390/rs16203879.
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Validating the contributions of new algorithms is a critical step in hyperspectral anomaly detection (HAD) research. Typically, validation involves comparing the performance of a proposed algorithm against other algorithms using a series of benchmark datasets. Despite the longstanding use of this comparison process, little attention has been paid to the characteristics of datasets and algorithms that ensure each algorithm has an equal opportunity of performing well. Characteristics of datasets and algorithms that inadvertently favor one algorithm can skew results, leading to misleading conclusions. To address this issue, this study introduces a feature-centric framework designed to assist in ensuring an unbiased comparison of HAD algorithms. The framework identifies significant correlations between datasets and algorithms by extracting distribution-related features from the datasets and statistically testing them against the algorithmic outcomes. The identified trends are then compared across datasets to ensure that all relevant trends are equally represented, thereby ensuring diversity and validating that no singular algorithm is afforded an inherent advantage. The framework was tested on five algorithms across 14 datasets. The results indicate that multiple measures of variance within the datasets are key drivers of diversity, and these measures accurately predicted algorithmic outcomes for 12 of the 14 datasets. This suggests that the identified trends effectively explain the algorithmic outcomes and highlights the importance of incorporating datasets with a diverse range of variances in comparisons of HAD algorithms.
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Thanoon, Radhwan Basim. "Hybrid Inverse Weed Optimization Algorithm with Math-Flame Optimization Algorithm." sinkron 8, no. 3 (August 4, 2024): 2008–21. http://dx.doi.org/10.33395/sinkron.v8i3.13755.
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In this work, two Meta-Heuristic Algorithms were hybridized, the first is the Invers Weed optimization algorithm (IWO), which is a passing multiple algorithm, and the second is the Moth-flame Optimization Algorithm (MFO). Which depend in their behavior on the intelligence of the swarm and the intelligence of society, and they have unique characteristics that exceed the characteristics of the intelligence of other swarms because they are efficient in achieving the right balance between exploration and exploitation. So the new algorithm improves the initial population that is randomly generated, A process of hybridization was made between the IWO and MFO Algorithm to call The new hybrid algorithm (IWOMFO). The new hybrid algorithm was used for 16 high-scaling optimization functions with different community sizes and 250 repetitions. The Algorithm showed access to optimal solutions by achieving the value Minority () for most of these functions and the results of this algorithm are compared with the basic algorithms IWO, MFO
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Yang, Huiwei. "Application of Hybrid Encryption Algorithm in Hardware Encryption Interface Card." Security and Communication Networks 2022 (May 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/7794209.
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In order to effectively solve the increasingly prominent network security problems, cryptographic algorithm is the key factor affecting the effectiveness of IPSec VPN encryption. Therefore, this paper mainly studies cryptographic algorithms and puts forward the following solutions: briefly analyze the concept and function of IPSec VPN, as well as the basic theoretical knowledge of IPSec Security Protocol and cryptography, and analyze the traditional cryptography, modern cryptography, symmetric cryptographic algorithms and asymmetric algorithms, and their security. At the same time, the executable and security performances of AES and DES algorithms are compared and analyzed. This paper studies the elliptic curve encryption algorithm ECC, expounds the mathematical basis of realizing the algorithm, and compares and analyzes the security performance and execution efficiency of ECC. Based on the above two algorithms, a hybrid encryption algorithm is proposed, and the realization mechanism of the hybrid encryption algorithm is studied and discussed. The hybrid encryption algorithm combines the advantages of ECC and AES. The algorithm selects 128-bit AES and 256-bit ECC. In order to better cover up plaintext C, AES is used to encrypt information. While enhancing security, speed is also considered. The improved encryption, decryption, and signature authentication algorithms are relatively safe and fast schemes. ECC algorithm is improved, and on this basis, ECC algorithm and AES algorithm are combined. Moreover, HMAC message authentication algorithm is added, and the performance of the improved algorithm is significantly improved.
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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 (March 23, 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 experimentation, this work has used two types of documents, i.e. .txt and .docx. Performance measures used are search time, number of iterations and accuracy. From the experimental results, it is realized that the enhanced KMP algorithm gives better accuracy compared to other string matching algorithms. Application/Improvements: Normally, these algorithms are used in the field of text mining, document classification, content analysis and plagiarism detection. In future, these algorithms have to be enhanced to improve their performance and the various types of documents will be used for experimentation.