Journal articles: 'Algorithems'

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Tian, Xin Cheng, and Xiao Hong Deng. "Trajectory Interpolation in CNC Grinding of Indexable Inserts." Advanced Materials Research 97-101 (March 2010): 2007–10. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.2007.

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Most of the commenly adotped interpolation algorithems are for the curve machining on CNC machine tools with Cartesian coordinates configuration. For a CNC machine tool with non-Cartesian configuration, new trajectory interpolation algorithem must be developed to machine complex part sueface. Based on the analysis of the geometric characteristics and the CNC grinding principle of indexable inserts, this paper proposes two interpolation algorithems to grind the nose surface of indexable inserts with constant or variable back angle. The algorithm precision analysis is also made in this paper.

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Morsy, H., M. El-khatib, W. Hussein, and M. Mahgoub. "INVESTIGATION OF MODERN CONTROL ALGORITHEMS IN MECHATRONIC SYSTEM." International Conference on Applied Mechanics and Mechanical Engineering 15, no. 15 (May 1, 2012): 1–20. http://dx.doi.org/10.21608/amme.2012.37057.

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Ye, Jun. "Applying Immune Algorithems to the Calculation of Sound Insulation of Walls." Applied Mechanics and Materials 584-586 (July 2014): 1853–57. http://dx.doi.org/10.4028/www.scientific.net/amm.584-586.1853.

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Building wall plays a key role in the noise isolation. As a there are lot of open holes in the wall for various construction equipments, pipes and lines, it is an important issue how to determine the maximum area of wall cracks with the given expect sound insulation. The calculation model is established with immune algorithm, the expected value of the sound isolation is defined as objective function, the areal density, thickness and Young modulus of monolayer wall are defined as bounded variable. The global maximum value of objective function is obtained by the MATLAB program and so to determine the materials, thickness and construction details which reach to the sound insulation.

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Murtuza, Syed. "A Concis Presentation of Supiervised Learing Algorithems for Feedforward Neural Netwoks." IFAC Proceedings Volumes 27, no. 9 (August 1994): 91–94. http://dx.doi.org/10.1016/s1474-6670(17)45902-7.

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LIU, Yanfang. "Application of integration algorithems for elasto-plasticity constitutive model for anisotropic sheet materials." Chinese Journal of Mechanical Engineering (English Edition) 19, no. 04 (2006): 554. http://dx.doi.org/10.3901/cjme.2006.04.554.

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Duvvada, Rajeswara Rao, Shaik Ayesha Fathima, and Shaik Noorjahan. "A Comprehensive Analysis and Design of Land Cover Usage from Satellite Images using Machine Learning Algorithems." Journal of Visual Language and Computing 2022, no. 1 (July 5, 2022): 25–35. http://dx.doi.org/10.18293/jvlc2022-n1-014.

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Devi, Kapila, and Saroj Ratnoo. "Cluster analysis of socio-economic factors and academic performance of school students." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (September 1, 2023): 1568. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1568-1577.

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The objective of the paper is to examine the academic performance of students’ vis-a-vis socio-economic factors using clustering analysis. The grades obtained in the 10<sup>th</sup> class are taken as the measure of academic performance. The variables such gender, caste, parental education and occupation. are considered as the socio-economic indicators. Three clustering algorithems are employed. The K-medoid performs better in the validation process to form the groupings based on intra-cluster homogeneity and inter-cluster heterogeneity. The clustering analysis results in two interesting groups of the students. One of the clusters is dominated by the students of general category and the other one by the scheduled caste category. Next, the appropriate statistical tests are applied to determine the factors that significantly differ in the two clusters. Cluster analysis shows that caste, parents' education and occupation, and family income are the differentiating factors between the two groups. However, we are unable to establish significant difference between the academic performance of the two groups of students at a 5% significance. The research carried out in this paper may be beneficial for making policies to bridge the gap in the educational attainment of the students from deprived sections of society.

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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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Gościniak, Ireneusz, and Krzysztof Gdawiec. "Visual Analysis of Dynamics Behaviour of an Iterative Method Depending on Selected Parameters and Modifications." Entropy 22, no. 7 (July 2, 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, various feedback iteration processes are used in this research. Presented examples and the conducted discussion on the algorithm’s operation allow to understand the influence of the proposed modifications on the algorithm’s behaviour. Understanding the impact of the proposed modification on the algorithm’s operation can be helpful in using it in other algorithms. The obtained images also have potential artistic applications.

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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 (September 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 strategy for integrating multiple AGglomerative NESting clusterings is proposed. The algorithm has three main steps: evaluating the credibility of labels, producing multiple base clusters, and constructing the relation among clusters. The proposed algorithm builds on the original advantages of AGglomerative NESting and further compensates for the inability to identify arbitrarily shaped clusters. It can establish the proposed algorithm’s superiority in terms of clustering performance by comparing the proposed algorithm’s clustering performance to that of existing clustering algorithms on different datasets.

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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 (October 1, 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 presenting different parameters, such as performance, algorithm’s complexity, algorithm’s problem, average waiting times, algorithm’s advantages-disadvantages, allocation way, etc. The main purpose of the article is to analyze the CPU scheduler in such a way that suits the scheduling goals. However, knowing the algorithm type which is most suitable for a particular situation by showing its full properties.

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Carnia, Ema, Rinaldi Wilopo, Herlina Napitupulu, Nursanti Anggriani, and Asep K. Supriatna. "Modified Kleene Star Algorithm Using Max-Plus Algebra and Its Application in the Railroad Scheduling Graphical User Interface." Computation 11, no. 1 (January 9, 2023): 11. http://dx.doi.org/10.3390/computation11010011.

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In max-plus algebra, some algorithms for determining the eigenvector of irreducible matrices are the power algorithm and the Kleene star algorithm. In this research, a modified Kleene star algorithm will be discussed to compensate for the disadvantages of the Kleene star algorithm. The Kleene star algorithm’s time complexity is O(n(n!)), and the new Kleene star algorithm’s time complexity is O(n4), while the power algorithm’s time complexity cannot be calculated. This research also applies max-plus algebra in a railroad network scheduling problem, constructing a graphical user interface to perform schedule calculations quickly and easily.

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Hairol Anuar, Siti Haryanti, Zuraida Abal Abas, Norhazwani Mohd Yunos, Nurul Hafizah Mohd Zaki, Nurul Akmal Hashim, Mohd Fariddudin Mokhtar, Siti Azirah Asmai, Zaheera Zainal Abidin, and Ahmad Fadzli Nizam. "Comparison between Louvain and Leiden Algorithm for Network Structure: A Review." Journal of Physics: Conference Series 2129, no. 1 (December 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 reviewed. The concept and benefit are summarized in detail by comparison. Finally, the Leiden algorithm’s property is considered the latest and fastest algorithm than the Louvain algorithm. For the future, the comparison can help in choosing the best community detection algorithms even though these algorithms have different definitions of community.

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Mölders, Marc. "Auf dem Weg zum Subsumtionsautomaten?" Sociologia Internationalis: Volume 56, Issue 2 56, no. 2 (July 1, 2018): 71–89. http://dx.doi.org/10.3790/sint.56.2.71.

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Algorithmen werden in Gerichten eingesetzt, z. B. zur Rückfälligkeitsvorhersage. Dies ruft Assoziationen zum Subsumtionsautomaten des 19. Jahrhunderts hervor und fügt sich nahtlos in die Kritik des Solutionismus ein, soziale Probleme würden zunehmend technisch gelöst. Die Untersuchung von zwei vielfach verwendeten und diskutierten Beispielen (COMPAS, PSA) zeigt allerdings, dass an entscheidenden Stellen gerade nicht auf Technik gesetzt wird. Um den Algorithmus fair zu gestalten, werden nicht technische Schnittstellen, sondern Beteiligungsformate gefordert. Nicht nur hierfür, sondern an vielen Stellen der Entwicklung von Algorithmen im Recht wird auf soziologisches Wissen zurückgegriffen. Entgegen der Kritik, Soziologie müsse praxisrelevanter werden, lässt sich hieran zeigen, dass in der Praxis Soziologie längst eingesetzt wird, allerdings nicht als kritische Beobachterin. Algorithms have entered courts, e. g. via scores assessing recidivism. The 19th century topos “subsumtion automat” as well as the recent notion of solutionism seem to coincide in warning of social problems getting increasingly fixed technologically. Analyzing two of the most prominent examples for legal algorithms (COMPAS, PSA) shows that crucial problems are not (only) tackled by technical means. To design a fair algorithm, for instance, stakeholder procedures should precede technical tweaks. Diffusion of these algorithms is triggered with the help of sociological knowledge. Against lines of criticism calling for a more applicable sociology, such developments rather show that practice makes use of sociology long since, yet not in terms of critical observation.

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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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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 (April 3, 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 efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests.

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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 (June 29, 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 convergence accuracy. A levy flight strategy further improves the algorithm’s ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks.

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Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (June 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 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.

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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 (May 31, 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 cipher and modified the key scheduling algorithm. To assess if these two algorithms are random or not, randomness analysis was done by using the NIST Statistical Test Suite. To create 100 samples for each algorithm, nine distinct data categories were used. These algorithms created ciphertext blocks, which were then concatenated to form a binary sequence. NIST tests carried out under 1% significance level. According to the results of the comparison study, the proposed algorithm's randomness analysis results are gave 27.48% better results than the original algorithm.

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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 (January 15, 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 algorithms utilizing the full array. Throughout, the data sets with realistic modelled noise perform near identically to the recorded noise data sets. The study concludes by testing an algorithm that simultaneously solves for the source location and moment tensor of a microseismic event. Not only does the algorithm fail to perform at the signal-to-noise ratios indicated by the WGN results but the results with realistic modelled noise highlight pitfalls of the algorithm not previously identified. The misleading results from the WGN data sets highlight the need to test algorithms under realistic noise conditions to gain an understanding of the conditions under which an algorithm can perform and to minimize the risk of misinterpretation of the results.

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Geiger, D., C. Meek, and Y. Wexler. "A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints." Journal of Artificial Intelligence Research 27 (September 22, 2006): 1–23. http://dx.doi.org/10.1613/jair.2028.

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We develop a novel algorithm, called VIP*, for structured variational approximate inference. This algorithm extends known algorithms to allow efficient multiple potential updates for overlapping clusters, and overcomes the difficulties imposed by deterministic constraints. The algorithm's convergence is proven and its applicability demonstrated for genetic linkage analysis.

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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 of SSA are summarized. Finally, based on the above research and previous research, the limitations of SSA and current improved SSA are analyzed, which provides ideas for further improvement of the algorithm.

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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 algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm’s weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.

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Pongchairerks, Pisut. "A Job-Shop Scheduling Problem with Bidirectional Circular Precedence Constraints." Complexity 2021 (November 9, 2021): 1–19. http://dx.doi.org/10.1155/2021/3237342.

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This paper introduces a job-shop scheduling problem (JSP) with bidirectional circular precedence constraints, called BCJSP. In the problem, each job can be started from any operation and continued by its remaining operations in a circular precedence-relation chain via either a clockwise or counterclockwise direction. To solve BCJSP, this paper proposes a multilevel metaheuristic consisting of top-, middle-, and bottom-level algorithms. The top- and middle-level algorithms are population-based metaheuristics, while the bottom-level algorithm is a local search algorithm. The top-level algorithm basically controls a start operation and an operation-precedence-relation direction of each job, so that BCJSP becomes a JSP instance that is a subproblem of BCJSP. Moreover, the top-level algorithm can also be used to control input parameters of the middle-level algorithm, as an optional extra function. The middle-level algorithm controls input parameters of the bottom-level algorithm, and the bottom-level algorithm then solves the BCJSP’s subproblem. The middle-level algorithm evolves the bottom-level algorithm’s parameter values by using feedback from the bottom-level algorithm. Likewise, the top-level algorithm evolves the start operations, the operation-precedence-relation directions, and the middle-level algorithm’s parameter values by using feedback from the middle-level algorithm. Performance of two variants of the multilevel metaheuristic (i.e., with and without the mentioned extra function) was evaluated on BCJSP instances modified from well-known JSP instances. The variant with the extra function performs significantly better in number than the other. The existing JSP-solving algorithms can also solve BCJSP; however, their results on BCJSP are clearly worse than those of the two variants of the multilevel metaheuristic.

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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 (November 8, 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 generated by RRT-connect. To check the proposed algorithm’s performance, visibility graph, RRT-connect algorithm, Triangular-RRT-connect algorithm and post triangular processing of midpoint interpolation (PTPMI) were compared in various environmental maps through simulation. Based on these experimental results, the proposed algorithm shows similar planning time but shorter path length than previous RRT-like algorithms as well as RRT-like algorithms with PTPMI having a similar number of samples.

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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 information on the plateaus). Based on our explanations of algorithm performance, we develop a new algorithm that combines characteristics of the best performers; the new algorithm's performance is better than the previous best. We show how hypothesis driven experimentation and search modeling can both explain algorithm performance and motivate the design of a new algorithm.

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Liang, Jianhui, Lifang Wang, and Miao Ma. "An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization." Biomimetics 8, no. 2 (May 19, 2023): 210. http://dx.doi.org/10.3390/biomimetics8020210.

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With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.

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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 (November 16, 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 utilises newly developed tracking and prediction algorithms, to safely navigate mobile robots. This is further supported by a fuzzy logic controller designed to efficiently account for the inherent high uncertainties in the robot’s operational environment at a reduced computational cost. This paper also includes physical experimental results combined with bench-marking against other recent methods. The reported results verify the algorithm’s successful advantages in navigating robots in both static and dynamic environments.

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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 information as a similarity measure, genetic algorithm as the search strategy and matlab as programming environment. Using the three-point crossover technique to exchange the three parameters in the rigid transformation repeectively to produce new individuals, the genetic algorithm’s local search ability enhanced and the prematurity phenomenon can be reduced through the depth study of the basic genetic algorithm. The experiments show that the registration has high stability and accuracy.

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Ahmad, Yasir, Mohib Ullah, Rafiullah Khan, Bushra Shafi, Atif Khan, Mahdi Zareei, Abdallah Aldosary, and Ehab Mahmoud Mohamed. "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 algorithm’s performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.

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Abi, Soufiane, and Bachir Benhala. "An optimal design of current conveyors using a hybrid-based metaheuristic algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6653. http://dx.doi.org/10.11591/ijece.v12i6.pp6653-6663.

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<span lang="EN-US">This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.</span>

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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 (May 31, 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 study emphasized the impact of algorithm design and presentation on perceptions. The findings are important for algorithm designers, especially employers subject to public scrutiny for their hiring practices.

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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 (February 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 subsets in the subset space in a different order and curtails search by employing various search pruning mechanisms. The proposed algorithms are anytime algorithms: a time limit can be set on an algorithm's search and the algorithm can be made to find a subset of MUSes. Experimental evaluation of the proposed algorithms demonstrates that they perform two to three orders of magnitude better than the existing indirect algorithms. Furthermore, the algorithms are able to find MUSes in large CSP benchmarks.

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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 (July 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 combines advantages of both K-means and mutation. As a result, proposed NKA algorithm has the advantage of faster convergence time, while escaping from local optima. In this algorithm, a probability function is utilized which adaptively tunes the rate of mutation. Furthermore, a special mutation operator is used to guide the search process according to the algorithm performance. Finally, the proposed algorithm is compared with the classical K-means, SOM Neural Network, Tabu Search and Genetic Algorithm in a given set of data. Simulation results statistically demonstrate that NKA out-performs all others and it is prominently prone to real time clustering.

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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 (April 24, 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 (AMSR-E) algorithm, the Chang algorithm, and the Foster algorithm. Among these algorithms, validation results indicate that FY-3B and WESTDC perform better than the others. However, these two algorithms often result in considerable underestimation for deep snowpack (greater than 20 cm), while the other three persistently overestimate snow depth, probably because of their poor representation of snowpack characteristics in China. To overcome the retrieval errors that occur under deep snowpack conditions without sacrificing performance under relatively thin snowpack conditions, we developed an empirical snow depth retrieval algorithm suite for the FY-3D satellite. Independent evaluation using weather station observations in 2014 and 2015 demonstrates that the FY-3D snow depth algorithm’s root mean square error (RMSE) and bias are 6.6 cm and 0.2 cm, respectively, and it has advantages over other similar algorithms.

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Muñoz, Mario A., and Kate A. Smith-Miles. "Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space." Evolutionary Computation 25, no. 4 (December 2017): 529–54. http://dx.doi.org/10.1162/evco_a_00194.

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This article presents a method for the objective assessment of an algorithm’s strengths and weaknesses. Instead of examining the performance of only one or more algorithms on a benchmark set, or generating custom problems that maximize the performance difference between two algorithms, our method quantifies both the nature of the test instances and the algorithm performance. Our aim is to gather information about possible phase transitions in performance, that is, the points in which a small change in problem structure produces algorithm failure. The method is based on the accurate estimation and characterization of the algorithm footprints, that is, the regions of instance space in which good or exceptional performance is expected from an algorithm. A footprint can be estimated for each algorithm and for the overall portfolio. Therefore, we select a set of features to generate a common instance space, which we validate by constructing a sufficiently accurate prediction model. We characterize the footprints by their area and density. Our method identifies complementary performance between algorithms, quantifies the common features of hard problems, and locates regions where a phase transition may lie.

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Abasi, Ammar Kamal, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Osama Ahmad Alomari, Mohammed A. Awadallah, Zaid Abdi Alkareem Alyasseri, Iyad Abu Doush, Ashraf Elnagar, Eman H. Alkhammash, and Myriam Hadjouni. "Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization." Applied Sciences 12, no. 19 (October 6, 2022): 10057. http://dx.doi.org/10.3390/app121910057.

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The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony(ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO’s robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively.

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Khajehzadeh, Mohammad, Amin Iraji, Ali Majdi, Suraparb Keawsawasvong, and Moncef L. Nehdi. "Adaptive Salp Swarm Algorithm for Optimization of Geotechnical Structures." Applied Sciences 12, no. 13 (July 3, 2022): 6749. http://dx.doi.org/10.3390/app12136749.

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Based on the salp swarm algorithm (SSA), this paper proposes an efficient metaheuristic algorithm for solving global optimization problems and optimizing two commonly encountered geotechnical engineering structures: reinforced concrete cantilever retaining walls and shallow spread foundations. Two new equations for the leader- and followers-position-updating procedures were introduced in the proposed adaptive salp swarm optimization (ASSA). This change improved the algorithm’s exploration capabilities while preventing it from converging prematurely. Benchmark test functions were used to confirm the proposed algorithm’s performance, and the results were compared to the SSA and other effective optimization algorithms. A Wilcoxon’s rank sum test was performed to evaluate the pairwise statistical performances of the algorithms, and it indicated the significant superiority of the ASSA. The new algorithm can also be used to optimize low-cost retaining walls and foundations. In the analysis and design procedures, both geotechnical and structural limit states were used. Two case studies of retaining walls and spread foundations were solved using the proposed methodology. According to the simulation results, ASSA outperforms alternative models and demonstrates the ability to produce better optimal solutions.

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Afathi, Maan. "Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems." Eastern-European Journal of Enterprise Technologies 6, no. 4 (114) (December 16, 2021): 6–14. http://dx.doi.org/10.15587/1729-4061.2021.245222.

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The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

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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 (May 31, 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 of each partition are calculated, combined, and then clustered. The performance of the suggested method's statistical significance was confirmed using the five datasets that served as testing and assessment methods for the proposed algorithm's efficacy. In terms of performance, the proposed MK-means algorithm is compared to the Clustering-based Genetic Algorithm (CGA), the Adaptive Biogeography Clustering-based Genetic Algorithm (ABCGA), and standard K-means algorithms. The results show that the MK-means algorithm outperforms other algorithms because it works by activating agents separately for clustering processes while each agent considers a separate group of features.

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Sakharov, M. K. "New Adaptive Multi-Memetic Global Optimization Algorithm for Loosely Coupled Systems." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 5 (128) (October 2019): 95–114. http://dx.doi.org/10.18698/0236-3933-2019-5-95-114.

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This study introduces a new parallel adaptive multi-memetic population-based algorithm for solving global optimization problems efficiently on loosely coupled computing systems. The existent approaches to the synthesis of adaptive population-based algorithms were considered along with the parallelization techniques; distinct features of the loosely coupled computing systems were identified; those features have to be considered carefully when designing algorithms for this class of systems. The proposed algorithm is based on the two level adaptation strategies. The upper level is a static one and allows one to adjust numeric values of the basic algorithm's free parameters before the optimization process using the information about an objective function obtained by means of the landscape analysis. The lower level is a dynamic one and was implemented by means of hybridization of the basic algorithm and several local search methods (memes). The work also presents a new static load balancing method for mapping the proposed algorithm onto parallel computing nodes. The proposed load balancing method takes into consideration both the optimization algorithm's distinct features and the computing system's architecture. This results in higher efficiency of the optimization algorithm comparing to the traditional load balancing methods. A wide performance investigation of the proposed optimization algorithm and its software optimization was carried out in this work with a use of high-dimensional benchmark functions of various classes.

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Karapetyan, Daniel, and Gregory Gutin. "A New Approach to Population Sizing for Memetic Algorithms: A Case Study for the Multidimensional Assignment Problem." Evolutionary Computation 19, no. 3 (September 2011): 345–71. http://dx.doi.org/10.1162/evco_a_00026.

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Memetic algorithms are known to be a powerful technique in solving hard optimization problems. To design a memetic algorithm, one needs to make a host of decisions. Selecting the population size is one of the most important among them. Most of the algorithms in the literature fix the population size to a certain constant value. This reduces the algorithm's quality since the optimal population size varies for different instances, local search procedures, and runtimes. In this paper we propose an adjustable population size. It is calculated as a function of the runtime of the whole algorithm and the average runtime of the local search for the given instance. Note that in many applications the runtime of a heuristic should be limited and, therefore, we use this bound as a parameter of the algorithm. The average runtime of the local search procedure is measured during the algorithm's run. Some coefficients which are independent of the instance and the local search are to be tuned at the design time; we provide a procedure to find these coefficients. The proposed approach was used to develop a memetic algorithm for the multidimensional assignment problem (MAP). We show that our adjustable population size makes the algorithm flexible to perform efficiently for a wide range of running times and local searches and this does not require any additional tuning of the algorithm.

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Cao, Li, Haishao Chen, Yaodan Chen, Yinggao Yue, and Xin Zhang. "Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization." Biomimetics 8, no. 2 (April 29, 2023): 186. http://dx.doi.org/10.3390/biomimetics8020186.

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A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimization mechanism on the basis of enhancing the performance of the original algorithm. To obtain a better fitness value throughout the optimization process and increase the algorithm’s optimization accuracy, the fitness function based on maximum likelihood estimation is modified. In order to speed up algorithm convergence and decrease needless global search without compromising population diversity, an initial solution is simultaneously added to the starting population location. Simulation findings demonstrate that the suggested method outperforms the TDOA/AOA algorithm and other comparable algorithms, including Taylor, Chan, PSO, CPSO, and basic CSA algorithms. The approach performs well in terms of robustness, convergence speed, and node positioning accuracy.

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Nie, Shu Zhi, Yan Hua Zhong, and Ming Hu. "Short-Time Traffic Flow Prediction Method Based on Universal Organic Computing Architecture." Advanced Materials Research 756-759 (September 2013): 2785–89. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2785.

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Designed a DNA-based genetic algorithm under the universal architecture of organic computing, combined particle swarm optimization algorithm, introduced a crossover operation for the particle location, can interfere with the particles speed, make inert particles escape the local optimum points, enhanced PSO algorithm's ability to get rid of local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and error analysis of experimental results showed that, the designed algorithms can accurately forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better, can be effectively applied to actual traffic engineering.

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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 (October 10, 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 disadvantages. However, there are several drawbacks to using this algorithm, including complex computational operations, fixed (S-Box) and pattern issues, which can arise while dealing with more complex data, including texts. Many academics have sought to increase the algorithm's efficiency. The modifications to the Blowfish algorithms provided by researchers in prior works are summarized in this publication.

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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 (June 28, 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 terminals. These guarantees ensure that algorithms break through the online lower bounds with good predictions and the competitive ratio gracefully degrades as the prediction error grows. We then observe that the theory is predictive of what will occur empirically. We show on graphs where terminals are drawn from a distribution, the new online algorithms have strong performance even with modestly correct predictions.

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Kang, Jin-Gu, Dong-Woo Lim, Yong-Sik Choi, Woo-Jin Jang, and Jin-Woo Jung. "Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning." Sensors 21, no. 2 (January 6, 2021): 333. http://dx.doi.org/10.3390/s21020333.

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This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

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Zhao, Ji-chun, Shi-hong Liu, and Jun-feng Zhang. "Personalized Distance Learning System based on Sequence Analysis Algorithm." International Journal of Online Engineering (iJOE) 11, no. 7 (August 31, 2015): 33. http://dx.doi.org/10.3991/ijoe.v11i7.4764.

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Personalized learning system can provide users with the most valuable learning resource to them through intelligent recommendation models and algorithms. This paper proposed the classical sequence analysis algorithms, and the Prefixspan algorithm is validated through distance learning platform data. In the event that the minimum support threshold is between 0.003 to 0.004%, test data shows that the performance of the algorithm's accuracy rate is relatively stable and the recommendation effect is satisfactory.

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Suman Laha, Rajeshwar Prasad, Utpal Roy, Damodar Patel, Amit Kumar Saxena,. "An Exhaustive Wrapper Method for Feature Selection in Large Dimensional Datasets (WFS)." Proceeding International Conference on Science and Engineering 11, no. 1 (February 18, 2023): 2206–25. http://dx.doi.org/10.52783/cienceng.v11i1.397.

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In this paper, a novel algorithm for randomly selecting a small subset of features from a dataset is presented. Using different combinations of features across a number of trials, the algorithm discovers the best subsets of features. When these subsets of features are obtained, the classification accuracies produced by three classifiers (K-Nearest Neighbor, Support Vector Machines, and Random Forest) are considered to evaluate the performance criterion of the proposed wrapper-based method. Further, to improve the classification accuracy and reduce the cardinality of the selected feature sets, an exhaustive feature selection method (the wrapper method) is used. The proposed algorithm is simulated on eighteen datasets, and the results are compared with those reported using nine comparable algorithms using three classifiers to justify the performance of the proposed algorithm. The average classification accuracies of eighteen datasets achieved are 88.66% in K-NN, 89.88% in SVM, and 89.14% in RF classifier with at most 10 features. The proposed algorithm archives better CA compared to nine comparable algorithms and the results of the experiments prove the proposed algorithm's performance is better in selecting the most effective features compared to other algorithms.

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Zhang, Haigang, and Da Wang. "An External Selection Mechanism for Differential Evolution Algorithm." Computational Intelligence and Neuroscience 2022 (April 4, 2022): 1–18. http://dx.doi.org/10.1155/2022/4544818.

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The procedures of differential evolution algorithm can be summarized as population initialization, mutation, crossover, and selection. However, successful solutions generated by each iteration have not been fully utilized to our best knowledge. In this study, an external selection mechanism (ESM) is presented to improve differential evolution (DE) algorithm performance. The proposed method stores successful solutions of each iteration into an archive. When the individual is in a state of stagnation, the parents for mutation operation are selected from the archive to restore the algorithm’s search. Most significant of all, a crowding entropy diversity measurement in fitness landscape is proposed, cooperated with fitness rank, to preserve the diversity and superiority of the archive. The ESM can be integrated into existing algorithms to improve the algorithm’s ability to escape the situation of stagnation. CEC2017 benchmark functions are used for verification of the proposed mechanism’s performance. Experimental results show that the ESM is universal, which can improve the accuracy of DE and its variant algorithms simultaneously.

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Journal articles on the topic 'Algorithems'

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