Journal articles: 'Adaptive algorithms'

1

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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Carstensen, Carsten, and Rob Stevenson. "Adaptive Algorithms." Oberwolfach Reports 13, no. 3 (2016): 2513–70. http://dx.doi.org/10.4171/owr/2016/44.

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Bhardwaj, Sumit, DR Vinod Shokeen, and Arun kumar. "A Review on Adaptive Equalizer Algorithms." International Journal of Scientific Research 3, no. 7 (June 1, 2012): 187–89. http://dx.doi.org/10.15373/22778179/july2014/59.

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Hu, Yingkang, Kirill A. Kopotun., and Xiang Ming Yu. "Modified Adaptive Algorithms." SIAM Journal on Numerical Analysis 38, no. 3 (January 2000): 1013–33. http://dx.doi.org/10.1137/s0036142999353569.

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Krommer, Arnold R., and Christoph W. Ueberhuber. "Architecture adaptive algorithms." Parallel Computing 19, no. 4 (April 1993): 409–35. http://dx.doi.org/10.1016/0167-8191(93)90055-p.

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Guan, Sihai, Qing Cheng, Yong Zhao, and Bharat Biswal. "Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function." PLOS ONE 16, no. 10 (October 11, 2021): e0258155. http://dx.doi.org/10.1371/journal.pone.0258155.

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Recently, adaptive filtering algorithms were designed using hyperbolic functions, such as hyperbolic cosine and tangent function. However, most of those algorithms have few parameters that need to be set, and the adaptive estimation accuracy and convergence performance can be improved further. More importantly, the hyperbolic sine function has not been discussed. In this paper, a family of adaptive filtering algorithms is proposed using hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF) function. Specifically, development of a robust adaptive filtering algorithm based on HSF, and extend the HSF algorithm to another novel adaptive filtering algorithm based on IHSF; then continue to analyze the computational complexity for HSF and IHSF; finally, validation of the analyses and superiority of the proposed algorithm via simulations. The HSF and IHSF algorithms can attain superior steady-state performance and stronger robustness in impulsive interference than several existing algorithms for different system identification scenarios, under Gaussian noise and impulsive interference, demonstrate the superior performance achieved by HSF and IHSF over existing adaptive filtering algorithms with different hyperbolic functions.

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Huy, Nguyen Quang, Ong Yew Soon, Lim Meng Hiot, and Natalio Krasnogor. "Adaptive Cellular Memetic Algorithms." Evolutionary Computation 17, no. 2 (June 2009): 231–56. http://dx.doi.org/10.1162/evco.2009.17.2.231.

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A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperforms other methods both in the quality of solutions obtained and the number of function evaluations for a range of continuous optimization problems.

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LAWLOR, DAVID, YANG WANG, and ANDREW CHRISTLIEB. "ADAPTIVE SUB-LINEAR TIME FOURIER ALGORITHMS." Advances in Adaptive Data Analysis 05, no. 01 (January 2013): 1350003. http://dx.doi.org/10.1142/s1793536913500039.

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We present a new deterministic algorithm for the sparse Fourier transform problem, in which we seek to identify k ≪ N significant Fourier coefficients from a signal of bandwidth N. Previous deterministic algorithms exhibit quadratic runtime scaling, while our algorithm scales linearly with k in the average case. Underlying our algorithm are a few simple observations relating the Fourier coefficients of time-shifted samples to unshifted samples of the input function. This allows us to detect when aliasing between two or more frequencies has occurred, as well as to determine the value of unaliased frequencies. We show that empirically our algorithm is orders of magnitude faster than competing algorithms.

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Uddin, Zahoor, Ayaz Ahmad, Muhammad Iqbal, and Zeeshan Kaleem. "Adaptive Step Size Gradient Ascent ICA Algorithm for Wireless MIMO Systems." Mobile Information Systems 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/7038531.

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Independent component analysis (ICA) is a technique of blind source separation (BSS) used for separation of the mixed received signals. ICA algorithms are classified into adaptive and batch algorithms. Adaptive algorithms perform well in time-varying scenario with high-computational complexity, while batch algorithms have better separation performance in quasistatic channels with low-computational complexity. Amongst batch algorithms, the gradient-based ICA algorithms perform well, but step size selection is critical in these algorithms. In this paper, an adaptive step size gradient ascent ICA (ASS-GAICA) algorithm is presented. The proposed algorithm is free from selection of the step size parameter with improved convergence and separation performance. Different performance evaluation criteria are used to verify the effectiveness of the proposed algorithm. Performance of the proposed algorithm is compared with the FastICA and optimum block adaptive ICA (OBAICA) algorithms for quasistatic and time-varying wireless channels. Simulation is performed over quadrature amplitude modulation (QAM) and binary phase shift keying (BPSK) signals. Results show that the proposed algorithm outperforms the FastICA and OBAICA algorithms for a wide range of signal-to-noise ratio (SNR) and input data block lengths.

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Zhang, Qingyang, Tianji Peng, Guangchun Zhang, Jie Liu, Xiaowei Guo, Chunye Gong, Bo Yang, and Xukai Fan. "An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing." Science and Technology of Nuclear Installations 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/5549602.

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This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel algorithm, an adaptive load-balancing algorithm that can increase the number of nodes dynamically is proposed to improve its efficiency. Moreover, with the natural parallelism of block Jacobi algorithm, another adaptive load-balancing algorithm is proposed to improve its performance. A 3 x 3 PWR fuel pin model and a 1000 MWt ABR metallic benchmark core were used to compare the performances of the two algorithms and verify the effectiveness of the two adaptive load-balancing algorithms. The results show that the adaptive load-balancing algorithms proposed in this paper can greatly improve the computing efficiency of block Jacobi algorithm and improve the performance of block Gauss–Seidel algorithm when the number of nodes is large. In addition, the adaptive load-balancing algorithms are especially effective when a case demands different computational power of OpenMC and FLUENT.

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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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Yamada, Seiji, Ichi-ann Ohsaki, Masatoshi Ikeuchi, and Hiroshi Niki. "Non-adaptive and adaptive SAOR-CG algorithms." Journal of Computational and Applied Mathematics 12-13 (May 1985): 635–50. http://dx.doi.org/10.1016/0377-0427(85)90055-x.

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Chen, Bo, Yilin Zhou, Zhaoyi Li, Jingjing Jia, and Yirui Zhang. "Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm." Sensors 23, no. 9 (April 28, 2023): 4371. http://dx.doi.org/10.3390/s23094371.

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Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms’ convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm.

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Vijayan, Darveen, and Izzatdin Aziz. "Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications." Telecom 4, no. 1 (December 22, 2022): 1–14. http://dx.doi.org/10.3390/telecom4010001.

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Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm’s high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruvka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without depreciating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters.

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Ueng, Fang-Biau, and Y. T. Su. "Adaptive IIR blind algorithms." Electronics Letters 31, no. 12 (June 8, 1995): 942–43. http://dx.doi.org/10.1049/el:19950688.

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Niesen, Urs, Devavrat Shah, and Gregory W. Wornell. "Adaptive Alternating Minimization Algorithms." IEEE Transactions on Information Theory 55, no. 3 (March 2009): 1423–29. http://dx.doi.org/10.1109/tit.2008.2011442.

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Shatdal, Ambuj, and Jeffrey F. Naughton. "Adaptive parallel aggregation algorithms." ACM SIGMOD Record 24, no. 2 (May 22, 1995): 104–14. http://dx.doi.org/10.1145/568271.223801.

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Green, D. G., R. E. Reichelt, and R. G. Buck. "Self-adaptive modelling algorithms." Mathematics and Computers in Simulation 30, no. 1-2 (February 1988): 33–38. http://dx.doi.org/10.1016/0378-4754(88)90101-2.

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19

Kangas, A., and E. Karlsson. "Adaptive Root Estimation Algorithms." IFAC Proceedings Volumes 24, no. 3 (July 1991): 743–48. http://dx.doi.org/10.1016/s1474-6670(17)52438-6.

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Afek, Yehuda, and Yaron De Levie. "Efficient adaptive collect algorithms." Distributed Computing 20, no. 3 (September 7, 2007): 221–38. http://dx.doi.org/10.1007/s00446-007-0041-1.

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Prakash, R., and V. V. Veeravalli. "Adaptive hard handoff algorithms." IEEE Journal on Selected Areas in Communications 18, no. 11 (November 2000): 2456–64. http://dx.doi.org/10.1109/49.895049.

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MIKHAEL, WASFY B., and FRANK H. WU. "A UNIFIED APPROACH FOR GENERATING OPTIMUM GRADIENT FIR ADAPTIVE ALGORITHMS WITH TIME-VARYING CONVERGENCE FACTORS." Journal of Circuits, Systems and Computers 01, no. 01 (March 1991): 19–42. http://dx.doi.org/10.1142/s0218126691000203.

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In this paper, a unified approach for generating fast block- and sequential-gradient LMS FIR tapped delay line (TDL) adaptive algorithms is presented. These algorithms employ time-varying convergence factors which are tailored for the adaptive filter coefficients and updated at each block or single data iteration. The convergence factors are chosen to minimize the mean squared error (MSE) and are easily computed from readily available signals. The general formulation leads to three classes of adaptive algorithms. These algorithms. ordered in a descending order of their computational complexity and performance. are: the optimum block adaptive algorithm with individual adaptation of parameters (OBAI), the optimum block adaptive (OBA) and OBA shifting (ODAS) algorithms, and the homogeneous adaptive (HA) algorithm. In this paper, it is shown how each class of algorithms is obtained from the previous one, by a simple trade-off between adaptation performance and computational complexity. Implementation aspects of the generated algorithms are examined and their performance is evaluated and compared with several recently proposed algorithms by means of computer simulations under a wide range of adaptation conditions. The evaluation results show that the generated algorithms have attractive features in the comparisons due to the considerable reduction in the number of iterations required for a given adaptation accuracy. The improvement, however. is achieved at the expense of a relatively modest increase in the number of computations per data sample.

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Fang, Xuelai, and Xiangchu Feng. "Domain-Aware Adaptive Logarithmic Transformation." Electronics 12, no. 6 (March 9, 2023): 1318. http://dx.doi.org/10.3390/electronics12061318.

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Tone mapping (TM) aims to display high dynamic range scenes on media with limited visual information reproduction. Logarithmic transformation is a widely used preprocessing method in TM algorithms. However, the conventional logarithmic transformation does not take the difference in image properties into account, nor does it consider tone mapping algorithms, which are designed based on the luminance or gradient-domain features. There will be problems such as oversaturation and loss of details. Based on the analysis of existing preprocessing methods, this paper proposes a domain-aware adaptive logarithmic transformation AdaLogT as a preprocessing method for TM algorithms. We introduce the parameter p and construct different objective functions for different domains TM algorithms to determine the optimal parameter values adaptively. Specifically, for luminance-domain algorithms, we use image exposure and histogram features to construct objective function; while for gradient-domain algorithms, we introduce texture-aware exponential mean local variance (EMLV) to build objective function. Finally, we propose a joint domain-aware logarithmic preprocessing method for deep-neural-network-based TM algorithms. The experimental results show that the novel preprocessing method AdaLogT endows each domain algorithm with wider scene adaptability and improves the performance in terms of visual effects and objective evaluations, the subjective and objective index scores of the tone mapping quality index improved by 6.04% and 5.90% on average for the algorithms.

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Łatuszyński, Krzysztof, and Jeffrey S. Rosenthal. "The Containment Condition and Adapfail Algorithms." Journal of Applied Probability 51, no. 4 (December 2014): 1189–95. http://dx.doi.org/10.1239/jap/1421763335.

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This short note investigates convergence of adaptive Markov chain Monte Carlo algorithms, i.e. algorithms which modify the Markov chain update probabilities on the fly. We focus on the containment condition introduced Roberts and Rosenthal (2007). We show that if the containment condition is not satisfied, then the algorithm will perform very poorly. Specifically, with positive probability, the adaptive algorithm will be asymptotically less efficient then any nonadaptive ergodic MCMC algorithm. We call such algorithms AdapFail, and conclude that they should not be used.

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Łatuszyński, Krzysztof, and Jeffrey S. Rosenthal. "The Containment Condition and Adapfail Algorithms." Journal of Applied Probability 51, no. 04 (December 2014): 1189–95. http://dx.doi.org/10.1017/s0021900200012055.

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This short note investigates convergence of adaptive Markov chain Monte Carlo algorithms, i.e. algorithms which modify the Markov chain update probabilities on the fly. We focus on the containment condition introduced Roberts and Rosenthal (2007). We show that if the containment condition is not satisfied, then the algorithm will perform very poorly. Specifically, with positive probability, the adaptive algorithm will be asymptotically less efficient then any nonadaptive ergodic MCMC algorithm. We call such algorithms AdapFail, and conclude that they should not be used.

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Hernandez, Carlos, Jorge Baier, Tansel Uras, and Sven Koenig. "Paper Summary: Time-Bounded Adaptive A*." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (August 20, 2021): 184–85. http://dx.doi.org/10.1609/socs.v3i1.18228.

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This paper summarizes our AAMAS 2012 paper on "Time-Bounded Adaptive A*," which introduces the game time model to evaluate search algorithms in real-time settings, such as video games. It then extends the existing real-time search algorithm TBA* to path planning with the freespace assumption in initially partially or completely unknown terrain, resulting in Time-Bounded Adaptive A* (TBAA*). TBAA* needs fewer time intervals in the game time model than several state-of-the-art complete and real-time search algorithms and about the same number of time intervals as the best compared complete search algorithm, even though it has the advantage over complete search algorithms that the agent starts to move right away.

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Kostromitsky, S. M., I. N. Davydzenko, and A. A. Dyatko. "Equivalent forms of writing of processing algorithms of adaptive antenna array." Proceedings of the National Academy of Sciences of Belarus, Physical-Technical Series 67, no. 2 (July 2, 2022): 230–38. http://dx.doi.org/10.29235/1561-8358-2022-67-2-230-238.

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The article is devoted to obtaining equivalent forms of writing of processing algorithms for the operation of adaptive antenna arrays, considering algorithms as varieties of some generalized LMS algorithm. This will facilitate a comparative analysis of the algorithms’ characteristics. The following algorithms of operation are considered: LMS, NLMS, LMS-Newton, SMI, RLS. The article contains the initial operation algorithms of adaptive antenna arrays, conclusions of equivalent processing algorithms and an equivalent block diagram of the generalized LMS algorithm. Equivalent forms of writing the operation algorithms of adaptive antenna arrays and their parameters are also presented in tabular form. Of particular interest is the equivalent operation algorithm in the case of the SMI algorithm, which differs most from the LMS algorithm. Equivalent algorithms differ only by the scalar convergence coefficient and the matrix normalizing factor. For LMS-Newton, SMI, and RLS algorithms, the matrix normalizing factor is the same, it is determined by inverting the estimation of the correlation matrix of input signals and reduces the dependence of the characteristics of the algorithms on the parameters of the correlation matrix. The scalar convergence coefficient of equivalent algorithms in the case of SMI and RLS algorithms depends on the iteration number and tends to zero for the SMI algorithm and to some non-zero value for the RLS algorithm. The dependence of the convergence coefficient on the iteration number makes it possible to optimize the characteristics of the algorithms at the transition stage. The tendency of the convergence coefficient to zero in the case of the SMI algorithm makes it effective only for stationary input signals. The non-zero steady-state value of the convergence coefficient in the case of the RLS algorithm allows its effective use in a non-stationary environment.

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Li, Meng He, Chuan Lin, Jing Bei Tian, and Sheng Hui Pan. "An Algorithms for Super-Resolution Reconstruction of Video Based on Spatio-Temporal Adaptive." Advanced Materials Research 532-533 (June 2012): 1680–84. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1680.

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For the weakness of conventional POCS algorithms, a novel spatio-temporal adaptive super-resolution reconstruction algorithm of video is proposed in this paper. The spatio-temporal adaptive mechanism, which is based on POCS super-resolution reconstruction algorithm, can effectively prevent reconstructed image from the influence of inaccuracy of motion information and avoid the impact of noise amplification, which exist in using conventional POCS algorithms to reconstruct image sequences in dramatic motion. Experimental results show that the spatio-temporal adaptive algorithm not only effectively alleviate amplification noise but is better than the traditional POCS algorithms in signal to noise ration.

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Zhertunova, T. V., and E. S. Yanakova. "ADAPTIVE ALGORITHM BASED ON NONLOCAL MEANS IN IMAGE PROCESSING." Issues of radio electronics, no. 8 (August 20, 2018): 79–86. http://dx.doi.org/10.21778/2218-5453-2018-8-79-86.

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This article describes the existing problem situation associated with the absence of resource-lights denoising algorithms, capable to produce good-quality output images in the different intensity noise conditions without blurring the boundaries, contours and basic structure. The adaptive algorithm proposed in the article allows to solve this problem due to the developed algorithms of splitting the search region into two sets of similar and points different from the pixel and adapting of the kernel type to the image region, depending on the presence or detection of structural and smooth pixels. The results of the proposed algorithm and the standard method of nonlocal means are compared with the metrics of the peak signal-to-noise ratio and structural similarity. It is found out that the developed adaptive algorithm is surpass by far than the standard method both on numerical results and on the quality of the image processing.

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Zou, Tingting, and Changyu Wang. "Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization." Mathematics 10, no. 7 (April 2, 2022): 1145. http://dx.doi.org/10.3390/math10071145.

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The Harris Hawks optimization (HHO) is a population-based metaheuristic algorithm; however, it has low diversity and premature convergence in certain problems. This paper proposes an adaptive relative reflection HHO (ARHHO), which increases the diversity of standard HHO, alleviates the problem of stagnation of local optimal solutions, and improves the search accuracy of the algorithm. The main features of the algorithm define nonlinear escape energy and adaptive weights and combine adaptive relative reflection with the HHO algorithm. Furthermore, we prove the computational complexity of the ARHHO algorithm. Finally, the performance of our algorithm is evaluated by comparison with other well-known metaheuristic algorithms on 23 benchmark problems. Experimental results show that our algorithms performs better than the compared algorithms on most of the benchmark functions.

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Korkmaz Tan, Rabia, and Şebnem Bora. "Adaptive parameter tuning for agent-based modeling and simulation." SIMULATION 95, no. 9 (June 25, 2019): 771–96. http://dx.doi.org/10.1177/0037549719846366.

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The purpose of this study was to solve the parameter-tuning problem of complex systems modeled in an agent-based modeling and simulation environment. As a good set of parameters is necessary to demonstrate the target behavior in a realistic way, modeling a complex system constitutes an optimization problem that must be solved for systems with large parameter spaces. This study presents a three-step hybrid parameter-tuning approach for agent-based models and simulations. In the first step, the problem is defined; in the second step, a parameter-tuning process is performed using the following meta-heuristic algorithms: the Genetic Algorithm, the Firefly Algorithm, the Particle Swarm Optimization algorithm, and the Artificial Bee Colony algorithm. The critical parameters of the meta-heuristic algorithms used in the second step are tuned using the adaptive parameter-tuning method. Thus, new meta-heuristic algorithms are developed, namely, the Adaptive Genetic Algorithm, the Adaptive Firefly Algorithm, the Adaptive Particle Swarm Optimization algorithm, and the Adaptive Artificial Bee Colony algorithm. In the third step, the control phase, the algorithm parameters obtained via the adaptive parameter-tuning method and the parameter values of the model obtained from the meta-heuristic algorithms are manually provided to the developed tool performing the parameter-tuning process and they are tested. The best results are achieved when the meta-heuristic algorithms that were successful in the optimization process are used with their critical parameters adjusted for optimum results. The proposed approach is tested by using the Predator–Prey model, the Eight Queens model, and the Flow Zombies model, and the results are compared.

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Afendikov, A. L., and V. S. Nikitin. "On Cartesian grids in some adaptive algorithms of aerodynamics." Computational Mathematics and Information Technologies 2 (2017): 180–84. http://dx.doi.org/10.23947/2587-8999-2017-2-180-184.

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Ene, Alina, and Huy Lê Nguyễn. "Adaptive and Universal Algorithms for Variational Inequalities with Optimal Convergence." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6559–67. http://dx.doi.org/10.1609/aaai.v36i6.20609.

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We develop new adaptive algorithms for variational inequalities with monotone operators, which capture many problems of interest, notably convex optimization and convex-concave saddle point problems. Our algorithms automatically adapt to unknown problem parameters such as the smoothness and the norm of the operator, and the variance of the stochastic evaluation oracle. We show that our algorithms are universal and simultaneously achieve the optimal convergence rates in the non-smooth, smooth, and stochastic settings. The convergence guarantees of our algorithms improve over existing adaptive methods and match the optimal non-adaptive algorithms. Additionally, prior works require that the optimization domain is bounded. In this work, we remove this restriction and give algorithms for unbounded domains that are adaptive and universal. Our general proof techniques can be used for many variants of the algorithm using one or two operator evaluations per iteration. The classical methods based on the ExtraGradient/MirrorProx algorithm require two operator evaluations per iteration, which is the dominant factor in the running time in many settings.

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Xin-Xin Zhou, Xin-Xin Zhou, Zhi-Rui Gao Xin-Xin Zhou, and Xue-Ting Yi Zhi-Rui Gao. "An Improved Chicken Swarm Optimization Algorithm Based on Adaptive Mutation Learning Strategy." 電腦學刊 33, no. 6 (December 2022): 001–19. http://dx.doi.org/10.53106/199115992022123306001.

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<p>To solve the problem that the Chicken swarm optimization (CSO) has low solution accuracy and tends to fall into the local optimum on later stages of iteration, an adaptive mutation learning Chicken swarm optimization (AMLCSO) is proposed in this paper. Firstly, to solve the problem of uneven initial distribution and improve the algorithm’s stability, a good-point set is introduced. Secondly, according to the difference between the current individual position and the optimal individual position, the nonlinear adaptive adjustment of weight is realized and the position update step is dynamically adjusted. This strategy improves the algorithm’s convergence. Thirdly, the learning update strategies of Gaussian mutation and normal distribution are introduced to improve the probability of selection and solving accuracy and avoid falling into the local optimum. Finally, the AMLCSO is compared with other standard algorithms and improved Chicken swarm optimization algorithms on twenty benchmark test functions. The experimental results show the AMLCSO has faster convergence and higher solution accuracy.</p> <p> </p>

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Dey, Tonmoy, Yixin Chen, and Alan Kuhnle. "DASH: A Distributed and Parallelizable Algorithm for Size-Constrained Submodular Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 3941–48. http://dx.doi.org/10.1609/aaai.v37i4.25508.

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MapReduce (MR) algorithms for maximizing monotone, submodular functions subject to a cardinality constraint (SMCC) are currently restricted to the use of the linear-adaptive (non-parallelizable) algorithm GREEDY. Low-adaptive algorithms do not satisfy the requirements of these distributed MR frameworks, thereby limiting their performance. We study the SMCC problem in a distributed setting and propose the first MR algorithms with sublinear adaptive complexity. Our algorithms, R-DASH, T-DASH and G-DASH provide 0.316 - ε, 3/8 - ε , and (1 - 1/e - ε) approximation ratios, respectively, with nearly optimal adaptive complexity and nearly linear time complexity. Additionally, we provide a framework to increase, under some mild assumptions, the maximum permissible cardinality constraint from O( n / ℓ^2) of prior MR algorithms to O( n / ℓ ), where n is the data size and ℓ is the number of machines; under a stronger condition on the objective function, we increase the maximum constraint value to n. Finally, we provide empirical evidence to demonstrate that our sublinear-adaptive, distributed algorithms provide orders of magnitude faster runtime compared to current state-of-the-art distributed algorithms.

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Hoang, Le Minh, Aleksandr A. Konovalov, and Dao Van Luc. "Tracking of Maneuvering Targets Using a Variable Structure Multiple Model Algorithm." Journal of the Russian Universities. Radioelectronics 26, no. 3 (July 6, 2023): 77–89. http://dx.doi.org/10.32603/1993-8985-2023-26-3-77-89.

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Introduction. In recent years, much attention has been paid to the development of trajectory filtering methods for tracking maneuvering targets. Multi-model (MM) algorithms are widely used for filtering maneuvering targets. Conventional MM algorithms are characterized by a fixed structure. However, highly maneuvering targets require a sufficiently large set of models covering the entire range of possible maneuvers, although an increase in the number of models cannot ensure an increase in the accuracy of tracking. To overcome these problems, multiple model algorithms with a variable structure (VSMM) were proposed. This article proposes two VSMM algorithms for tracking maritime targets performing a coordinated turn at constant speed. These are algorithms with a variable set of models based on adaptive grid and switching grid methods.Aim. To develop an adaptive trajectory tracking algorithm that uses a constant turn model to track maneuvering surface objects.Materials and methods. The resulting algorithm is based on the theory of grid adaptation in multi-model estimation methods and is used to estimate the components of the coordinate and velocity vectors of surface maneuvering targets. The algorithm efficiency was evaluated using computer statistical modeling in the MATLAB environment.Results. The structure of an adaptive VSMM algorithm was described. Simulations were carried out to confirm the algorithm efficiency. In the considered simulation scenarios, the algorithm produces effective estimates of the coordinate vectors and speed of surface maneuvering targets.Conclusion. Adaptive algorithms improve the efficiency of target tracking in comparison with multi-model algorithms with a fixed structure, at the same time as saving computational resources.

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Trabia, Mohamed B., and Xiao Bin Lu. "A Fuzzy Adaptive Simplex Search Optimization Algorithm." Journal of Mechanical Design 123, no. 2 (October 1, 1999): 216–25. http://dx.doi.org/10.1115/1.1347991.

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Most optimization algorithms use empirically-chosen fixed parameters as a part of their search strategy. This paper proposes to replace these fixed parameters by adaptive ones to make the search more responsive to changes in the problem by incorporating fuzzy logic in optimization algorithms. The proposed ideas are used to develop a new adaptive form of the simplex search algorithm whose objective is to minimize a function of n variables. The new algorithm is labeled Fuzzy Simplex. The search starts by generating a simplex with n+1 vertices. The algorithm then repeatedly replaces the point with the highest function value by a new point. This process has three components: reflecting the point with the highest function value, expanding, and contracting the simplex. These operations use fuzzy logic controllers whose inputs incorporate the relative weights of the function values at the simplex points. Standard minimization test problems are used to evaluate the efficiency of the algorithm. The Fuzzy Simplex algorithm generally results in a faster convergence. Robustness and sensitivity of the algorithm are also considered. The Fuzzy Simplex algorithm is also applied successfully to several engineering design problems. The results of the Fuzzy Simplex algorithm compare favorably with other available minimization algorithms.

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Agrawal, Anmol, B. K. Tripathy, and Ramkumar Thirunavukarasu. "An Improved Fuzzy Adaptive Firefly Algorithm-Based Hybrid Clustering Algorithms." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, Supp02 (December 2021): 259–78. http://dx.doi.org/10.1142/s0218488521400146.

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The implication of firefly and fuzzy firefly optimization algorithms has been greatly witnessed in clustering techniques and extensively used in applications such as Image segmentation. Parameters such as step factor and attractiveness have been kept constant in these algorithms, which affect the convergence rate and accuracy of the clustering process. Though fuzzy adaptive firefly algorithm tackled this problem by making those parameters an adaptive one, issues such as low convergence rate, and provision of non-optimal solutions are still there. To tackle these issues, this paper proposed a novel fuzzy adaptive fuzzy firefly algorithm that significantly improves the accuracy and convergence rate while comparing with the existing optimization algorithms. Further, the proposed algorithm fused with existing hybrid clustering algorithms involving fuzzy set, intuitionistic fuzzy set, and rough set resulted in eight novel hybrid clustering algorithms which lead to better performance in optimizing the selection of initial centroids. To validate the proposal, experimental studies have been conducted on datasets found in bench-marking data repositories such as UCI, and Kaggle. The performance and accuracy evaluation of proposed algorithms have been carried out with the aid of seven accuracy measures. Results clearly indicate the improved accuracy and convergence rate of the proposed algorithms.

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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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Chen, Yuan Yuan, Run Jie Liu, Jin Yuan Shen, and Dan Dan He. "The Use of Adaptive Algorithms on Smart Antenna Device." Advanced Materials Research 548 (July 2012): 730–34. http://dx.doi.org/10.4028/www.scientific.net/amr.548.730.

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Adaptive beamforming is one of the core technology of the smart antenna system. Two different adaptive algorithms which adopt the minimum mean square algorithm (LMS) and recursive least squares algorithm (RLS) are employed to realize the beamforming in smart antenna system. The smart antenna system based on LMS and RLS is simulated and realized by the MATLAB software in which a uniform linear adaptive antenna array is used. The results show that the smart antenna systems based on RLS and LMS algorithms can significantly reduce the bit error rate especially with the low SNR.

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SMYTH, W. F., and SHU WANG. "AN ADAPTIVE HYBRID PATTERN-MATCHING ALGORITHM ON INDETERMINATE STRINGS." International Journal of Foundations of Computer Science 20, no. 06 (December 2009): 985–1004. http://dx.doi.org/10.1142/s0129054109007005.

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We describe a hybrid pattern-matching algorithm that works on both regular and indeterminate strings. This algorithm is inspired by the recently proposed hybrid algorithm FJS and its indeterminate successor. However, as discussed in this paper, because of the special properties of indeterminate strings, it is not straightforward to directly migrate FJS to an indeterminate version. Our new algorithm combines two fast pattern-matching algorithms, ShiftAnd and BMS (the Sunday variant of the Boyer-Moore algorithm), and is highly adaptive to the nature of the text being processed. It avoids using the border array, therefore avoids some of the cases that are awkward for indeterminate strings. Although not always the fastest in individual test cases, our new algorithm is superior in overall performance to its two component algorithms — perhaps a general advantage of hybrid algorithms.

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Wu, Xidong, Feihu Huang, Zhengmian Hu, and Heng Huang. "Faster Adaptive Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 10379–87. http://dx.doi.org/10.1609/aaai.v37i9.26235.

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Federated learning has attracted increasing attention with the emergence of distributed data. While extensive federated learning algorithms have been proposed for the non-convex distributed problem, the federated learning in practice still faces numerous challenges, such as the large training iterations to converge since the sizes of models and datasets keep increasing, and the lack of adaptivity by SGD-based model updates. Meanwhile, the study of adaptive methods in federated learning is scarce and existing works either lack a complete theoretical convergence guarantee or have slow sample complexity. In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on the momentum-based variance reduced technique in cross-silo FL. We first explore how to design the adaptive algorithm in the FL setting. By providing a counter-example, we prove that a simple combination of FL and adaptive methods could lead to divergence. More importantly, we provide a convergence analysis for our method and prove that our algorithm is the first adaptive FL algorithm to reach the best-known samples O(epsilon(-3)) and O(epsilon(-2)) communication rounds to find an epsilon-stationary point without large batches. The experimental results on the language modeling task and image classification task with heterogeneous data demonstrate the efficiency of our algorithms.

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Voskoboinikov, Yuri E. "А locally adaptive wavelet filtering algorithm for images." Analysis and data processing systems, no. 1 (March 29, 2023): 25–36. http://dx.doi.org/10.17212/2782-2001-2023-1-25-36.

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The algorithms based on the decomposition of a noisy image in an orthogonal basis of wavelet functions have been widely used to filter images (especially contrasting ones) over the past four decades. In this case, most wavelet filtering algorithms are of a threshold nature, namely: the decomposition coefficient smaller in an absolute value of a certain threshold value is reset to zero; otherwise the coefficient undergoes some (most often nonlinear) transformation. A certain (and very significant) drawback of threshold algorithms is that all coefficients of a certain decomposition level are processed with one identical threshold value (i.e., a constant value for all de-composition coefficients). This does not allow taking into account the “individual energy” of each decomposition coefficient for its more optimal processing. Therefore, we propose its own filtering factor for each coefficient, built on the basis of the optimal Wiener filtering and where a filtering parameter is introduced to compensate for incomplete a priori information on the value of the processed decomposition coefficients. In order to select a filtering parameter, a statistical approach has been proposed that makes it possible to estimate the optimal value of this parameter with acceptable accuracy. The performed computational experiment has shown the developed algorithm effectiveness for wavelet filtering of images.

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Chen, Tsung-Yi, Hui-Chuan Chu, Yuh-Min Chen, and Kuan-Chun Su. "Ontology-based Adaptive Dynamic e-Learning Map Planning Method for Conceptual Knowledge Learning." International Journal of Web-Based Learning and Teaching Technologies 11, no. 1 (January 2016): 1–20. http://dx.doi.org/10.4018/ijwltt.2016010101.

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E-learning improves the shareability and reusability of knowledge, and surpasses the constraints of time and space to achieve remote asynchronous learning. Since the depth of learning content often varies, it is thus often difficult to adjust materials based on the individual levels of learners. Therefore, this study develops an ontology-based adaptive dynamic knowledge concept e-learning mechanism that generates learning maps based on learner characteristics and guides learners effectively. To achieve this goal, this study proposes an adaptive dynamic concept e-learning navigation procedure, designs learning models based on the adaptive learning needs of learners, and develops knowledge map model and learning map model. Finally, this study designs adaptive dynamic concept learning map-planning algorithms based on the particle swarm optimization (PSO) algorithm. The learning maps generated by these algorithms meet the dynamic needs of learners by continually adjusting and modifying the learning map throughout the learning process. Adapting the adaptive learning content according to the dynamic needs of learners allows learners to receive more instruction in a limited period.

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Hernandez, Carlos, and Jorge Baier. "Real-Time Adaptive A∗ with Depression Avoidance." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, no. 1 (October 9, 2011): 146–51. http://dx.doi.org/10.1609/aiide.v7i1.12455.

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RTAA* is probably the best-performing real-time heuristic search algorithm at path-finding tasks in which the environ- ment is not known in advance or in which the environment is known and there is no time for pre-processing. As most real- time search algorithms do, RTAA∗ performs poorly in presence of heuristic depressions, which are bounded areas of the search space in which the heuristic is too low with respect to their border. Recently, it has been shown that LSS-LRTA∗, a well-known real-time search algorithm, can be improved when search is actively guided away of depressions. In this paper we investigate whether or not RTAA∗ can be improved in the same manner. We propose aRTAA∗ and daRTAA∗, two algorithms based on RTAA∗ that avoid heuristic depressions. Both algorithms outperform RTAA∗ on standard path-finding tasks, obtaining better-quality solutions when the same time deadline is imposed on the duration of the planning episode. We prove, in addition, that both algorithms have good theoretical properties

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Crutchfield, William Y., and Michael L. Welcome. "Object-Oriented Implementation of Adaptive Mesh Refinement Algorithms." Scientific Programming 2, no. 4 (1993): 145–56. http://dx.doi.org/10.1155/1993/838429.

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We describe C++ classes that simplify development of adaptive mesh refinement (AMR) algorithms. The classes divide into two groups, generic classes that are broadly useful in adaptive algorithms, and application-specific classes that are the basis for our AMR algorithm. We employ two languages, with C++ responsible for the high-level data structures, and Fortran responsible for low-level numerics. The C++ implementation is as fast as the original Fortran implementation. Use of inheritance has allowed us to extend the original AMR algorithm to other problems with greatly reduced development time.

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Mandic, Danilo P. "Data-Reusing Recurrent Neural Adaptive Filters." Neural Computation 14, no. 11 (November 1, 2002): 2693–707. http://dx.doi.org/10.1162/089976602760408026.

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A class of data-reusing learning algorithms for real-time recurrent neural networks (RNNs) is analyzed. The analysis is undertaken for a general sigmoid nonlinear activation function of a neuron for the real time recurrent learning training algorithm. Error bounds and convergence conditions for such data-reusing algorithms are provided for both contractive and expansive activation functions. The analysis is undertaken for various configurations that are generalizations of a linear structure infinite impulse response adaptive filter.

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Ye, Chen, Guan Gui, Shin-ya Matsushita, and Li Xu. "Block Sparse Signal Reconstruction Using Block-Sparse Adaptive Filtering Algorithms." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 7 (December 20, 2016): 1119–26. http://dx.doi.org/10.20965/jaciii.2016.p1119.

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Sparse signal reconstruction (SSR) problems based on compressive sensing (CS) arise in a broad range of application fields. Among these are the so-called “block-structured” or “block sparse” signals with nonzero atoms occurring in clusters that occur frequently in natural signals. To make block-structured sparsity use more explicit, many block-structure-based SSR algorithms, such as convex optimization and greedy pursuit, have been developed. Convex optimization algorithms usually pose a heavy computational burden, while greedy pursuit algorithms are overly sensitive to ambient interferences, so these two types of block-structure-based SSR algorithms may not be suited for solving large-scale problems in strong interference scenarios. Sparse adaptive filtering algorithms have recently been shown to solve large-scale CS problems effectively for conventional vector sparse signals. Encouraged by these facts, we propose two novel block-structure-based sparse adaptive filtering algorithms, i.e., the “block zero attracting least mean square” (BZA-LMS) algorithm and the “block&ell;0-norm LMS” (BL0-LMS) algorithm, to exploit their potential performance gain. Experimental results presented demonstrate the validity and applicability of these proposed algorithms.

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Dogariu, Laura-Maria, Cristian-Lucian Stanciu, Camelia Elisei-Iliescu, Constantin Paleologu, Jacob Benesty, and Silviu Ciochină. "Tensor-Based Adaptive Filtering Algorithms." Symmetry 13, no. 3 (March 15, 2021): 481. http://dx.doi.org/10.3390/sym13030481.

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Tensor-based signal processing methods are usually employed when dealing with multidimensional data and/or systems with a large parameter space. In this paper, we present a family of tensor-based adaptive filtering algorithms, which are suitable for high-dimension system identification problems. The basic idea is to exploit a decomposition-based approach, such that the global impulse response of the system can be estimated using a combination of shorter adaptive filters. The algorithms are mainly tailored for multiple-input/single-output system identification problems, where the input data and the channels can be grouped in the form of rank-1 tensors. Nevertheless, the approach could be further extended for single-input/single-output system identification scenarios, where the impulse responses (of more general forms) can be modeled as higher-rank tensors. As compared to the conventional adaptive filters, which involve a single (usually long) filter for the estimation of the global impulse response, the tensor-based algorithms achieve faster convergence rate and tracking, while also providing better accuracy of the solution. Simulation results support the theoretical findings and indicate the advantages of the tensor-based algorithms over the conventional ones, in terms of the main performance criteria.

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Kaznacheeva, E. S., V. M. Kuz’kin, G. A. Lyakhov, S. A. Pereselkov, and S. A. Tkachenko. "Adaptive Algorithms for Interferometric Processing." Physics of Wave Phenomena 28, no. 3 (July 2020): 267–73. http://dx.doi.org/10.3103/s1541308x20030103.

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Journal articles on the topic 'Adaptive algorithms'

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