Journal articles on the topic 'Adaptive algorithm'
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Litvinchuk, Yuliia. "Self-adaptive CMA-ES Algorithm." Mathematical and computer modelling. Series: Physical and mathematical sciences 24 (December 5, 2023): 81–90. http://dx.doi.org/10.32626/2308-5878.2023-24.81-90.
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This article will consider one of the self-adaptive algorithms for selecting parameters of complex systems, examples of which are neural networks. Self-adaptive algorithms are algorithms that change their behavior at runtime based on available information and predetermined reward mechanisms. These algorithms are widely used in various fields, including machine learning, optimization, and data compression. The self-adaptiveness of the algorithm in this case will be based on the selection of the number of peaks in the mixture of distributions in the extended CMA-ES algorithm under the condition of a normal base distribution. The work presents an improved self-adaptive CMA-ES algorithm, with an emphasis on the parameter that selects the number of pixels in a mixture of normal distributions. The algorithm takes into account the methods of setting this optimal value, which is used when choosing cluster numbers in the CURE, BIRCH, etc. clustering algorithms. It is obvious that the given justification of this approach can be extended to mixtures with a different base distribution, each of which is characterized by a skin number of peaks in the mixture distribution. This implies self-adaptability and applicability of the algorithm to a wider range of scenarios involving different distribution characteristics. There is no doubt that the proposed sado-adaptive parameter setting algorithm, based on the CMA-ES algorithm, can be extended to other genetic and evolutionary algorithms that include the selection of additional chromosomes (individuals) during the transition between iteration epochs of the algorithm. Another feature of the proposed algorithm is the use of theoretical foundations of cluster analysis to estimate the number of peaks in the distribution of chromosomes. This approach is widely used in the latest self-adaptive algorithms for determining the initial parameters (hyperparameters) of complex systems
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Zhang, Zhaoxia. "Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm." International Journal of Intelligent Information Technologies 20, no. 1 (May 17, 2024): 1–19. http://dx.doi.org/10.4018/ijiit.344024.
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Multistage testing (MST) is a portion of computational adaptive testing that adapts assessment structure at the sublevel rather than the component level. The goal of the MST algorithm is to identify bugs in computer programming, and there is a significant cost to utilising MST due to its decreased versatility during software development and maintenance. The efficiency of most algorithms drastically reduces for adaptive MST with complex feasible regions, while some modern algorithms function well while tackling computerised MST with a basic practicable range. The study offers an automated Adaptive Multistage Testing algorithm based on Adaptive Genetic Algorithm (AMST-AGA) for optimisation and scalability problems, in which constraints are successively introduced and dealt with at various evolutionary phases. In this paper, many test cases will aid in finding bugs and meeting completeness goals. Each time test cases are created, these testing scenarios must continue to pass.
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Chen, Wei, Binghui Peng, Grant Schoenebeck, and Biaoshuai Tao. "Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 590–97. http://dx.doi.org/10.1609/aaai.v34i01.5398.
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We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1-1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a (1-1/e) fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the independent cascade model and the linear threshold model. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a (1-1/e)-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular cascade model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.
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Chen, Wei, Binghui Peng, Grant Schoenebeck, and Biaoshuai Tao. "Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization." Journal of Artificial Intelligence Research 74 (May 26, 2022): 303–51. http://dx.doi.org/10.1613/jair.1.12997.
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We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1 − 1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a (1−1/e) fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the independent cascade model and the linear threshold model. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a (1 − 1/e)-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular diffusion model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.
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O'Malley, Lawrence V. "Adaptive clustering algorithm." IBM Journal of Research and Development 29, no. 1 (January 1985): 68–72. http://dx.doi.org/10.1147/rd.291.0068.
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Kusuma, Purba Daru, and Meta Kallista. "Adaptive Cone Algorithm." International Journal on Advanced Science, Engineering and Information Technology 13, no. 5 (October 31, 2023): 1605. http://dx.doi.org/10.18517/ijaseit.13.5.18284.
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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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Xi, Zichen. "Analysis of Adaptive Equalization Algorithms." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 295–305. http://dx.doi.org/10.54097/hset.v70i.12477.
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Adaptive equalization algorithms play a pivotal role in suppressing inter-symbol interference in wireless channels. Contemporarily, with the rapid development of science and technology, there is still a lack of unified cognition for adaptive equalization algorithms. Therefore, this study systematically discusses the research status and development process of adaptive equalization algorithms, focusing on the least mean square algorithm (LMS), constant modulus blind equalization algorithm (CMA) and neural network algorithm. Subsequently, based on Matlab simulation, their performance is analyzed visually. Finally, a table is listed to compare the three commonly used algorithms. From the aspects of practicability and application environment, it deeply analyzes the limitations of traditional adaptive equalization algorithms such as LMS and CMA in the current era, and demonstrates the superior performance of neural networks. On this basis, this paper emphasizes the powerful learning ability of neural networks and the opportunities for future research, which will lay the foundation for the development of next-generation communication networks.
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Kobayashi, Masaki, and Yasunori Nagasaka. "Equivalency of SSCF Adaptive Algorithm to Noise Free LMS Adaptive Algorithm." IEEJ Transactions on Electronics, Information and Systems 133, no. 6 (2013): 1173–77. http://dx.doi.org/10.1541/ieejeiss.133.1173.
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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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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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Nariman, Goran Saman, and Hamsa D. Majeed. "Adaptive Filter based on Absolute Average Error Adaptive Algorithm for Modeling System." UHD Journal of Science and Technology 6, no. 1 (May 7, 2022): 60–69. http://dx.doi.org/10.21928/uhdjst.v6n1y2022.pp60-69.
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Adaptive identification of the bandpass finite impulse response (FIR) filtering system is proposed through this paper using variable step-size least mean square (VSS-LMS) algorithm called absolute average error-based adjusted step-size LMS as an adapted algorithm. This algorithm used to design an adaptive FIR filter by calculating the absolute averaged value for the recently assessed error with the previous one. Then, the step size has been attuned accordingly with consideration of the slick transition of the step size from bigger to smaller to score an achievement through high convergence rate and low steady-state misadjustment over the other algorithms used for the same purpose. The simulation results through the computer demonstrate remarkable performance compared to the traditional algorithm of LMS and another VSS-LMS algorithm (normalized LMS) which used in this paper for the designed filter. The powerful of the filter has been served in the identification system, bandpass filter has been chosen to be identified in the proposed adaptive system identification. It reports conceivable enhancements in the modeling system concerning the time of convergence, which is well-defined as a fast and steady-state adjustment defined with a low level. The designed filter identified the indefinite system with less than 10 samples; meanwhile, other algorithms were taking more than 20 samples for identification. Alongside the fine behavior of preserving the tradeoff between miss adjustment and the capability of tracking, the fewer calculations and computations regarding the algorithm requirement make the applied real-time striking.
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楊恙, 楊恙, Xukun Zuo Yang Yang, Maosheng Fu Xukun Zuo, Shuhao Yu Maosheng Fu, and Chaochuan Jia Shuhao Yu. "Adaptive Cuckoo Search Algorithm Based on Dynamic Adjustment Mechanism." 電腦學刊 32, no. 5 (October 2021): 171–83. http://dx.doi.org/10.53106/199115992021103205014.
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Cuckoo Search (CS) algorithm, a simple and effective global optimization algorithm, has been widely used to deal with practical optimization problems. So as to improvethe standard cuckoo search algorithm, such as slow convergence and easy convergence to local optimal value, an Adaptive Cuckoo Search algorithm on the basis of Dynamic Adjustment Mechanism (ACSDAM) has been proposed. Based on exponential function and logarithmic function, the dynamic adjustment is made for updating step size and discovering probability. During the optimization process, updating step size and discovering probability of each nest are adjusted according to the number of iterations of each nest, so as to equilibrate the global detection and local capacity of the algorithm. Then 23 standard test functions will be selected for a simulation experiment, and compared with other CS variant algorithms, ACS-DAM effectively improved the rate of convergence and the algorithmic precision. ACS-DAM algorithm was employed to optimize the Support Vector Machine (SVM). The experiment proves that the convergence rate with ACSDAM is better than that with CS obviously and ACS-DAM has stronger optimization ability and higher efficiency than CS.
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Chen, Yi Rui, and Yi Zhuang. "An Adaptive Decision Concurrency Control Algorithm." Advanced Materials Research 1046 (October 2014): 512–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.512.
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For the lack of adaptability about the existing concurrency control algorithms, adaptive decision concurrency control algorithm is proposed. ADCC algorithm divides concurrency control process into two phases in: execution authorizing phase and strategy selecting phase. In execution authorizing phase, algorithm compares statistics and effectiveness of transactions to determine the execution order of conflict transactions. In strategy selecting phase, according to transactions’ read/write status and current conflict rate, algorithm selects optimistic/pessimistic conflict resolution strategy adaptively. Such selection mechanism makes ADCC algorithm have high efficiency no matter database system is busy or idle. Simulation experiment proves that ADCC algorithm this paper proposed is superior to classical strict two phases locking algorithm and hybrid concurrency control. So ADCC algorithm performs well in the period of concurrency control.
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Peng, Shuo, A. J. Ouyang, and Jeff Jun Zhang. "An Adaptive Invasive Weed Optimization Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 02 (February 27, 2015): 1559004. http://dx.doi.org/10.1142/s0218001415590041.
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With regards to the low search accuracy of the basic invasive weed optimization algorithm which is easy to get into local extremum, this paper proposes an adaptive invasive weed optimization (AIWO) algorithm. The algorithm sets the initial step size and the final step size as the adaptive step size to guide the global search of the algorithm, and it is applied to 20 famous benchmark functions for a test, the results of which show that the AIWO algorithm owns better global optimization search capacity, faster convergence speed and higher computation accuracy compared with other advanced algorithms.
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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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Jeyanthi, K. Meena alias, and A. P. Kabilan. "A Simple Adaptive Beamforming Algorithm with interference Suppression." International Journal of Engineering and Technology 1, no. 1 (2009): 67–70. http://dx.doi.org/10.7763/ijet.2009.v1.12.
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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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Daumont, Steredenn, and Daniel Le Guennec. "An Analytical Multimodulus Algorithm for Blind Demodulation in a Time-Varying MIMO Channel Context." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/307927.
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This paper addresses the issue of blind multiple-input multiple-output (MIMO) demodulation of communication signals, with time-varying channels and in an interception context. A new adaptive-blind source separation algorithm, which is based on the implementation of the Multimodulus cost function by analytical methods, is proposed. First a batch processing analysis is performed; then an adaptive implementation of the (Analytical Multi-Modulus Algorithm) AMMA and its simplified version named (Analytical Simplified Constant Modulus Algorithm) ASCMA is detailed. These algorithms, named adaptive-AMMA and adaptive-ASCMA, respectively, are compared with the adaptive (Analytical Constant Modulus Algorithm) ACMA and the MMA (Multi-Modulus Algorithm). The adaptive-AMMA and adaptive-ASCMA achieve a lower residual intersymbol interference and bit error rate than those of the adaptive-ACMA and MMA.
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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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Ouyang, Chengtian, Yaxian Qiu, and Donglin Zhu. "Adaptive Spiral Flying Sparrow Search Algorithm." Scientific Programming 2021 (August 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/6505253.
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The sparrow search algorithm is a new type of swarm intelligence optimization algorithm with better effect, but it still has shortcomings such as easy to fall into local optimality and large randomness. In order to solve these problems, this paper proposes an adaptive spiral flying sparrow search algorithm (ASFSSA), which reduces the probability of getting stuck into local optimum, has stronger optimization ability than other algorithms, and also finds the shortest and more stable path in robot path planning. First, the tent mapping based on random variables is used to initialize the population, which makes the individual position distribution more uniform, enlarges the workspace, and improves the diversity of the population. Then, in the discoverer stage, the adaptive weight strategy is integrated with Levy flight mechanism, and the fusion search method becomes extensive and flexible. Finally, in the follower stage, a variable spiral search strategy is used to make the search scope of the algorithm more detailed and increase the search accuracy. The effectiveness of the improved algorithm ASFSSA is verified by 18 standard test functions. At the same time, ASFSSA is applied to robot path planning. The feasibility and practicability of ASFSSA are verified by comparing the algorithms in the raster map planning routes of two models.
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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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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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Volarić, Ivan, and Victor Sucic. "Adaptive thresholding for sparse image reconstruction." Telfor Journal 15, no. 1 (2023): 8–13. http://dx.doi.org/10.5937/telfor2301008v.
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The performance of the class of sparse reconstruction algorithms which is based on the iterative thresholding is highly dependent on a selection of the appropriate threshold value, controlling a trade-off between the algorithm execution time and the solution accuracy. This is why most of the state-of-the-art reconstruction algorithms employ some method of decreasing the threshold value as the solution converges toward the optimal one. To address this problem we propose a data-driven adaptive threshold selection method based on the fast intersection of confidence intervals (FICI) method, with which we have augmented the two-step iterative shrinkage thresholding (TwIST) algorithm. The performance of the proposed algorithm, denoted as the FICI-TwIST algorithm, has been evaluated on a problem of image reconstruction with the missing pixels, exploiting image sparsity in the discrete cosine transformation domain. The obtained results have shown competitive performance in comparison with a number of state-of-the-art sparse reconstruction algorithms, even outperforming them in some scenarios.
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Zhang, Yuhao. "Adaptive block level bilateral filtering algorithm." Applied and Computational Engineering 17, no. 1 (October 23, 2023): 77–85. http://dx.doi.org/10.54254/2755-2721/17/20230917.
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During the acquisition or transmission process, video images are subject to random signal interference and generate noise, which can hinder people's understanding of the image and subsequent processing work. Therefore, it is necessary to study video image denoising and filtering algorithms. Bilateral filter is one of many typical video image filtering algorithms. However, the traditional bilateral filter algorithm does not consider the differences in the contents of different regions of the image. It is difficult to obtain the optimal filtering effect by using a fixed filtering weight to filter the entire image, which leads to problems such as blurred image edges and inadequate details processing. This paper studies the influence of different filter block sizes on the bilateral filter effect, and proposes an algorithm to adaptively update the bilateral filter weight according to the block's variance. The experimental result shows that the performance of adaptive bilateral filter with different block sizes is expected to be better than that of traditional algorithms with fixed filter weights.
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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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Mangaonkar, Abhinandan P., Karuna C. Gull, Sushiladevi Vantamuri, Arpita Patil, and Jaya M. Pattanshetti. "Adaptive Energy-Optimized Consolidation Algorithm." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (May 17, 2023): 150–58. http://dx.doi.org/10.17762/ijritcc.v11i5s.6639.
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We have been hearing about cloud computing for quite a long time now. This type of computing is booming and emerging as a popular computing paradigm for its scalability and flexibility in nature. Cloud computing provides the provision of service on-demand, on-demand resources supply and services to end-users. However, energy consumption and energy wastage are becoming a major concern for cloud providers due to its direct impression on costs required for operations and carbon emissions. To tackle this issue, Adaptive Energy-Optimized Consolidation Algorithm has been proposed to efficiently manage energy consumption in cloud environments. This algorithm involves sharing by dividing, in this process resource allocation is done into two different phases, those are, consolidation of tasks and consolidation of resources. Compared to single-task consolidation algorithms, the proposed two-phase Adaptive energy optimized consolidation algorithm shows improved performance in terms of energy efficiency and resource utilization. The results of experiments conducted using a cloud-sim show the effectiveness of the proposed algorithm in decreasing energy consumption while maintaining the quality-of-service requirements of computing in cloud.
The need for an hour is to automate things without human intervention. Thus, using Autonomous computing refers to a type of computing system that is capable of performing tasks and making decisions without the intervention of humans. This type of system typically relies on Artificial.Intelligence, Machine.Learning, and other futuristic technologies to study the data, identify patterns, and make decisions based on that data. Cloud computing can certainly be incorporated into an autonomous computing system. The performance of an automated computing environment depends on a various factor, considering the quality of the different algorithms used, also the amount and quality of various data available to the system, the computational resources available, and the system's ability to learn and adapt over time. However, by incorporating cloud computing, an autonomous computing system can potentially access more resources and process data more quickly, which can improve its overall performance.
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MAYYAS, K. "ANALYSIS OF THE TRANSFORM DOMAIN LMS ALGORITHM WITH INSUFFICIENT LENGTH ADAPTIVE FILTER." Journal of Circuits, Systems and Computers 14, no. 03 (June 2005): 469–81. http://dx.doi.org/10.1142/s0218126605002441.
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Though, in most practical applications, the length of the adaptive filter is less than that of the unknown system impulse response, analysis of adaptive filtering algorithms almost always assumed a sufficient length adaptive filter whose length is equal to that of unknown system. Theoretical results on the sufficient length adaptive algorithm do not necessarily apply to the realistic insufficient length case and, therefore, it becomes extremely desirable for practical purposes that we quantify the statistical behavior of the insufficient length adaptive algorithm. In this paper, we analyze the popular Transform Domain LMS (TDLMS) algorithm with insufficient length adaptive filter for Gaussian input data and using the common independence assumption. Analysis yields exact theoretical expressions that describe the mean and mean-square convergence of the algorithm, which lead to a better understanding to the performance properties of the insufficient length TDLMS adaptive algorithm. Simulation experiments illustrate the accuracy of the theoretical results in predicting the convergence behavior of the algorithm.
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Haario, Heikki, Eero Saksman, and Johanna Tamminen. "An Adaptive Metropolis Algorithm." Bernoulli 7, no. 2 (April 2001): 223. http://dx.doi.org/10.2307/3318737.
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Linovich, A. Yu, V. S. Litvinova, and M. D. Korolev. "COMB ADAPTIVE FILTERING ALGORITHM." Vestnik of Ryazan State Radio Engineering University 77 (2021): 3–16. http://dx.doi.org/10.21667/1995-4565-2021-77-3-16.
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The problem of multipath channel frequency response equalization in a receiver is considered. The aim is to develop an algorithm of comb adaptive filtering, which makes possible, on the one hand, to provide high rate of multirate receiver system adaptation, and on the other hand, to reduce computational complexities of real-time processing. The robustness analysis of the suggested algorithm is carried out. Two variants of comb adaptive filter are studied. For the second one a fast modification is proposed. On the assumption of multichannel communication system equalizer realization the developed algorithm is able to provide the advantage in rate almost by a factor of ten and at the same time to reduce computational complexities by a factor of three as compared with the least-mean-square algorithm as the experimental results demonstrate.
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FUJII, Kensaku, and Mitsuji MUNEYASU. "Reconsideration of Adaptive Algorithm." IEICE ESS Fundamentals Review 8, no. 4 (2015): 292–313. http://dx.doi.org/10.1587/essfr.8.292.
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Maheshwari, J., and N. V. George. "Polynomial sparse adaptive algorithm." Electronics Letters 52, no. 25 (December 2016): 2063–65. http://dx.doi.org/10.1049/el.2016.3747.
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SASTRI, T. "An Adaptive Estimation Algorithm." IIE Transactions 20, no. 2 (June 1988): 176–85. http://dx.doi.org/10.1080/07408178808966167.
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Kresoja, Milena, Zorana Lužanin, and Irena Stojkovska. "Adaptive stochastic approximation algorithm." Numerical Algorithms 76, no. 4 (February 27, 2017): 917–37. http://dx.doi.org/10.1007/s11075-017-0290-4.
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Kelly, E. J. "An Adaptive Detection Algorithm." IEEE Transactions on Aerospace and Electronic Systems AES-22, no. 2 (March 1986): 115–27. http://dx.doi.org/10.1109/taes.1986.310745.
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Moreno, Jaime A., Daniel Y. Negrete, Victor Torres-González, and Leonid Fridman. "Adaptive continuous twisting algorithm." International Journal of Control 89, no. 9 (December 8, 2015): 1798–806. http://dx.doi.org/10.1080/00207179.2015.1116713.
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Maday, Y., and O. Mula. "An adaptive parareal algorithm." Journal of Computational and Applied Mathematics 377 (October 2020): 112915. http://dx.doi.org/10.1016/j.cam.2020.112915.
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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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Chiheb, Amira, and Hassina Khelladi. "Performance Comparison of LMS and RLS Algorithms for Ambient Noise Attenuation." International Journal of Electrical and Computer Engineering Research 4, no. 1 (March 15, 2024): 14–19. http://dx.doi.org/10.53375/ijecer.2024.383.
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The aim of this study is to implement two different types of adaptive algorithms for the noise cancellation. The study explores the well-known least mean squares (LMS) adaptive algorithm, which is based on stochastic gradient descent approach, and its performances in terms of noise attenuation level and swiftness in active noise control (ANC). Another algorithm is considered in this investigation based upon the use of the least squares estimation (LSE), commonly named, the recursive least squares algorithm (RLS), and will be compared to the LMS. In order to evaluate the potential of each one, a few simulations are achieved. The numerical experiments are performed by using several real recordings of different environment noises tested on the two proposed adaptive algorithms. A comparison is emphasized regarding noise suppression ability and convergence speed, by implementing both adaptive algorithms on the same noise sources. From this numerical study, the RLS algorithm reveals a faster convergence speed and better control performances than the LMS algorithm.
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Wang, Zhenwu, Chao Qin, Benting Wan, William Wei Song, and Guoqiang Yang. "An Adaptive Fuzzy Chicken Swarm Optimization Algorithm." Mathematical Problems in Engineering 2021 (March 1, 2021): 1–17. http://dx.doi.org/10.1155/2021/8896794.
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The chicken swarm optimization (CSO) algorithm is a new swarm intelligence optimization (SIO) algorithm and has been widely used in many engineering domains. However, there are two apparent problems with the CSO algorithm, i.e., slow convergence speed and difficult to achieve global optimal solutions. Aiming at attacking these two problems of CSO, in this paper, we propose an adaptive fuzzy chicken swarm optimization (FCSO) algorithm. The proposed FCSO uses the fuzzy system to adaptively adjust the number of chickens and random factors of the CSO algorithm and achieves an optimal balance of exploitation and exploration capabilities of the algorithm. We integrate the cosine function into the FCSO to compute the position update of roosters and improve the convergence speed. We compare the FCSO with eight commonly used, state-of-the-art SIO algorithms in terms of performance in both low- and high-dimensional spaces. We also verify the FCSO algorithm with the nonparametric statistical Friedman test. The results of the experiments on the 30 black-box optimization benchmarking (BBOB) functions demonstrate that our FCSO outperforms the other SIO algorithms in both convergence speed and optimization accuracy. In order to further test the applicability of the FCSO algorithm, we apply it to four typical engineering problems with constraints on the optimization processes. The results show that the FCSO achieves better optimization accuracy over the standard CSO algorithm.
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Bagirov, Adil, Sona Taheri, and Burak Ordin. "AN ADAPTIVE 𝑘-MEDIANS CLUSTERING ALGORITHM." Problems of Information Technology 13, no. 2 (July 6, 2022): 3–15. http://dx.doi.org/10.25045/jpit.v13.i2.01.
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A new version of the k-medians algorithm, the adaptive k-medians algorithm, is introduced to solve clustering problems with the similarity measure defined using the L1-norm. The proposed algorithm first calculates the center of the whole data set as its median. To solve the k-clustering problem (k-1), we formulate the auxiliary clustering problem to generate a set of starting points for the k-th cluster center. Then, the k-medians algorithm is applied starting from the previous (k-1) cluster centers and each point from the set of starting points to solve the k-clustering problem. A solution with the least value of the clustering function is accepted as the solution to the k-clustering problem. We evaluate the performance of the adaptive k-medians algorithm and compare it with other concurrent clustering algorithms using 8 real-world data sets.
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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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Chai, Ruishuai. "Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm." Complexity 2021 (March 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/5564690.
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In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.
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Wu, Hong Bing, Pei Huang Lou, and Dun Bing Tang. "Adaptive Dynamic Clone Selection Strategy for Optimization." Key Engineering Materials 567 (July 2013): 133–38. http://dx.doi.org/10.4028/www.scientific.net/kem.567.133.
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Based on the Antibody Clonal Selection Theory of immunology, an adaptive dynamic clone select algorithm is put forward. The new algorithm is intended to integrate the local searching with the global and the probability evolution searching with the stochastic searching. Compared with other algorithms, the new algorithm prevents prematurely more effectively and has high convergence speed. Numeric experiments of function optimization indicate that the new algorithm is effective and useful.
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Singh, Bhupinder, and Priyanka Anand. "A novel adaptive butterfly optimization algorithm." International Journal of Computational Materials Science and Engineering 07, no. 04 (December 2018): 1850026. http://dx.doi.org/10.1142/s2047684118500264.
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Butterfly optimization algorithm (BOA) is an interesting bio-inspired algorithm that uses a nature inspired simulation model, based on the food foraging behavior of butterflies. A common problem with BOA is that in early stages of simulation process, it may converge to sub-optimal solutions due to the loss of diversity in its population. The sensory modality is the critical parameter which is responsible for searching new solutions in the nearby regions. In this work, an adaptive butterfly optimization algorithm is proposed in which a novel phenomenon of changing the sensory modality of BOA is employed during the optimization process in order to achieve better results in comparison to traditional BOA. The proposed Adaptive butterfly optimization algorithm (ABOA) is tested against seventeen standard bench mark functions. Its performance is then compared against existing standard optimization algorithms, namely artificial bee colony, firefly algorithm and standard butterfly optimization algorithm. The results indicate that the proposed adaptive BOA with improved parameter calculation mechanism produces superior results in terms of convergence and achievement of the global optimal solution efficiently.
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Liu, Qiong, and Tian Yang Li. "Improved Immune Clonal Selection Algorithm and its Application in Power Network Planning." Advanced Materials Research 614-615 (December 2012): 1635–40. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1635.
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Power network planning is a NP hard problem difficult to be solved. It can be contributed to similar TSP problem. Aiming at the slow convergence speed of the traditional immune clonal selection algorithm (ICA), adaptive immune clonal selection algorithm without memory（AICA）and adaptive immune clonal selection algorithm with memory(AICAM) are proposed respectively based on the combination of adaptive algorithm of clonal probability, immune probability , and group disaster algorithm. The two proposed algorithms have been applied to Power network planning problem. The adaptive algorithm has strong global search ability and weak local search ability at early evolution. Global search ability is weakened and local search ability is enhanced with the process of evolution in order to find global optimal point. The application of group disaster algorithm can enhance the diversity of the population and avoid the premature problems to some extent. Simulation results indicate that compared with the traditional immune clonal selection algorithm(ICA), the proposed algorithms can enhance the diversity of the population, avoid the premature problems, and can accelerate convergence speed in some extent.
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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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Chang, Chun Yuan. "An Adaptive Algorithm for Forest Fire Spread Based on Genetic Algorithm." Advanced Materials Research 694-697 (May 2013): 2842–45. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2842.
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The spread of forest fire is a complex adaptive system. The spread could be seen as the result of fire agents continuous learning, adaptation and co-ordination. This paper founded an Agent-based model for forest fire spread, modeled the generating of fire spread rules based on Genetic Algorithms. Created the spread rules with effect of wind and topography independently for forest fire, designed the fitness function, and took the genetic operation on the rules, which created new rules. Implemented the adaptive algorithm on Repast S, and used it in the Agent-based model of forest fire spread. The result of models running indicated the adaptive algorithm could improve the adaptive ability of fire agent.
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Raheem, Syed, and Dr Subhashish Bose. "Subband Adaptive Filter in Signal Processing Application." Revista Gestão Inovação e Tecnologias 11, no. 4 (August 4, 2021): 4096–109. http://dx.doi.org/10.47059/revistageintec.v11i4.2434.
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Owing to the powerful digital signal processors and the improvement of advanced edge adaptive algorithms there are an extraordinary number of various applications in which adaptive filters are utilized. Subband adaptive filtering algorithms can build the assembly pace of framework ID undertakings when the info signal is hued. The adaptive filter can filter the dubious noise signal, track the difference in the signal, and consistently change the boundaries to accomplish the ideal filtering impact. Another standardized subband adaptive filtering algorithm has been proposed, whose primary benefit is the lower computational intricacy when contrasted with best in class subband approaches, while keeping up comparable union execution. A connection between the adaptive subband coefficients and the ideal full band move work is determined, and the algorithm is demonstrated to create an asymptotically unprejudiced arrangement. The proficiency of the adaptive filters basically relies upon the plan procedure utilized and the algorithm of variation. The adaptive filters can be analogical plans, digital or blended which show their benefits and inconveniences, for instance, the analogical filters are low power consuming and fast response, however they address balance issues, which influence the activity of the variation algorithm.
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Ashrafi, Seyem Mohammad, and Noushin Emami Kourabbaslou. "An Efficient Adaptive Strategy for Melody Search Algorithm." International Journal of Applied Metaheuristic Computing 6, no. 3 (July 2015): 1–37. http://dx.doi.org/10.4018/ijamc.2015070101.
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An efficient adaptive version of Melody Search algorithm (EAMS) is introduced in this study, which is a powerful tool to solve optimization problems in continuous domains. Melody search (MS) algorithm is a recent newly improved version of harmony search (HS), while the algorithm performance strongly depends on fine-tuning of its parameters. Although MS is more efficient for solving continuous optimization problems than most of other HS-based algorithms, the large number of algorithm parameters makes it difficult to use. Hence, the main objective in this study is to reduce the number of algorithm parameters and improving its efficiency. To achieve this, a novel improvisation scheme is introduced to generate new solutions, a useful procedure is developed to determine the possible variable ranges in different iterations and an adaptive strategy is employed to calculate proper parameters' values and choose suitable memory consideration rules during the evolution process. Extensive computational comparisons are carried out by employing a set of eighteen well-known benchmark optimization problems with various characteristics from the literature. The obtained results reveal that EAMS algorithm can achieve better solutions compared to some other HS variants, basic MS algorithms and certain cases of well-known robust optimization algorithms.