Relevant bibliographies by topics / Ackley's function

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Academic literature on the topic 'Ackley's function'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Ackley's function"

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Agibalov, O., T. Blinovskaya, and N. Ventsov. "On the issue of using intuitionistic fuzzy sets for describing the expediency of solving optimization problems by genetic algorithms with given parameters." E3S Web of Conferences 224 (2020): 01008. http://dx.doi.org/10.1051/e3sconf/202022401008.

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The paper analyses a possible option for preparing data on the results of the genetic algorithm for transfer to another subject area. It was shown that the complexity of modern target functions requires the development of new approaches to determining the parameters of search procedures. A set of experiments, each stage of which consisted of performing 100 runs of the genetic algorithm on a CPU or GPU architecture, which determines the optimal solution of the Ackley’s function within a given time interval, was carried out. After the specified time interval expired, the operation of the algorithm was correctly completed by fixing the results obtained at the final iteration. The values of the absolute error were set to Δ={0.5, 0.15, 0.1, 0.05}. For each error value the number of algorithm runs, as a result of which the deviation was greater than Δ, was determined. On the basis of the experiment carried out, fuzzy estimates of the inexpediency of searching for the optimum of the Ackley’s function by the genetic algorithm on the CPU architecture in a time from 100 ms ...1800 ms were determined. The possibility of using intuitionistic fuzzy sets for describing the expediency of solving optimization problems by genetic algorithms with given parameters was shown.
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Zambrano Zambrano, Dannyll Michellc, Darío Vélez, Yohanna Daza, and José Manuel Palomares. "Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function." Enfoque UTE 10, no. 3 (September 30, 2019): 67–80. http://dx.doi.org/10.29019/enfoque.v10n3.490.

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This paper presents the social foraging behavior of Escherichia coli (E. Coli) bacteria based on Bacteria Foraging Optimization algorithms (BFOA) to find optimization and distributed control values. The search strategy for E. coli is very complex to express and the dynamics of the simulated chemotaxis stage in BFOA is analyzed with the help of a simple mathematical model. The methodology starts from a detailed analysis of the parameters of bacterial swimming and tumbling (C) and the probability of elimination and dispersion (Ped), then an adaptive variant of BFOA is proposed, in which the size of the chemotherapeutic step is adjusted according to the current suitability of a virtual bacterium. To evaluate the performance of the algorithm in obtaining optimal values, the resolution was applied to one of the benchmark functions, in this case the Ackley minimization function, a comparative analysis of the BFOA is then performed. The simulation results have shown the validity of the optimal values (minimum or maximum) obtained on a specific function for real world problems, with a function belonging to the benchmark group of optimization functions.
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Negrin Diaz, Iván Antonio, Ernesto Luciano Chagoyén Méndez, and Alejandro Negrin Montecelo. "Parameter tuning in the process of optimization of reinforced concrete structures." DYNA 88, no. 216 (February 22, 2021): 87–95. http://dx.doi.org/10.15446/dyna.v88n216.87169.

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Parameter tuning deals with finding the best parameter configuration of an optimization method in a given problem. In structural optimization, it could be an extensive and high-computing cost process. One way to avoid this drawback is to use analytical functions (or benchmark functions), for simulating main features of objective functions in real problems. In this paper, Biogeography-Based Optimization is applied during structural optimization of reinforced concrete frame structures, and Ackley function for parameter tuning in real cases simulation. The tuned method outperformed other meta-heuristics in the actual optimization problem. Structural results show that by not including static soil-structure interaction, differences in direct cost of the superstructure of up to 4.42% are obtained for predominantly cohesive soils and 11.55% for predominantly frictional ones. In beams, L/h ratios around 15 and high reinforcement ratios are highly recommended. In columns and shallow foundations, best rectangularity reaches values of 1.15 and 2.00 respectively.
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Ewald, Dawid, Hubert Zarzycki, Łukasz Apiecionek, and Jacek Czerniak. "Ordered Fuzzy Numbers Applied in Bee Swarm Optimization Systems." JUCS - Journal of Universal Computer Science 26, no. 11 (November 28, 2020): 1475–94. http://dx.doi.org/10.3897/jucs.2020.078.

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The paper presents an innovative OFNBee optimization method based on combining the swarm intelligence with the use of directed fuzzy numers OFN. In the introduction, the issues related to the subject of the study, including bee algorithms and OFN numbers, were reviewed. The innovative OFNBee algorithm was presented and verified against a set of known benchmarks functions such as Sphere, Rastrigin, Griewank, Rosenbrock, Schwefel and Ackley. These functions have been applied due to their reliability in the literature. In the further part of the study, the configuration of the algorithm parameters is carried out, including the launch of each mathematical function several dozen times for different data, such as different population sizes. The key part of the research and analysis was to compare OFNBee with six standard ABC, MBO, IMBO, TLBO, HBMO, BBMO bee algorithms. The article ends with a summary and an indication of the possible future works.
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Mirfenderesgi, Golnazalsadat, and S. Jamshid Mousavi. "Adaptive meta-modeling-based simulation optimization in basin-scale optimum water allocation: a comparative analysis of meta-models." Journal of Hydroinformatics 18, no. 3 (December 4, 2015): 446–65. http://dx.doi.org/10.2166/hydro.2015.157.

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Incorporating river basin simulation models in heuristic optimization algorithms can help modelers address complex, basin-scale water resource problems. We have developed a hybrid optimization-simulation model by linking a stretching particle swarm optimization (SPSO) algorithm and the MODSIM river basin decision support system (DSS), and have used the SPSO-MODSIM model to optimize water allocation at basin scale. Due to high computational cost of the SPSO-MODSIM model, we have, subsequently, used four meta-model types of artificial neural networks (ANN), support vector machines (SVM), kriging and polynomial response functions, replacing the MODSIM DSS, in an adaptively learning meta-modeling approach. The performances of the meta-models are first compared in two Ackley and Dejong benchmark functions optimization problems, and the meta-models are then evaluated by solving the Atrak river basin water allocation optimization problem in Iran. The results demonstrate that independent of the meta-model type, the sequentially space-filling meta-modeling approach can improve the performance of meta-models in the course of optimization by adaptively locating the promising regions of the search space where more samples need to be generated. However, the ANN and SVM meta-models perform better than others in saving the number of costly, original objective function evaluations.
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Carmona Cortes, Omar Andres, and Josenildo Costa da Silva. "Unconstrained numerical optimization using real-coded genetic algorithms: a study case using benchmark functions in R from Scratch." Revista Brasileira de Computação Aplicada 11, no. 3 (September 25, 2019): 1–11. http://dx.doi.org/10.5335/rbca.v11i3.9047.

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Unconstrained numerical problems are common in solving practical applications that, due to its nature, are usually devised by several design variables, narrowing the kind of technique or algorithm that can deal with them. An interesting way of tackling this kind of issue is to use an evolutionary algorithm named Genetic Algorithm. In this context, this work is a tutorial on using real-coded genetic algorithms for solving unconstrained numerical optimization problems. We present the theory and the implementation in R language. Five benchmarks functions (Rosenbrock, Griewank, Ackley, Schwefel, and Alpine) are used as a study case. Further, four different crossover operators (simple, arithmetical, non-uniform arithmetical, and Linear), two selection mechanisms (roulette wheel and tournament), and two mutation operators (uniform and non-uniform) are shown. Results indicate that non-uniform mutation and tournament selection tend to present better outcomes.
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Houcine, Lassad, Mohamed Bouzbida, and Abdelkader Chaari. "Improved Adaptive Particle Swarm Optimization for Optimization Functions and Clustering Fuzzy Modeling System." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 05 (September 28, 2018): 717–39. http://dx.doi.org/10.1142/s0218488518500332.

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In this paper, a new PSO algorithm with a new adaptive weight of inertia and time acceleration coefficients (TVAC) is proposed; this algorithm called IAPSO is introduced for global optimization. The objective of this proposition is to initialize the weight of inertia to a high value, giving priority to the global exploration of the research space and gradually decreasing the new inertia adaptable to the weight in order to obtain refined solutions. The test of our algorithm is performed on three standard reference functions (Schwefel’s (unimodal), Ackley (multimodal) and Griewank (Multimodal)). The proposed IAPSO algorithm combined with the Fuzzy Clustering NPCM algorithm for modeling and identifying a non-linear system. The new NPCM-IAPSO grouping algorithm also solves the problems of the classical clustering algorithm (FCM, GK, PCM, EPCM, FCM-PSO, EPCM-PSO …etc.), such as convergence towards local optimization and sensitivity to initialization. The effectiveness of the proposed NPCM-IAPSO algorithm was tested on the furnace gas Box and Jenkins, dryer system and two other nonlinear systems described by differential equations.
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Han, Wen Hua. "A New Simple Micro-PSO for High Dimensional Optimization Problem." Applied Mechanics and Materials 236-237 (November 2012): 1195–200. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.1195.

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The particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search optimization technique, which has already been widely used to various of fields. In this paper, a simple micro-PSO is proposed for high dimensional optimization problem, which is resulted from being introduced escape boundary and perturbation for global optimum. The advantages of the simple micro-PSO are more simple and easily implemented than the previous micro-PSO. Experiments were conducted using Griewank, Rosenbrock, Ackley, Tablets functions. The experimental results demonstrate that the simple micro-PSO are higher optimization precision and faster convergence rate than PSO and robust for the dimension of the optimization problem.
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Asadieh, Behzad, and Abbas Afshar. "Optimization of Water-Supply and Hydropower Reservoir Operation Using the Charged System Search Algorithm." Hydrology 6, no. 1 (January 8, 2019): 5. http://dx.doi.org/10.3390/hydrology6010005.

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The Charged System Search (CSS) metaheuristic algorithm is introduced to the field of water resources management and applied to derive water-supply and hydro-power operating policies for a large-scale real-world reservoir system. The optimum algorithm parameters for each reservoir operation problems are also obtained via a tuning procedure. The CSS algorithm is a metaheuristic optimization method inspired by the governing laws of electrostatics in physics and motion from the Newtonian mechanics. In this study, the CSS algorithm’s performance has been tested with benchmark problems, consisting of highly non-linear constrained and/or unconstrained real-valued mathematical models, such as the Ackley’s function and Fletcher–Powell function. The CSS algorithm is then used to optimally solve the water-supply and hydropower operation of “Dez” reservoir in southern Iran over three different operation periods of 60, 240, and 480 months, and the results are presented and compared with those obtained by other available optimization approaches including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Constrained Big Bang–Big Crunch (CBB–BC) algorithm, as well as those obtained by gradient-based Non-Linear Programming (NLP) approach. The results demonstrate the robustness and superiority of the CSS algorithm in solving long term reservoir operation problems, compared to alternative methods. The CSS algorithm is used for the first time in the field of water resources management, and proves to be a robust, accurate, and fast convergent method in handling complex problems in this filed. The application of this approach in other water management problems such as multi-reservoir operation and conjunctive surface/ground water resources management remains to be studied.
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de la Fraga, Luis Gerardo. "Differential Evolution under Fixed Point Arithmetic and FP16 Numbers." Mathematical and Computational Applications 26, no. 1 (February 4, 2021): 13. http://dx.doi.org/10.3390/mca26010013.

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In this work, the differential evolution algorithm behavior under a fixed point arithmetic is analyzed also using half-precision floating point (FP) numbers of 16 bits, and these last numbers are known as FP16. In this paper, it is considered that it is important to analyze differential evolution (DE) in these circumstances with the goal of reducing its consumption power, storage size of the variables, and improve its speed behavior. All these aspects become important if one needs to design a dedicated hardware, as an embedded DE within a circuit chip, that performs optimization. With these conditions DE is tested using three common multimodal benchmark functions: Rosenbrock, Rastrigin, and Ackley, in 10 dimensions. Results are obtained in software by simulating all numbers using C programming language.
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Dissertations / Theses on the topic "Ackley's function"

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Knoflíček, Jakub. "Analýza různých přístupů k řešení optimalizačních úloh." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236207.

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This paper deals with various approaches to solving optimization tasks. In prolog some examples from real life that show the application of optimization methods are given. Then term optimization task is defined and introducing of term fitness function which is common to all optimization methods follows. After that approaches by particle swarm optimization, ant colony optimization, simulated annealing, genetic algorithms and reinforcement learning are theoretically discussed. For testing we are using two discrete (multiple knapsack problem and set cover problem) and two continuous tasks (searching for global minimum of Ackley's and Rastrigin's function) which are presented in next chapter. Description of implementation details follows. For example description of solution representation or how current solutions are changed. Finally, results of measurements are presented. They show optimal settings for parameters of given optimization methods considering test tasks. In the end are given test tasks, which will be used for finding optimal settings of given approaches.
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Conference papers on the topic "Ackley's function"

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Hu, Jhen-Jai, Yu-Te Su, and Tzuu-Hseng S. Li. "A novel ecological-biological-behavior praticle swarm optimization for Ackley's function." In 2010 International Symposium on Computer, Communication, Control and Automation (3CA). IEEE, 2010. http://dx.doi.org/10.1109/3ca.2010.5533436.

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Cai, Wei, Li Yang, and Yu Yu. "Solution of ackley function based on particle swarm optimization algorithm." In 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA). IEEE, 2020. http://dx.doi.org/10.1109/aeeca49918.2020.9213634.

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Wen, ChangJun, Bo Xia, and Xin Liu. "Solution of second order Ackley function based on SAPSO algorithm." In 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE). IEEE, 2017. http://dx.doi.org/10.1109/ccsse.2017.8088008.

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Motiian, Saeed, and Hamid Soltanian-Zadeh. "Improved particle swarm optimization and applications to Hidden Markov Model and Ackley function." In 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA). IEEE, 2011. http://dx.doi.org/10.1109/cimsa.2011.6059932.

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Journal articles
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