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Wyświetlanie 1-4 z 4
Tytuł:
Gradient-Based Algorithms in the Brachistochrone Problem Having a Black-Box Represented Mathematical Model
Autorzy:
Dębski, R.
Powiązania:
https://bibliotekanauki.pl/articles/308956.pdf
Data publikacji:
2014
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
black-box optimization
brachistochrone problem
optimal control
trajectory optimization
Opis:
Trajectory optimization problems with black-box represented objective functions are often solved with the use of some meta-heuristic algorithms. The aim of this paper is to show that gradient-based algorithms, when applied correctly, can be effective for such problems as well. One of the key aspects of successful application is choosing, in the search space, a basis appropriate for the problem. In an experiment to demonstrate this, three simple adaptations of gradient-based algorithms were executed in the forty-dimensional search space to solve the brachistochrone problem having a blackbox represented mathematical model. This experiment was repeated for two different bases spanning the search space. The best of the algorithms, despite its very basic implementation, needed only about 100 iterations to find very accurate solutions. 100 iterations means about 2000 objective functional evaluations (simulations). This corresponds to about 20 iterations of a typical evolutionary algorithm, e.g. ES(μ,l ).
Źródło:
Journal of Telecommunications and Information Technology; 2014, 1; 32-40
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Benchmarking Procedures for Continuous Optimization Algorithms
Autorzy:
Opara, K.
Arabas, J.
Powiązania:
https://bibliotekanauki.pl/articles/308400.pdf
Data publikacji:
2011
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
black-box optimization
comparing optimization algorithms
evaluation criteria
parallel computing
Opis:
Reliable comparison of optimization algorithms requires the use of specialized benchmarking procedures. This paper highlights motivations which influence their structure, discusses evaluation criteria of algorithms, typical ways of presenting and interpreting results as well as related statistical procedures. Discussions are based on examples from CEC and BBOB benchmarks. Moreover, attention is drawn to these features of comparison procedures, which make them susceptible to manipulation. In particular, novel application of the weak axiom of revealed preferences to the field of benchmarking shows why it may be misleading to assess algorithms on basis of their ranks for each of test problems. Additionally, an idea is presented of developing massively parallel implementation of benchmarks. Not only would this provide faster computation but also open the door to improving reliability of benchmarking procedures and promoting research into parallel implementations of optimization algorithms.
Źródło:
Journal of Telecommunications and Information Technology; 2011, 4; 73-80
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative Study of PSO and CMA-ES Algorithms on Black-box Optimization Benchmarks
Autorzy:
Szynkiewicz, P.
Powiązania:
https://bibliotekanauki.pl/articles/307566.pdf
Data publikacji:
2018
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
benchmarking
black-box optimization
CMA-ES
global optimization
PSO
stochastic optimization
Opis:
Numerous practical engineering applications can be formulated as non-convex, non-smooth, multi-modal and ill-conditioned optimization problems. Classical, deterministic algorithms require an enormous computational effort, which tends to fail as the problem size and its complexity increase, which is often the case. On the other hand, stochastic, biologically-inspired techniques, designed for global optimum calculation, frequently prove successful when applied to real life computational problems. While the area of bio-inspired algorithms (BIAs) is still relatively young, it is undergoing continuous, rapid development. Selection and tuning of the appropriate optimization solver for a particular task can be challenging and requires expert knowledge of the methods to be considered. Comparing the performance of viable candidates against a defined test bed environment can help in solving such dilemmas. This paper presents the benchmark results of two biologically inspired algorithms: covariance matrix adaptation evolution strategy (CMA-ES) and two variants of particle swarm optimization (PSO). COCO (COmparing Continuous Optimizers) – a platform for systematic and sound comparisons of real-parameter global optimization solvers was used to evaluate the performance of CMA-ES and PSO methods. Particular attention was paid to the effciency and scalability of both techniques.
Źródło:
Journal of Telecommunications and Information Technology; 2018, 4; 5-17
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Incrementally Solving Nonlinear Regression Tasks Using IBHM Algorithm
Autorzy:
Zawistowski, P.
Arabas, J.
Powiązania:
https://bibliotekanauki.pl/articles/308427.pdf
Data publikacji:
2011
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
black-box modeling
neural networks
nonlinear approximation
nonlinear regression
support vector regression
weighted correlation
Opis:
This paper considers the black-box approximation problem where the goal is to create a regression model using only empirical data without incorporating knowledge about the character of nonlinearity of the approximated function. This paper reports on ongoing work on a nonlinear regression methodology called IBHM which builds a model being a combination of weighted nonlinear components. The construction process is iterative and is based on correlation analysis. Due to its iterative nature, the methodology does not require a priori assumptions about the final model structure which greatly simplifies its usage. Correlation based learning becomes ineffective when the dynamics of the approximated function is too high. In this paper we introduce weighted correlation coefficients into the learning process. These coefficients work as a kind of a local filter and help overcome the problem. Proof of concept experiments are discussed to show how the method solves approximation tasks. A brief discussion about complexity is also conducted.
Źródło:
Journal of Telecommunications and Information Technology; 2011, 4; 65-72
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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