A collection of robotics problems for benchmarking evolutionary computation methods
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU148221" target="_blank" >RIV/00216305:26210/23:PU148221 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1007/978-3-031-30229-9_24" target="_blank" >http://dx.doi.org/10.1007/978-3-031-30229-9_24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-30229-9_24" target="_blank" >10.1007/978-3-031-30229-9_24</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A collection of robotics problems for benchmarking evolutionary computation methods
Popis výsledku v původním jazyce
The utilization of benchmarking techniques has a crucial role in the development of novel optimization algorithms, and also in performing comparisons between already existing methods. This is especially true in the field of evolutionary computation, where the theoretical performance of the method is difficult to analyze. For these benchmarking purposes, artificial (or synthetic) functions are currently the most widely used ones. In this paper, we present a collection of real-world robotics problems that can be used for benchmarking evolutionary computation methods. The proposed benchmark problems are a combination of inverse kinematics and path planning in robotics that can be parameterized. We conducted an extensive numerical investigation that encompassed solving 200 benchmark problems by seven selected metaheuristic algorithms. The results of this investigation showed that the proposed benchmark problems are quite difficult (multimodal and non-separable) and that they can be successfully used for differentiating and ranking various metaheuristics.
Název v anglickém jazyce
A collection of robotics problems for benchmarking evolutionary computation methods
Popis výsledku anglicky
The utilization of benchmarking techniques has a crucial role in the development of novel optimization algorithms, and also in performing comparisons between already existing methods. This is especially true in the field of evolutionary computation, where the theoretical performance of the method is difficult to analyze. For these benchmarking purposes, artificial (or synthetic) functions are currently the most widely used ones. In this paper, we present a collection of real-world robotics problems that can be used for benchmarking evolutionary computation methods. The proposed benchmark problems are a combination of inverse kinematics and path planning in robotics that can be parameterized. We conducted an extensive numerical investigation that encompassed solving 200 benchmark problems by seven selected metaheuristic algorithms. The results of this investigation showed that the proposed benchmark problems are quite difficult (multimodal and non-separable) and that they can be successfully used for differentiating and ranking various metaheuristics.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023
ISBN
978-3-031-30229-9
ISSN
0302-9743
e-ISSN
—
Počet stran výsledku
16
Strana od-do
„364 “-„379“
Název nakladatele
Springer
Místo vydání
Cham
Místo konání akce
Brno
Datum konání akce
12. 4. 2023
Typ akce podle státní příslušnosti
EUR - Evropská akce
Kód UT WoS článku
—