Novel Zigzag-based Benchmark Functions for Bound Constrained Single Objective Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU142804" target="_blank" >RIV/00216305:26210/21:PU142804 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9504720" target="_blank" >https://ieeexplore.ieee.org/document/9504720</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC45853.2021.9504720" target="_blank" >10.1109/CEC45853.2021.9504720</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Novel Zigzag-based Benchmark Functions for Bound Constrained Single Objective Optimization

Popis výsledku v původním jazyce

The development and comparison of new optimization methods in general, and evolutionary algorithms in particular, rely heavily on benchmarking. In this paper, the construction of novel zigzag-based benchmark functions for bound constrained single objective optimization is presented. The new benchmark functions are non-differentiable, highly multimodal, and have a built-in parameter that controls the complexity of the function. To investigate the properties of the new benchmark functions two of the best algorithms from the CEC'20 Competition on Single Objective Bound Constrained Optimization, as well as one standard evolutionary algorithm, were utilized in a computational study. The results of the study suggest that the new benchmark functions are very well suited for algorithmic comparison.

Název v anglickém jazyce

Novel Zigzag-based Benchmark Functions for Bound Constrained Single Objective Optimization

Popis výsledku anglicky

The development and comparison of new optimization methods in general, and evolutionary algorithms in particular, rely heavily on benchmarking. In this paper, the construction of novel zigzag-based benchmark functions for bound constrained single objective optimization is presented. The new benchmark functions are non-differentiable, highly multimodal, and have a built-in parameter that controls the complexity of the function. To investigate the properties of the new benchmark functions two of the best algorithms from the CEC'20 Competition on Single Objective Bound Constrained Optimization, as well as one standard evolutionary algorithm, were utilized in a computational study. The results of the study suggest that the new benchmark functions are very well suited for algorithmic comparison.

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)

Projekt

<a href="/cs/project/EF16_026%2F0008392" target="_blank" >EF16_026/0008392: Výpočtové simulace pro efektivní nízkoemisní energetiku</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 IEEE Congress on Evolutionary Computation (CEC)

ISBN

978-1-7281-8393-0

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

857-862

Název nakladatele

IEEE

Místo vydání

neuveden

Místo konání akce

Krakow

Datum konání akce

28. 6. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000703866100108