Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00306039" target="_blank" >RIV/68407700:21230/17:00306039 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S095219761630241X" target="_blank" >http://www.sciencedirect.com/science/article/pii/S095219761630241X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2016.12.014" target="_blank" >10.1016/j.engappai.2016.12.014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

Popis výsledku v původním jazyce

The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. Nevertheless, this algorithm requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped in local optima), as well as enhance solution diversity. Thus, this paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, to permit adaptive selection of its global and local search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem. Experimental results reveal that ATLBO exhibits competitive performances against the original TLBO and other meta-heuristic counterparts.

Název v anglickém jazyce

Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

Popis výsledku anglicky

The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. Nevertheless, this algorithm requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped in local optima), as well as enhance solution diversity. Thus, this paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, to permit adaptive selection of its global and local search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem. Experimental results reveal that ATLBO exhibits competitive performances against the original TLBO and other meta-heuristic counterparts.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Svazek periodika

59

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

35-50

Kód UT WoS článku

000393937400004

EID výsledku v databázi Scopus

2-s2.0-85007109693