A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00321819" target="_blank" >RIV/68407700:21230/18:00321819 - isvavai.cz</a>

Result on the web

<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195675" target="_blank" >http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195675</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0195675" target="_blank" >10.1371/journal.pone.0195675</a>

Result language

angličtina

Original language name

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

Original language description

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sinecosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Levy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

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

Name of the periodical

PLoS ONE

ISSN

1932-6203

e-ISSN

1932-6203

Volume of the periodical

13

Issue of the periodical within the volume

5

Country of publishing house

US - UNITED STATES

Number of pages

29

Pages from-to

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UT code for WoS article

000432348900004

EID of the result in the Scopus database

2-s2.0-85047420118