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

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. Altho...

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Main Authors: Kamal Z., Zamli, Fakhrud, Din, Ahmed, Bestoun S., Bures, Miroslav
Format: Article
Language:English
English
Published: Public Library of Science 2018
Subjects:
AI Indexes (General)
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/22593/1/journal.pone.0195187.t006.ppt
http://umpir.ump.edu.my/id/eprint/22593/2/A%20hybrid%20Q-learning%20sine-cosine-based%20strategy.pdf
http://umpir.ump.edu.my/id/eprint/22593/
https://doi.org/10.1371/journal.pone.0195675
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spelling my.ump.umpir.225932019-07-09T05:01:00Z http://umpir.ump.edu.my/id/eprint/22593/ A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem Kamal Z., Zamli Fakhrud, Din Ahmed, Bestoun S. Bures, Miroslav AI Indexes (General) QA75 Electronic computers. Computer science 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 sine-cosine 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 (Le´vy 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. Public Library of Science 2018-05-17 Article PeerReviewed slideshow en http://umpir.ump.edu.my/id/eprint/22593/1/journal.pone.0195187.t006.ppt pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/22593/2/A%20hybrid%20Q-learning%20sine-cosine-based%20strategy.pdf Kamal Z., Zamli and Fakhrud, Din and Ahmed, Bestoun S. and Bures, Miroslav (2018) A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem. PLoS ONE, 13 (5). pp. 1-41. ISSN 1932-6203 https://doi.org/10.1371/journal.pone.0195675 10.1371/journal.pone.0195675
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic AI Indexes (General)
QA75 Electronic computers. Computer science
spellingShingle AI Indexes (General)
QA75 Electronic computers. Computer science
Kamal Z., Zamli
Fakhrud, Din
Ahmed, Bestoun S.
Bures, Miroslav
A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem
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 sine-cosine 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 (Le´vy 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.
format Article
author Kamal Z., Zamli
Fakhrud, Din
Ahmed, Bestoun S.
Bures, Miroslav
author_facet Kamal Z., Zamli
Fakhrud, Din
Ahmed, Bestoun S.
Bures, Miroslav
author_sort Kamal Z., Zamli
title A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem
title_short A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem
title_full A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem
title_fullStr A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem
title_full_unstemmed A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem
title_sort hybrid q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem
publisher Public Library of Science
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/22593/1/journal.pone.0195187.t006.ppt
http://umpir.ump.edu.my/id/eprint/22593/2/A%20hybrid%20Q-learning%20sine-cosine-based%20strategy.pdf
http://umpir.ump.edu.my/id/eprint/22593/
https://doi.org/10.1371/journal.pone.0195675
_version_ 1643669423575269376
score 13.211869

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