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

Zamli, Kamal Z.; Din, Fakhrud; Ahmed, Bestoun S.; Bureš, Miroslav

doi:10.1371/journal.pone.0195675

Cited by 60 publications

(23 citation statements)

References 39 publications

(88 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since its introduction [99], it has attracted much attention and finds its application in many fields [100]- [105]. Moreover, there are many improved SCA variants have been proposed in the literature, for example, Riesz fractional derivative-based SCA [106], crossover scheme based SCA [107], mutation strategies driven SCA [108], multiorthogonal search based SCA [109], Multi-objective SCA [101], Q-learning based SCA [110], elitism strategy and new updating mechanism based SCA [111], Levey flight based SCA [112].…”

Section: Proposed Coscamentioning

confidence: 99%

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

et al. 2020

View full text Add to dashboard Cite

This study proposes an effective intelligent predictive model for prediction of cervical hyperextension injury. The prediction model is constructed by combing an improved sine cosine algorithm (SCA) with support vector machines (SVM), which is named COSCA-SVM. The core of the developed model is the COSCA method that combines the opposition-based learning mechanism and covariance mechanism to boost and recover the exploratory competence of SCA. The proposed COSCA approach is utilized to optimize the two critical parameters of the SVM, and it is also employed to catch the optimal feature subset. Based on the optimal parameter combination and feature subset, COSCA-SVM is able to make self-directed prediction of cervical hyperextension injury. The proposed COSCA was compared with other well-known and effective methods using 23 benchmark problems. Simulation results verify that the proposed COSCA is significantly superior to studied methods in dealing with majority of benchmark problems. Meanwhile, the proposed COSCA-SVM is compared with six other machine learning approaches considering a reallife dataset. Results have shown that the proposed COSCA-SVM can achieve better classification routine and higher stability on all four indicators. Therefore, we can expect that COSCA-SVM can be a promising building block for predicting cervical hyperextension injury.

show abstract

Section: Proposed Coscamentioning

confidence: 99%

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In another situation, the number of uncovered tuples was taken as the cost of the candidate solution, which required minimization. The cardinality of the set and the objective value were represented by the number of non-covered interaction tuples covered in [16].…”

Section: A Csst Testingmentioning

confidence: 99%

Multiple Black Hole Inspired Meta-Heuristic Searching Optimization for Combinatorial Testing

Al-Sammarraie

Jawawi

2020

IEEE Access

View full text Add to dashboard Cite

Combinatorial searching-based software testing (CSST) is a challenging optimization procedure. The achievement of optimal solutions involves a careful formulation of the optimization problem and the selection of an appropriate approach. Meta-heuristic searching procedures have proven to be effective for solving CSST issues. Black hole (BH) optimization is among the more recently developed meta-heuristic searching algorithms. While this approach has been observed to be an effective alternative to particle swarm optimization, its operation is based on only one swarm. To date, no efforts have been made to modify this approach to accommodate multiple swarms. This study proposes a new variant of BH that involves a combination of multiple swarms. The BH optimizer is modified from continuous searching to binary searching and subsequently applied for solving CSST. The evaluation is based on a modified-benchmarking mathematical function and well-known CSST problems. This modified BH method is superior to the original BH and the established particle swarm optimization (PSO) approach. In terms of CSST problems, binary multiple black hole (BMBH) optimizations generate reduction rates between 50% and more than 60% for t = 4 according to the problem. INDEX TERMS Black hole optimization, combinatorial searching-based software testing, meta-heuristic searching, multiple black hole optimization, swarm meta-heuristic.

show abstract

“…Software module clustering has been used in the literature within software reverse engineering to assess the software comprehension, evolution, and maintenance [15][16][17]. In particular, the interest on software module clustering problem has been advocated by recent emergence of the new field called Search based Software Engineering (SBSE) [18][19][20][21]. Much work has been done in the maintenance part to help to identify and group (i.e., cluster) those modules (sometimes called subsystems) with common features.…”

Section: Related Work On Software Module Clusteringmentioning

confidence: 99%

“…If (X # (%&') ) dominates (X *+,% ) then [18]. X *+,% = X # (%&') [19]. Add X *+,% into PF archive [20].…”

Section: The Design Of Ms-jayamentioning

confidence: 99%

Multi-Start Jaya Algorithm for Software Module Clustering Problem

Zamli¹,

Alsewari²,

Ahmed³

2018

AZJHPC

View full text Add to dashboard Cite

Jaya algorithm has gained considerable attention lately due to its simplicity and requiring no control parameters (i.e. parameter free). Despite its potential, Jaya algorithm is inherently designed for single objective problems. Additionally, Jaya is limited by the intense conflict between exploration (i.e. roams the random search space at the global scale) and exploitation (i.e. neighborhood search by exploiting the current good solution). Thus, Jaya requires better control for exploitation and exploration in order to prevent premature convergence and avoid being trapped in local optima. Addressing these issues, this paper proposes a new multi-objective Jaya variant with a multi-start adaptive capability and Cuckoo search like elitism scheme, called MS-Jaya, to enhance its exploitation and exploration allowing good convergence while permitting more diverse solutions. To assess its performances, we adopt MS-Jaya for the software module clustering problem. Experimental results reveal that MS-Jaya exhibits competitive performances against the original Jaya and state-of-the-art parameter free meta-heuristic counterparts consisting of Teaching Learning based Optimization (TLBO), Global Neighborhood Algorithm (GNA), Symbiotic Optimization Search (SOS), and Sine Cosine Algorithm (SCA).

show abstract

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

Cited by 60 publications

References 39 publications

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

Multiple Black Hole Inspired Meta-Heuristic Searching Optimization for Combinatorial Testing

Multi-Start Jaya Algorithm for Software Module Clustering Problem

Contact Info

Product

Resources

About