Constraints handling in combinatorial interaction testing using multi-objective crow search and fruitfly optimization

Ramgouda, P.; Chandraprakash, V.

doi:10.1007/s00500-019-03795-w

Cited by 16 publications

(9 citation statements)

References 23 publications

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“…Hybrid of multi-objective crow search and fruitfly optimization has been studied and offers an optimal selection of the test suites at a fairly good convergence rate [12]. Haslinger et al [13] applied a Simulated Annealing algorithm to generate t-wise covering array and demonstrated a tool to improve the performance of SPL testing.…”

Section: Related Workmentioning

confidence: 99%

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Sahid,

Saringat,

Hamzah

et al. 2023

IJACSA

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Generating test configuration for Software Product Line (SPL) is difficult, due to the exponential effect of feature combination. Pairwise testing can generate test input for a single software product that deviates from exhaustive testing, nevertheless proven to be effective. In the context of SPL testing, to generate minimal test configuration that maximizes pairwise coverage is not trivial, especially when dealing with a huge number of features and when constraints must be satisfied, which is the case in most SPL systems. In this paper, we propose an estimation of distribution algorithm, based on pairwise testing, to alleviate this problem. Comparisons are made against a greedybased and a constraint handling based approach. The experiments demonstrate the feasibility of the proposed algorithm, such that it achieves better test configurations dissimilarity and at the same time maintain the test configuration size and pairwise coverage. This is supported by analysis using descriptive statistics.

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Section: Related Workmentioning

confidence: 99%

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Sahid,

Saringat,

Hamzah

et al. 2023

IJACSA

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“…Since its proposed, it has received extensive attention and been applied in some areas of optimization like economic dispatch problems [135], feature selection problems [136], and so on. The authors found CSA metaheuristic has been introduced around combinatorial tway testing [137].…”

Section: Swarm-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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“…Hence, many evolutionary and heuristic approaches are proposed. In particle swam optimization (PSO), the fitness value of all particles is optimized, and it is like flock of birds every bird in the flock moves in the same direction of its neighbor (Ramgouda and Chandraprakash, 2019). Each bird is associated with a velocity with which it is moving.…”

Section: Literature Surveymentioning

confidence: 99%

An improved peak side lobe reduction method for linear array antenna for military applications

Kotamraju

2021

IJICC

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PurposeThe purpose of this paper is to improve energy efficiency and further reduction of side lobe level the algorithm proposed is firework algorithm. In this paper, roused by the eminent swarm conduct of firecrackers, a novel multitude insight calculation called fireworks algorithm (FA) is proposed for work enhancement. The FA is introduced and actualized by mimicking the blast procedure of firecrackers. In the FA, two blast (search) forms are utilized and systems for keeping decent variety of sparkles are likewise all around planned. To approve the presentation of the proposed FA, correlation tests were led on nine benchmark test capacities among the FA, the standard PSO (SPSO) and the clonal PSO (CPSO).Design/methodology/approachThe antenna arrays are used to improve the capacity and spectral efficiency of wireless communication system. The latest communication systems use the antenna array technology to improve the spectral efficiency, fill rate and the energy efficiency of the communication system can be enhanced. One of the most important properties of antenna array is beam pattern. A directional main lobe with low side lobe level (SLL) of the beam pattern will reduce the interference and enhance the quality of communication. The classical methods for reducing the side lobe level are differential evolution algorithm and PSO algorithm. In this paper, roused by the eminent swarm conduct of firecrackers, a novel multitude insight calculation called fireworks algorithm (FA) is proposed for work enhancement. The FA is introduced and actualized by mimicking the blast procedure of firecrackers. In the FA, two blast (search) forms are utilized and systems for keeping decent variety of sparkles are likewise all around planned. To approve the presentation of the proposed FA, correlation tests were led on nine benchmark test capacities among the FA, the standard PSO (SPSO) and the clonal PSO (CPSO). It is demonstrated that the FA plainly beats the SPSO and the CPSO in both enhancement exactness and combination speed. The results convey that the side lobe level is reduced to −34.78dB and fill rate is increased to 78.53.FindingsSamples including 16-element LAAs are conducted to verify the optimization performances of the SLL reductions. Simulation results show that the SLLs can be effectively reduced by FA. Moreover, compared with other benchmark algorithms, fireworks has a better performance in terms of the accuracy, the convergence rate and the stability.Research limitations/implicationsWith the use of algorithms radiation is prone to noise one way or other. Even with any optimizations we cannot expect radiation to be ideal. Power dissipation or electro magnetic interference is bound to happen, but the use of optimization algorithms tries to reduce them to the extent that is possible.Practical implications16-element linear antenna array is available with latest versions of Matlab.Social implicationsThe latest technologies and emerging developments in the field of communication and with exponential growth in users the capacity of communication system has bottlenecks. The antenna arrays are used to improve the capacity and spectral efficiency of wireless communication system. The latest communication systems use the antenna array technology which is to improve the spectral efficiency, fill rate and the energy efficiency of the communication system can be enhanced.Originality/valueBy using FA, the fill rate is increased to 78.53 and the side lobe level is reduced to 35dB, when compared with the bench mark algorithms.

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Constraints handling in combinatorial interaction testing using multi-objective crow search and fruitfly optimization

Cited by 16 publications

References 23 publications

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

An improved peak side lobe reduction method for linear array antenna for military applications

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