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Soft Computing

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19.02.2018 | Methodologies and Application

An optimization algorithm applied to the class integration and test order problem

verfasst von: Yanmei Zhang, Shujuan Jiang, Xingya Wang, Ruoyu Chen, Miao Zhang

Erschienen in: Soft Computing | Ausgabe 12/2019

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Abstract

In recent years, many intelligent optimization algorithms are applied to the class integration and test order problem and it has been proved that they can efficiently solve this problem. In this paper, the particle swarm optimization algorithm is applied to the class integration and test order problem. First, the initial population (test orders) is generated randomly and each test order is taken as a particle. Here, we map particles to one-dimensional space; then, a particle (an integration test order) can be represented by a position in the one-dimensional space; finally, the optimal particle (integration test order) is selected by particle swarm optimization algorithm. Also, whether the precedence table of dependency relations is introduced as the constraints in evolution can impact the effect of particle swarm optimization approach is validated through experiment. The experimental results show that the particle swarm optimization algorithm is encouraging in solving class integration and test order problem.

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Titel: An optimization algorithm applied to the class integration and test order problem
verfasst von: Yanmei Zhang
Shujuan Jiang
Xingya Wang
Ruoyu Chen
Miao Zhang
Publikationsdatum: 19.02.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 12/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3077-1

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