Applying Meta-Heuristics Algorithms in Model-Driven Approaches for Solving the CRA Problem

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Faridmoayer Sogol; HoseinDoost Samaneh; Kolahdouz Rahimi Shekoufeh; Zamani Bahman

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF COMPUTING AND SECURITY Year:621 | Volume: | Issue: Page(s): 141-161

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Applying Meta-Heuristics Algorithms in Model-Driven Approaches for Solving the CRA Problem

Author(s)

Faridmoayer Sogol | HoseinDoost Samaneh | Kolahdouz Rahimi Shekoufeh | Zamani Bahman | Issue Writer Certificate

Keywords

Model-Driven Software EngineeringQ4

Model TransformationQ4

Search-Based OptimizationQ4

Particle Swarm Optimization (PSO)Q4

Ant Colony Optimization (ACO)Q4

Abstract

The Class Responsibility Assignment (CRA) problem is one of the most important problems in Object-Oriented Software Engineering. It is a Search-Based Optimization problem to assign attributes and methods to a set of classes such that the related class diagram has maximum cohesion and minimum coupling. Due to the large and complex search space of the problem, finding an optimal solution is a costly and challenging task. In this regard, the use of optimization approaches can be promising. In this paper, the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms are implemented using Model-Driven Engineering (MDE) techniques for solving the CRA problem. To evaluate the proposed approach, the effectiveness of provided algorithms is presented using models with different scales. Additionally, the results are compared with existing solutions for the CRA problem in the community. The results indicated that for large-scale models the ACO algorithm could find a much better solution in less time compared to the PSO algorithm.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops