Keywords : Particle Swarm Optimization

A Proposed Method for Feature Selection using a Binary Particle Swarm Optimization Algorithm and Mutual Information Technique

Mustafa Ayham Abed Alhafedh; Omar Saber Qasim

AL-Rafidain Journal of Computer Sciences and Mathematics, 2019, Volume 13, Issue 2, Pages 49-60
DOI: 10.33899/csmj.2020.163520

Feature selection is one of the most important issues in improving the data classification process. It greatly influences the accuracy of the classification. There are many evolutionary algorithms used for this purpose, such as the Particle Swarm Optimization (PSO) in discrete space through the Binary PSO concept. The BPSO optimization algorithm derives its mechanism from the default PSO algorithm but in discrete space. In this research, a hybrid approach was proposed between the BPSO algorithm and Mutual Information (MI) to obtain subsets of features through two basic phases: the first is to use the BPSO algorithm to determine the features affecting the data classification process by relying on an objective function. In the second phase, the MI method is used to reduce the number of features identified by the BPSO method. The results of the proposed algorithm have demonstrated efficiency and effectiveness by obtaining higher classification accuracy and using fewer features than default methods.

Apply Particle Swarm Optimization Algorithm to Measure the Software Quality

Ibrahim Ahmed Saleh; Salha Raa’d Mahammead

AL-Rafidain Journal of Computer Sciences and Mathematics, 2018, Volume 12, Issue 1, Pages 26-36
DOI: 10.33899/csmj.2018.163572

The process of improvement software quality began from early stages of software engineering development. It uses multiple quality metrics which are very important in software development. To calculate the   standards quality of in software testing has been adopted.  The software testing is focusing on the Software defect. In this paper is proposed new methods which combine the particle swarm optimization (PSO) to handle the best features Extraction with back-propagation networks to  testing and evaluation of the data set . The paper depended database for NASA standards data.  The result and experiment method improved quality performance for all classification methods used in the research"Combining Particle Swarm Optimization based Feature Selection and Bagging for Software Defect Prediction ".