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DOI: 10.14569/IJACSA.2023.01406130
PDF

Parameter Identification of a Multilayer Perceptron Neural Network using an Optimized Salp Swarm Algorithm

Author 1: Mohamad Al-Laham
Author 2: Salwani Abdullah
Author 3: Mohammad Atwah Al-Ma’aitah
Author 4: Mohammed Azmi Al-Betar
Author 5: Sofian Kassaymeh
Author 6: Ahmad Azzazi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 6, 2023.

  • Abstract and Keywords
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Abstract: Effort estimation in software development (SEE) is a crucial concern within the software engineering domain, as it directly impacts cost estimation, scheduling, staffing, planning, and resource allocation accuracy. In this scientific article, the authors aim to tackle this issue by integrating machine learning (ML) techniques with metaheuristic algorithms in order to raise prediction accuracy. For this purpose, they employ a multilayer perceptron neural network (MLP) to perform the estimation for SEE. Unfortunately, the MLP network has numerous drawbacks as well, including weight dependency, rapid convergence, and accuracy limits. To address these issues, the SSA Algorithm is employed to optimize the MLP weights and biases. Simultaneously, the SSA algorithm has shortcomings in some aspects of the search mechanisms as well, such as rapid convergence and being susceptible to the local optimal trap. As a result, the genetic algorithm (GA) is utilized to address these shortcomings through fine-tuning its parameters. The main objective is to develop a robust and reliable prediction model that can handle a wide range of SEE problems. The developed techniques are tested on twelve benchmark SEE datasets to evaluate their performance. Furthermore, a comparative analysis with state-of-the-art methods is conducted to further validate the effectiveness of the developed techniques. The findings demonstrate that the developed techniques surpass all other methods in all benchmark problems, affirming their superiority.

Keywords: Software development effort estimation; machine learning; multilayer perceptron neural network; salp swarm algorithm; genetic algorithm

Mohamad Al-Laham, Salwani Abdullah, Mohammad Atwah Al-Ma’aitah, Mohammed Azmi Al-Betar, Sofian Kassaymeh and Ahmad Azzazi, “Parameter Identification of a Multilayer Perceptron Neural Network using an Optimized Salp Swarm Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 14(6), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01406130

@article{Al-Laham2023,
title = {Parameter Identification of a Multilayer Perceptron Neural Network using an Optimized Salp Swarm Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01406130},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01406130},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {6},
author = {Mohamad Al-Laham and Salwani Abdullah and Mohammad Atwah Al-Ma’aitah and Mohammed Azmi Al-Betar and Sofian Kassaymeh and Ahmad Azzazi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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