A Hybrid Salp Swarm Algorithm with Artificial Neural Network Model for Predicting the Team Size Required for Software Testing Phase

Kassaymeh, Sofian; Abdullah, Salwani; Alweshah, Mohammed; Hammouri, Abdelaziz I.

doi:10.1109/iceei52609.2021.9611128

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…However, several irrelevant genes are usually included in the generated gene expression, which makes disease detection more difficult. Also, according to experts in the field of AI, high dimensionality could deceive the learning algorithm and have a negative impact on the generalization mechanism because several noisy genes may be included in the learning mode [ 7 , 8 ]. Hence, FS is an important step in the classification process because it helps to minimize both the size and redundancy of gene expression data [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Among the techniques that have been used in this area are metaheuristic algorithms. Due to the increasing number of metaheuristics that have been proposed and their efficiency in achieving exact outcomes, metaheuristic algorithms are frequently used for optimization [ 27 , 28 ]. Nowadays, they are often used alone or paired with another method to ensure that the right solutions are found [ 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

Alweshah,

Aldabbas,

Abu-Salih

et al. 2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

Alweshah,

Aldabbas,

Abu-Salih

et al. 2023

Heliyon

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“…Conversely, software development effort estimation (SEE) represents a formidable challenge that holds significant importance in the software development process. SEE can be defined as the process of constructing a model that aids software engineers in determining the cost of a software project prior to or at the commencement of the software development process [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…One common type of ANN is the Multilayer Perceptron Neural Network (MLP), which is widely employed in addressing classification and prediction problems. Additionally, MLP exhibits excellent capability for handling non-linear and complex engineering problems [2], [3].…”

Section: Introductionmentioning

confidence: 99%

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

Al-Laham¹,

Abdullah²,

Al-Ma’aitah³

et al. 2023

IJACSA

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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-ofthe-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.

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“…Metaheuristic algorithms have recently been developed in many categories. There has not been a consistent and detailed de nition of the word metaheuristic until recently, and different de nitions have been suggested [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

Alweshah

Jebril

Alkhalaileh

et al. 2022

Preprint

Self Cite

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Feature selection (FS) is an important preprocessing step that has been commonly used in several fields to improve the performance of learning algorithms. In the field of medical data mining, a huge number of features are used in diagnosing disease, but these features have a lot of non-relevant weak correlations and redundant characteristics, which causes a number of problems that adversely affect diagnostic predictive accuracy. Work on FS has grown extensively many fields due to increased demand for methods that can reduce the dimensionality of data by choosing the best subset of features according to specific criteria in order to maximize prediction accuracy and minimize irrelevant features. In recent times, metaheuristics have been preferred over conventional optimization methods for solving FS problems in order to try to obtain an almost optimal solution in a finite time. Metaheuristics are general-purpose algorithms that can be used to solve almost any optimization problem because they generate “appropriate” solutions in a reasonable amount of time, which is especially useful when seeking to solve complicated problems. Many popular implementations have shown the utility of metaheuristics in different ways by contrasting their performance on well-known problems with that of other algorithms or applications .There are many metaheuristic algorithms in the literature such as those based on swarm intelligence, including particle swarm optimizations and ant colony optimization. The major objective of this research is to provide an increased degree of accuracy to resolve FS problems by conducting different experiments using a metaheuristic algorithm, namely the heap-based optimizer algorithm (HBO). The HBO is used with a k-nearest neighbor classifier in a wrapper to improve the FS process. The performance of the proposed method is evaluated and compared against seven approaches in the literature that are applied on nine high-dimensional data sets that contain, a low number of samples and multiple classes. The findings reveal that the HBO decreases the number of features for classification tasks, and is able to achieve high accuracy in two data sets as compared to the other approaches, the BHBO achieved the best convergence speed as compared to the competing methods. It is therefore concluded that the proposed HBO method can be used to optimize the FS process, whether in terms of classification accuracy or selection size.

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A Hybrid Salp Swarm Algorithm with Artificial Neural Network Model for Predicting the Team Size Required for Software Testing Phase

Cited by 11 publications

References 19 publications

Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

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

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

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