Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques

Kassaymeh, Sofian; Alweshah, Mohammed; Al-Betar, Mohammed Azmi; Hammouri, Abdelaziz I.; Al-Ma’aitah, Mohammad Atwah

doi:10.1007/s10586-023-03979-y

Cited by 17 publications

(6 citation statements)

References 71 publications

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“…The effectiveness of GWO and its variants enables it to successfully tackle a wide range of real-world applications. This include electrical and electronic engineering [147], [148], [185], renewable energy [226], [181], [195], networking and communication [214], [186], mechanical engineering [220], [130], medical applications [227], [67], classification [228], [157], [229], [173], [85], chemical engineering [230], [231], petroleum engineering [129], [215], industrial engineering [232], [219], software engineering [65], civil engineering [233], [234], geotechnical & geoenvironmental Engineering [235], [236], planing & scheduling [170], [110], robotics [237], [127], image processing [238], [239], [240], and mathematical functions [123], [241], and others [242], [243], [244].…”

Section: Applications Of Grey Wolf Optimizermentioning

confidence: 99%

“…In the initial searching stage, the GWO starts with high intensity on the exploration stage, while in the last course of runs, the GWO provides more attention to the exploitation phase through the gradual changes in the positions of the threebest leaders. Therefore, GWO is able to tackle a plethora of optimization problems from a wide range of research fields, such as engineering, networking and communication, image processing, robotics, mathematics, bioinformatics, biomedical, and others [65].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, hybridization with other optimization algorithms has been employed to strike a balance between exploration and exploitation capabilities, thereby improving the quality of solutions generated by GWO. Indeed, many instances of GWO are proposed in the literature, each of which is suitable for specific research applications [66], [67], [68], [65].…”

Section: Introductionmentioning

confidence: 99%

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Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

et al. 2024

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The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.

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Section: Applications Of Grey Wolf Optimizermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

et al. 2024

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“…One type of algorithm that can be used to achieve this goal is a metaheuristic [ 18 , 19 ]. This type of algorithm can be employed as an optimizer for the learning model [ 20 , 21 ]. Metaheuristic algorithms have captured the interest of several researchers in recent years because these algorithms have the capacity to solve diverse problems in different fields [ 22 , 23 ].…”

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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“…Software engineering encompasses a systematic, methodical, and quantitative approach to the creation, operation, and maintenance of software systems. In order to ensure the effective and timely production of software products, software engineering management adopts specific actions such as planning, monitoring, measuring, and reporting [1], [2]. Conversely, software development effort estimation (SEE) represents a formidable challenge that holds significant importance in the software development process.…”

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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Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques

Cited by 17 publications

References 71 publications

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

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

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