Parameter Estimation of Software Reliability Growth Models: A Comparison Between Grey Wolf Optimizer and Improved Grey Wolf Optimizer

Musa, Abubakar Ahmad; Imam, Sukairaj Hafiz; Choudhary, Ankur; Agrawal, Arun Prakash

doi:10.1109/confluence51648.2021.9377194

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…In addition, BGWO [27], which used an adaptation function as a local search strategy, performed better than IGWO [28], which did not use a balancing strategy, although both algorithms outperformed GWO. This shows the importance of balancing local search and search for the algorithm.…”

Section: Related Workmentioning

confidence: 97%

Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

Lin

2023

IEEE Access

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The Grey Wolf Optimization Algorithm (GWO) is an algorithm that replicates the leadership and foraging mechanisms of the natural grey wolf, which excels at solving problems in a variety of domains. However, the algorithm tends to converge on local optimal and has a slow convergence rate. This paper proposes an enhanced Grey Wolf optimization algorithm (HTGWO) based on hyperbolic tangent inertia weights to solve this problem. HTGWO employs inertia weight based on hyperbolic tangent functions to balance GWO's global and local search capabilities. HTGWO has a quicker convergence rate and more accurate solutions than GWO. Five classical test functions construct comparative experiments between HWGWO and five classical intelligent optimization algorithms. The comparison results indicate that HWGWO has superior convergence speed, solution precision, and stability compared to the other five classical intelligent optimization algorithms. Moreover, experimental evidence suggests that HWGWO is more effective at solving multi-modal than uni-modal functions.

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Section: Related Workmentioning

confidence: 97%

Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

Lin

2023

IEEE Access

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“…Also, for the accurate estimation of software faults, the authors developed a modifed sigmoid model (MSM). Musa [34] adopted the Improved GWO (IGWO) to estimate the optimum parameters for SRGMs. Te proposed method utilizes seven real-world failure datasets.…”

Section: Applications Of Gwo Woa Hho and Mfo In Softwarementioning

confidence: 99%

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

et al. 2023

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Swarm intelligence algorithms are metaheuristics inspired by the collective behavior of species such as birds, fish, bees, and ants. They are used in many optimization problems due to their simplicity, flexibility, and scalability. These algorithms get the desired convergence during the search by balancing the exploration and exploitation processes. These metaheuristics have applications in various domains such as global optimization, bioinformatics, power engineering, networking, machine learning, image processing, and environmental applications. This paper presents a systematic literature review (SLR) on applications of four swarm intelligence algorithms i.e., grey wolf optimization (GWO), whale optimization algorithms (WOA), Harris hawks optimizer (HHO), and moth-flame optimizer (MFO) in the field of software engineering. It presents an in-depth study of these metaheuristics’ adoption in the field of software engineering. This SLR is mainly comprised of three phases such as planning, conducting, and reporting. This study covers all related studies published from 2014 up to 2022. The study shows that applications of the selected metaheuristics have been utilized in various fields of software engineering especially software testing, software defect prediction, and software reliability. The study also points out some of the areas where applications of these swarm intelligence algorithms can be utilized. This study may act as a guideline for researchers in improving the current state-of-the-art on generally adopting these metaheuristics in software engineering.

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Identifying groundwater contamination sources based on the hybrid grey wolf gradient algorithm and deep belief neural network

et al. 2022

Stoch Environ Res Risk Assess

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Parameter Estimation of Software Reliability Growth Models: A Comparison Between Grey Wolf Optimizer and Improved Grey Wolf Optimizer

Cited by 6 publications

References 26 publications

Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

Identifying groundwater contamination sources based on the hybrid grey wolf gradient algorithm and deep belief neural network

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