Feature selection algorithm for usability engineering: a nature inspired approach

Jain, Rajat; Joseph, Tania; Saxena, Anvita; Gupta, Deepak; Khanna, Ashish; Sagar, Kalpna; Ahlawat, Anil

doi:10.1007/s40747-021-00384-z

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…Jain [32] proposed a modifed GWO (MGWO) algorithm for the selection of crucial features in a hierarchical software model. As per the results, MGWO outperformed other relevant optimizers in terms of accuracy.…”

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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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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“…Jain et al developed an improved GWO for selecting the most important features in hierarchical software model (Jain et al. 2021 ). The permission set of an Android application is that it is required during installation.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

“…( 2020a ) CSA Software Usability Medium Medium High Jain et al. ( 2021 ) GWO Software Usability Medium High High Bhattacharya et al. ( 2019 ) PSO Android application Permissions Medium Low High Ni et al.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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“…The GWO algorithm is a new type of swarm intelligence optimization algorithm proposed by Mirialili et al [56] in 2014. It originates from the simulation of the predation behavior of gray wolves and achieves optimization through the processes of wolves tracking, encircling, hunting, and attacking prey [57].…”

Section: Basic Gwo Algorithmmentioning

confidence: 99%

Portfolio optimization model with uncertain returns based on prospect theory

Zhou

Tan

2021

Complex Intell. Syst.

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When investing in new stocks, it is difficult to predict returns and risks in a general way without the support of historical data. Therefore, a portfolio optimization model with an uncertain rate of return is proposed. On this basis, prospect theory is used for reference, and then the uncertain return portfolio optimization model is established from the perspective of expected utility maximization. An improved gray wolf optimization (GWO) algorithm is designed because of the complex nonsmooth and nonconcave characteristics of the model. The results show that the GWO algorithm is superior to the traditional particle swarm optimization algorithm and genetic algorithm.

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Feature selection algorithm for usability engineering: a nature inspired approach

Cited by 7 publications

References 33 publications

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

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

A survey on binary metaheuristic algorithms and their engineering applications

Portfolio optimization model with uncertain returns based on prospect theory

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