A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering. (23rd February 2023)

• Record Type: Journal Article
• Authors: Zeb, Alam; Din, Fakhrud; Fayaz, Muhammad; Mehmood, Gulzar; Zamli, Kamal Z.
• Other Names: Afan Haitham Abdulmohsin Academic Editor.
• Is Part Of: Complexity. Volume 2023(2023)
• Publication Date: 2023-02-23
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2023/4577581
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.