This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.
The butterfly optimization algorithm (BOA) is a recent successful metaheuristic swarm-based optimization algorithm. The BOA has attracted scholars' attention due to its extraordinary features. Such as the few adaptive parameters to handle and the high balance between exploration and exploitation. Accordingly, the BOA has been extensively adapted for various optimization problems in different domains in a short period. Therefore, this paper reviews and summarizes the recently published studies that utilized the BOA for optimization problems. Initially, introductory information about the BOA is presented to illustrate the essential foundation and its relevant optimization concepts. In addition, the BOA inspiration and its mathematical model are provided with an illustrative example to prove its high capabilities. Subsequently, all reviewed studies are classified into three main classes based on the adaptation form, including original, modified, and hybridized. The main BOA applications are also thoroughly explained. Furthermore, the BOA advantages and drawbacks in dealing with optimization problems are analyzed. Finally, the paper is summarized in conclusion with the future directions that can be investigated further.
The COVID-19 pandemic increases the reliance on video conferencing applications for learning. Accessible video conferencing applications with good learning features can help people with visual impairment when they participate in online classes. This paper investigates the accessibility limitations and the available learning features of the top two current video conferencing applications, namely Zoom and MS Teams. A task-based expert review and a blind user evaluation are conducted using Web Content Accessibility Guidelines 2.1. In addition, the study identifies the application with the better learning features based on Universal Design for Learning guidelines. A set of recommendations are outlined for developing better inclusive video conferencing applications for people with visual impairment. The presented ideas can be applied to enhance the learning experience of people with visual impairment.
Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.
In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.
Algorithms , Data Mining , Animals , Benchmarking , Rats
In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
