A Discrete Bat Algorithm for the Community Detection Problem

Hassan, Eslam Ali; Hafez, Ahmed Ibrahem; Hassanien, Aboul Ella; Fahmy, Aly A.

doi:10.1007/978-3-319-19644-2_16

Cited by 28 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the bat motion step, all the search agents move toward the best solution (position with best fitness value), and thus, leads to falling into local optima. In addition to lacking the parameters that increase its exploration ability and prevent it from falling into this problem (Hassan et al , 2015). Another determinant is that in the standard bat algorithm, the movement to a new location (a new solution) is not performed unless it is better than the current global solution and this leads to limiting the movement of search agents (Hassan et al , 2015).…”

Section: Bat Algorithm Limitationsmentioning

confidence: 99%

“…In addition to lacking the parameters that increase its exploration ability and prevent it from falling into this problem (Hassan et al , 2015). Another determinant is that in the standard bat algorithm, the movement to a new location (a new solution) is not performed unless it is better than the current global solution and this leads to limiting the movement of search agents (Hassan et al , 2015). BA cannot support the optimization problems with discrete values and needs to improve its implementation, as well as requiring different parameter tuning (Ezugwu et al , 2020).…”

Section: Bat Algorithm Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Critical analysis: bat algorithm-based investigation and application on several domains

Umar

Rashid

2021

WJE

View full text Add to dashboard Cite

Purpose The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms. Design/methodology/approach Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm). Findings Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it. Originality/value As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

show abstract

Section: Bat Algorithm Limitationsmentioning

confidence: 99%

Section: Bat Algorithm Limitationsmentioning

confidence: 99%

Critical analysis: bat algorithm-based investigation and application on several domains

Umar

Rashid

2021

WJE

View full text Add to dashboard Cite

show abstract

“…BA is another MH algorithm that has been used to develop and improve CD methods. In [132], Hassan et al proposed a new CD method based on discrete BA optimization. The method uses the modularity function as a fitness function that should be maximized.…”

Section: B Community Detection Based On Meta-heuristic Algorithmsmentioning

confidence: 99%

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

et al. 2021

View full text Add to dashboard Cite

Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, Parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions. INDEX TERMSCommunity detection; Deep learning; Complex networks; Meta-heuristic algorithms; Parallel computing.

show abstract

“…More recently, the community has started to explore the suitability of modern nature-inspired meta-heuristics for community detection in graphs. One of these methods is BA, as exposed by works such as [72] and [162]. Particularly relevant are [120] and [207], which focus on the application of BA over dynamic social networks, and in which a multi-objective community finding problem is formulated.…”

Section: Recent Work In Community Detection Using Bio-inspired Meta-hmentioning

confidence: 99%

Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

Osaba¹,

Ser

Camacho

et al. 2020

Applied Soft Computing

View full text Add to dashboard Cite

A Discrete Bat Algorithm for the Community Detection Problem

Cited by 28 publications

References 24 publications

Critical analysis: bat algorithm-based investigation and application on several domains

Critical analysis: bat algorithm-based investigation and application on several domains

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

Contact Info

Product

Resources

About