A multi-objective ant colony optimization algorithm for community detection in complex networks

Sani, Naeem Shahabi; Manthouri, Mohammad; Farivar, Faezeh

doi:10.1007/s12652-018-1159-7

Cited by 45 publications

(13 citation statements)

References 48 publications

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“…Upon the completion of each iteration, the ants update the pheromone trails along the length of path planning. In the available literature, the ACO has been applied mostly to society detection with single objective (Shahabi Sani, Manthouri & Farivar, 2020), while it has been applied to a multi-objective ACO optimization using decomposition (Mu et al, 2019). In fact, ant colony inspired researchers in how ants find the best route to food source.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

Qawqzeh

Alharbi

Jaradat

et al. 2021

PeerJ Computer Science

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Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

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Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

Qawqzeh

Alharbi

Jaradat

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…This helps other ants to find the shortest path to the food source through chemical pheromones left on the ground. ACO has been utilized to a variety of optimization problems in various domains and applications, e.g., CN applications [106], DL applications [107], internet-of-things applications [108], etc.…”

Section: A Common Meta-heuristic Algorithmsmentioning

confidence: 99%

“…The CD solutions are first initialized and then tuned and addressed by the three search phases. In the same direction, the multi-objective ACO algorithm has been adapted to discover communities in CNs in [106]. Two objective functions are used: community score and community fitness, which measure the density of groups and minimize the external relations, respectively.…”

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

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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.

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“…In addition, a discrete bat algorithm has been used to estimate the global optimality of community detection within complex networks (Song, Mingbo, Xuehai, Wei, & Ke, 2016). Finally, a novel multi-objective optimization algorithm using an ant colony algorithm was used to confront the community detection problem (Shahabi Sani, Manthouri, & Farivar, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm

Ardjmand

Young

Almasarwah

2021

International Journal of Operations Research and Information Systems

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Detecting the communities that exist within complex social networks has a wide range of application in business, engineering, and sociopolitical settings. As a result, many community detection methods are being developed by researchers in the academic community. If the communities within social networks can be more accurately detected, the behavior or characteristics of each community within the networks can be better understood, which implies that better decisions can be made. In this paper, a discrete version of an unconscious search algorithm was applied to three widely explored complex networks. After these networks were formulated as optimization problems, the unconscious search algorithm was applied, and the results were compared against the results found from a comprehensive review of state-of-the-art community detection methods. The comparative study shows that the unconscious search algorithm consistently produced the highest modularity that was discovered through the comprehensive review of the literature.

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A multi-objective ant colony optimization algorithm for community detection in complex networks

Cited by 45 publications

References 48 publications

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

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

Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm

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