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

Osaba, Eneko; Ser, Javier Del; Camacho, David; Bilbao, Miren Nekane; Yang, Xin-She

doi:10.1016/j.asoc.2019.106010

Cited by 56 publications

(23 citation statements)

References 168 publications

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“…In our proposed method, the two criteria of Normalized Mutual Information (NMI) and the accuracy of community discovery performance based on the accuracy of user clustering were adopted. e normalized common information criterion is used to compare the modular structure of communities discovered in the social network, which is inspired by information theory [47]. is criterion…”

Section: Performance Evaluationmentioning

confidence: 99%

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Peng

Rastgari

Navaei

et al. 2021

Mathematical Problems in Engineering

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Social network aims to extend a widespread framework to communicate users and find alike people with common features, easier and faster. As people usually experience in everyday life, social communication can be formed from common groups with almost identical properties. Detecting such groups or communities is a challenging task in various fields of social network analysis. Many researchers intend to develop algorithms that work effectively and efficiently on social networks. It is believed that the most influential user in a community that had been followed by similar users could be a central point of a community or cluster, and the similar user would be members of the community. Research studies tend to increase intracommunity similarity and decrease intercommunity similarity to improve the performance of the community detection methods by finding such influential users accurately. In this paper, a hybrid metaheuristic method is proposed. In the proposed method called trust-based community detection using artificial bee colony by feature fusion (TCDABCF), we use a fusion approach combined with artificial bee colony (ABC) to improve the accuracy of the community detection task. In this approach, not only the social features of users are considered but also the relationship of trust between users in a community is also calculated. So, the proposed method can lead to finding more precise clusters of similar users with influential users in the center of each cluster. The proposed method uses the artificial bee colony (ABC) to find the influential users and the relation of their followers accurately. We compare this algorithm with nine state-of-the-art methods on the Facebook dataset. Experimental results show that the proposed method has obtained values of 0.9662 and 0.9533 for NMI and accuracy, respectively, which has improved in comparison with state-of-the-art community detection methods.

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Section: Performance Evaluationmentioning

confidence: 99%

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Peng

Rastgari

Navaei

et al. 2021

Mathematical Problems in Engineering

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“…With respect to applications, CS has been extensively applied to many domains, such as neural networks [87], image processing [88], nonlinear systems [89,90], network structural optimization [91], agriculture optimization [92], engineering optimization [93], and scheduling [94]. These applications indicate that CS algorithm is an effective and efficient optimizer for solving some real-world problems.…”

Section: Related Workmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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“… approximation of triangular and trapezoidal membership functions with exponential functions [90] or using approximation approaches transferred from PIand PID-fuzzy controllers,  least-squares fitting by the proper definition of an optimization problem and its solving by classical [91][92][93][94] or nature-inspired [95][96][97][98] optimization algorithms.…”

Section: Stability Analysis Approachmentioning

confidence: 99%

A Center Manifold Theory-Based Approach to the Stability Analysis of State Feedback Takagi-Sugeno-Kang Fuzzy Control Systems

Precup

Preitl²,

Petriu³

et al. 2020

FU Mech Eng

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The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system.

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

Cited by 56 publications

References 168 publications

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

A Center Manifold Theory-Based Approach to the Stability Analysis of State Feedback Takagi-Sugeno-Kang Fuzzy Control Systems

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