Noisy extremal optimization

Noisy extremal optimization is a new optimization-based heuristic designed to identify the community structure of complex networks by maximizing the modularity function. The extremal optimization algorithm evolves configurations that represent network covers, composed of nodes evaluated separately....

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Bibliographic Details
Published inSoft computing (Berlin, Germany) Vol. 21; no. 5; pp. 1253 - 1270
Main Authors Lung, Rodica Ioana, Suciu, Mihai, Gaskó, Noémi
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2017
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Computational Intelligence
Control
Engineering
Heuristic
Hierarchies
Iterative methods
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Modularity
Nodes
Optimization
Optimization algorithms
Robotics
Greedy Randomized Adaptive Search Procedure
Extremal Optimization
Fitness Function
Normalize Mutual Information
Community Detection
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Summary:Noisy extremal optimization is a new optimization-based heuristic designed to identify the community structure of complex networks by maximizing the modularity function. The extremal optimization algorithm evolves configurations that represent network covers, composed of nodes evaluated separately. Each iteration, a number of nodes having the worst fitness values are randomly assigned different communities. A network shifting procedure is used to induce a noise in the population as a diversity preserving mechanism. Numerical experiments, performed on synthetic and real-world networks, illustrate the potential of this approach.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1858-3