Reducing complexity in many objective optimization using community detection

Maulana, Asep; Jiang, Zhongzhou; Jing, Liu; Bäck, Thomas; Emmerich, Michael

doi:10.1109/cec.2015.7257281

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…To optimize many objectives simultaneously various approaches have been developed. Some of them aim at reducing complexity, such as Objective Reduction in Many-objective Optimization: Linear and Nonlinear Algorithms [3], Reducing Complexity in Many Objective Optimization Using Community Detection [4], and Objective Reduction Based on Nonlinear Correlation Information Entropy [5]. Other approaches are based on Evolutionary Multiobjective optimization (EMO) extended to deal with many objectives, cf.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper we will follow the approach by Maulana et al [4]. They proposed a method to reduce complexity in many objective optimization by forming groups of objective functions based on the correlation between objective functions: Objectives are either conflicting (negative correlation), supporting each other (positive correlation), or indifferent w.r.t.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, a high number of objectives can yield to a significant increase in the computational time complexity required to compute Pareto fronts. In spite of these difficulties many technique and approach have been tried to deal with many objective optimization [2,3,4,5].…”

Section: Many-objective Optimizationmentioning

confidence: 99%

“…Here the analysis proposed by Maulana et al [4] is used, where the edge weight is determined by the absolute value of the correlation coefficient. This leads to a separation of independent communities of layers.…”

Section: Problem Deffinitation Of Many-objective Community Detection In Multiplex Networkmentioning

confidence: 99%

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Modularities maximization in multiplex network analysis using Many-Objective Optimization

Maulana

Gemmetto

Garlaschelli

et al. 2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Nowadays, social network analysis receives big attention from academia, industries and governments. Some practical applications such as community detection and centrality in economic networks have become main issues in this research area. Community detection algorithm for complex network analysis is mainly accomplished by the Louvain Method that seeks to find communities by heuristically finding a partitioning with maximal modularity. Traditionally, community detection applied for a network that has homogeneous semantics, for instance indicating friend relationship between people or import-export relationships between countries etc. However we increasingly deal with more complex network and also with so-called multiplex networks. In a multiplex network the set of nodes stays the same, while there are multiple sets of edges. In the analysis we would like to identify communities, but different edge sets give rise to different modularity optimizing partitions into communities. We propose to view community detection of such multilayer networks as a many-objective optimization problem. For this apply Evolutionary Many Objective Optimization and compute the Pareto fronts between different modularity layers. Then we group the objective functions into community in order to better understand the relationship and dependence between different layers (conflict, indifference, complementarily). As a case study, we compute the Pareto fronts for model problems and for economic data sets in order to show how to find the network modularity tradeoffs between different layers.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Many-objective Optimizationmentioning

confidence: 99%

Section: Problem Deffinitation Of Many-objective Community Detection In Multiplex Networkmentioning

confidence: 99%

See 2 more Smart Citations

Modularities maximization in multiplex network analysis using Many-Objective Optimization

Maulana

Gemmetto

Garlaschelli

et al. 2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

Self Cite

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“…1, which was recently introduced in Kerschke et al (2016b). Similarly, multi-objective, multimodal problems occur also in high energy physics and quantum control (Laforge et al, 2011), drug design by docking considering energy and contact (Nicolaou and Brown, 2013) and in urban planning problems, when we want to choose a location near to different types of facilities (Maulana et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Search Dynamics on Multimodal Multiobjective Problems

Kerschke

Wang

Preuß

et al. 2019

Evolutionary Computation

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We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective subproblems. An experimental analysis compares two MO algorithms, one that explicitly relies on hypervolume gradient approximation, and one that is based on local search, both on a selection of generated example problems. We do not focus on performance but on the interaction induced by the problems and algorithms, which can be described by means of specific characteristics explicitly designed for the multimodal MO setting. Furthermore, we widen the scope of our analysis by additionally applying visualization techniques in the decision space. This strengthens and extends the foundations for Exploratory Landscape Analysis (ELA) in MO.

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