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Multiobjective Group Search Optimization Approach for Community Detection in Networks

Multiobjective Group Search Optimization Approach for Community Detection in Networks

Nidhi Arora, Hema Banati
Copyright: © 2016 |Volume: 7 |Issue: 3 |Pages: 21
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781466690820|DOI: 10.4018/IJAEC.2016070103
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MLA

Arora, Nidhi, and Hema Banati. "Multiobjective Group Search Optimization Approach for Community Detection in Networks." IJAEC vol.7, no.3 2016: pp.50-70. http://doi.org/10.4018/IJAEC.2016070103

APA

Arora, N. & Banati, H. (2016). Multiobjective Group Search Optimization Approach for Community Detection in Networks. International Journal of Applied Evolutionary Computation (IJAEC), 7(3), 50-70. http://doi.org/10.4018/IJAEC.2016070103

Chicago

Arora, Nidhi, and Hema Banati. "Multiobjective Group Search Optimization Approach for Community Detection in Networks," International Journal of Applied Evolutionary Computation (IJAEC) 7, no.3: 50-70. http://doi.org/10.4018/IJAEC.2016070103

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Abstract

Various evolving approaches have been extensively applied to evolve densely connected communities in complex networks. However these techniques have been primarily single objective optimization techniques, which optimize only a specific feature of the network missing on other important features. Multiobjective optimization techniques can overcome this drawback by simultaneously optimizing multiple features of a network. This paper proposes MGSO, a multiobjective variant of Group Search Optimization (GSO) algorithm to globally search and evolve densely connected communities. It uses inherent animal food searching behavior of GSO to simultaneously optimize two negatively correlated objective functions and overcomes the drawbacks of single objective based CD algorithms. The algorithm reduces random initializations which results in fast convergence. It was applied on 6 real world and 33 synthetic network datasets and results were compared with varied state of the art community detection algorithms. The results established show the efficacy of MGSO to find accurate community structures.

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