Expert Systems with Applications: An International Journal
Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing
Computer Communications
Community detection in social networks with genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Effective Algorithm for Detecting Community Structure in Complex Networks Based on GA and Clustering
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
GA-Net: A Genetic Algorithm for Community Detection in Social Networks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Complex Network Community Detection Based on Swarm Aggregation
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
A Multi-objective Genetic Algorithm for Community Detection in Networks
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Community detection in complex networks using collaborative evolutionary algorithms
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Harmony search in water pump switching problem
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
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Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most of contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). To solve the community detection problem this study used modified harmony search algorithm (HAS), the original HAS often converges to local optima which is a disadvantage with this method. To avoid this shortcoming the HAS was combined with a Chaotic Local Search (CLS). In the proposed algorithm an external repository considered to save non-dominated solutions found during the search process and a fuzzy clustering technique was used to control the size of the repository. The experiments in synthetic and real networks show that the proposed multi-objective community detection algorithm is able to discover more accurate community structures.