2016
DOI: 10.1007/s10489-016-0840-9
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A discrete modified fireworks algorithm for community detection in complex networks

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Cited by 36 publications
(10 citation statements)
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“…Therefore, we can know that the three most important parts of FWA are explosion parameters, mutation parameters and selection strategy [34].…”
Section: Firework Algorithmmentioning
confidence: 99%
“…Therefore, we can know that the three most important parts of FWA are explosion parameters, mutation parameters and selection strategy [34].…”
Section: Firework Algorithmmentioning
confidence: 99%
“…Finally, we pause at several recent works where assorted bio-inspired metaheuristics have been adapted for the community detection problem. This is the case of [64], which explores the efficiency of a method never used before for community detection: the Fireworks Algorithm. The main characteristics that authors used to develop a competitive method are new initialization strategies and new mutation functions, both based on the label propagation strategy to speed up the convergence.…”
Section: Recent Work In Community Detection Using Bio-inspired Meta-hmentioning
confidence: 99%
“…In summary, as the evolution proceeds, the number of individuals in the global memory should gradually increase. From this idea, this study proposes the number of individuals in the global memory that adaptively changes with iteration as shown in (13), instead of (4).…”
Section: B Adaptive Global Memory Capacitymentioning
confidence: 99%
“…Swarm intelligence algorithms have been widely used in engineering, such as image processing [10], feature selection [11], robot obstacle avoidance [12], complex network community detection [13], cluster head selection, and coverage control of wireless sensor networks [14] [15]. Their outstanding application has attracted research interest from many scholars.…”
Section: Introductionmentioning
confidence: 99%