Journal
IEEE ACCESS
Volume 6, Issue -, Pages 22728-22743Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2724073
Keywords
Chaos; dynamic group optimization; convergence; heuristic algorithms
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Funding
- University of Macau, RDAO, through Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance [MYRG2016-00069-FST]
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Recently, a new algorithm named dynamic group optimization (DGO) has been proposed, which is developed to mimic the behaviors of animal and human group socializing. However, one of the major drawbacks of the DGO is the premature convergence. Therefore, in order to deal with this challenge, we introduce chaos theory into the DGO algorithm and come up with a new chaotic dynamic group optimization algorithm (CDGO) that can accelerate the convergence of DGO. Various chaotic maps are used to adjust the update of solutions in CDGO. Extensive experiments have been carried out, and the results have shown that CDGO can be a very promising tool for solving optimization algorithms. We also demonstrated good results based on real world data, where, in particular, solving multimedia data clustering problems.
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