Abstract
Multipopulational metaheuristic methods have been used to solve a variety of problems. The use of multiple populations evolved in parallel and exchanging data according to a particular communication strategy is known to mitigate premature convergence, enlarge diversity of the populations, and generally improve the results obtained by the methods maintaining a sole panmictic population of candidate solutions. Moreover, multipopulational algorithms can be easily parallelized and efficiently accelerated by contemporary multicore and distributed architectures. In this work, we study two populational real-parameter optimization metaheuristics in a traditional and multipopulational configuration, and propose a new heterogeneous multipopulational approach. The usefulness of the new method is briefly evaluated on experiments with several well known test functions for real-parameter optimization.
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Snášel, V., Krömer, P. (2015). Multipopulational Metaheuristic Approaches to Real-Parameter Optimization. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_11
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DOI: https://doi.org/10.1007/978-3-319-12286-1_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12285-4
Online ISBN: 978-3-319-12286-1
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