Abstract
Asynchronous Differential Evolution (ADE) [1] is a derivative-free method to solve global optimization problems. It provides effective parallel realization. In this work we derive ADE with restart (ADE-R). By increasing population size after each restart, new strategy enhances its chances to locate the global minimum. The ADE-R algorithm has convergence rate comparable or better than ADE with fixed population sizes. Performance of the ADE-R algorithm is demonstrated on a set of benchmark functions.
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Zhabitskaya, E., Zhabitsky, M. (2013). Asynchronous Differential Evolution with Restart. In: Dimov, I., Faragó, I., Vulkov, L. (eds) Numerical Analysis and Its Applications. NAA 2012. Lecture Notes in Computer Science, vol 8236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41515-9_64
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DOI: https://doi.org/10.1007/978-3-642-41515-9_64
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