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\iteman{io-port 06065811}
\itemau{Halder, Udit; Maity, Dipankar; Dasgupta, Preetam; Das, Swagatam}
\itemti{Self-adaptive cluster-based differential evolution with an external archive for dynamic optimization problems.}
\itemso{Panigrahi, Bijaya Ketan (ed.) et al., Swarm, evolutionary, and memetic computing. Second international conference, SEMCCO 2011, Visakhapatnam, Andhra Pradesh, India, December 19--21, 2011. Proceedings, Part I. Berlin: Springer (ISBN 978-3-642-27171-7/pbk). Lecture Notes in Computer Science 7076, 19-26 (2011).}
\itemab
Summary: In this paper we propose a self adaptive cluster based Differential Evolution (DE) algorithm to solve the Dynamic Optimization Problems (DOPs). We have enhanced the classical DE to perform better in dynamic environments by a powerful clustering technique. During evolution, the information gained by the particles of different clusters is exchanged by a self adaptive strategy. The information exchange is done by re-clustering, and the cluster number is updated adaptively throughout the optimization process. To detect the environment change a test particle is used. Moreover, to adapt the population in new environment an External Archive is also used. The performance of SACDEEA is evaluated on GDBG benchmark problems and compared with other existing algorithms.
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\itemut{differential evolution; dynamic optimization problem (DOP); self adaptive clustering}
\itemli{doi:10.1007/978-3-642-27172-4\_3}
\end