Self-adaptive cluster-based differential evolution with an external archive for dynamic optimization problems

Authors:
Udit Halder;Dipankar Maity;Preetam Dasgupta;Swagatam Das
Affiliations:
Dept. of Electronics & Tele-comm. Engineering, Jadavpur University, Kolkata, India;Dept. of Electronics & Tele-comm. Engineering, Jadavpur University, Kolkata, India;Dept. of Electronics & Tele-comm. Engineering, Jadavpur University, Kolkata, India;Electronics and Comm. Sciences Unit, Indian Statistical Institute, Kolkata, India
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Year:
2011

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Abstract

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.