Adaptive Differential Evolution Based on New Mutation Strategy

Authors:
Shujun Bi;Jianjun Zhou
Affiliations:
-;-
Venue:
ICCIS '11 Proceedings of the 2011 International Conference on Computational and Information Sciences
Year:
2011

Citing 0
Cited 1

Automatic generation of evolutionary operators: a study with mutation strategies for the differential evolution

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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Abstract

In this paper, an adaptive differential evolution (DE) algorithm based on new mutation strategy is proposed to solve optimization problems. The proposed approach is called ANMDE which employs a self-adjust control parameter mechanism and a new mutation strategy. In order to verify the performance of ANMDE, several well-known benchmark functions are selected in the experiments. Simulation results show that our approach outperforms standard DE and two other improved DE variant.