Title :
Adaptive Differential Evolution Based on New Mutation Strategy
Author :
Bi, Shujun ; Zhou, Jianjun
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.
Keywords :
Benchmark testing; Equations; Mathematical model; Optimization; Particle swarm optimization; Simulation; Vectors; differential evolution; evolutionary technique; global optimization; mutation;
Conference_Titel :
Computational and Information Sciences (ICCIS), 2011 International Conference on
Conference_Location :
Chengdu, China
Print_ISBN :
978-1-4577-1540-2
DOI :
10.1109/ICCIS.2011.64