Differential Evolution based on adaptive mutation
Xiaofeng Miao, Fan Pan-guo, Jiangbo Wang, Chuanwei Li · 2010
Differential Evolution (DE) is a novel evolutionary computation technique, which has attracted much attention and wide applications for its simple concept, easy implementation and quick convergence. In order to enhance the performance of classical DE, a new DE algorithm, namely AMDE, is proposed by using an adaptive mutation. In AMDE, the mutation step size is dynamically adjusted in terms of the size of current search space. To verify the performance of the proposed approach, we test AMDE on six well-known benchmark functions. The simulation results show that AMDE performs better than other three evolutionary algorithms on majority of test functions.