Improved differential evolution with adaptive opposition strategy

Generalized opposition-based differential evolution (GODE) is an effective algorithm for global optimization over continuous search space. However, the performance of GODE highly depends on its control parameters. To improve the performance of GODE, this paper proposes an enhanced GODE algorithm called AGODE, which employs an adaptive generalized opposition-based learning (GOBL) mechanism to automatically adjust the probability of opposition during the evolution. Experimental study is conducted on a set of 19 well-known benchmark functions. Computational results show that the proposed approach AGODE outperforms some state-of-the-art DE variants on the majority of test problems.