Hybrid parameter adaptation strategy for differential evolution to solve real-world problems

Differential Evolution algorithm (DE) has been investigated in several studies. Indeed, it has been revealed that despite its successful search operators, DE may get trapped in local optimum due to the poor parameter configuration, and the inappropriate search operators. In this study, we introduce a resilient mutation strategy well-suited to real-world problems. Moreover, a machine learning-based parameter adaptation mechanism is proposed to configure DE parameters during the search process. The new adaptive DE has been tested to find the optimal mechanical structure of a novel electric motor topology. Furthermore, the results have been validated using the real-world problems from the CEC 2011 test suite. The results have revealed that the proposal can be competitive compared to recent adaptive DE algorithms.