Proposal of Adaptive Randomness in Differential Evolution
Junya Tsubamoto, Akira Notsu, Seiki Ubukata, Katsuhiro Honda · 2020
Differential evolution (DE) is a widely used optimization algorithm, which can achieve high accuracy with a simple mechanism, but sometimes have only limited performances due to its simplicity. In order to mitigate the inappropriate effect of poor initial search points, it is known that adding a random search to DE contributes to obtain better results than normal DE. However, it is inefficient to perform many random searches when the search process is almost converged. In this study, we propose a novel method of DE with Adaptive Randomness (DEAR), which is a hybrid of two promising algorithms of DIEtoDE and SaDE, and can adaptively change the frequency of random search maintaining efficiency. Numerical experiments demonstrated that the proposed method can identify better solutions than other comparative methods.