Improving SHADE with Center-based Mutation for Large-scale Optimization

Differential Evolution is a powerful and efficient approach for numerical optimization. A Success-History Based Parameter Adaptation (SHADE) is the recent variant of the adaptive DE that utilizes a historical performance of the successful control parameter. In this paper, we propose a center-based mutation for SHADE algorithm (CSHADE). In this mutation scheme, the base vector for SHADE's mutation is replaced with center-based sampled candidate solution using the normal distribution. The proposed method is evaluated on CEC-2010 and CEC-2013 LSGO benchmark functions with dimension 1000. The experimental results show that CSHADE outperforms SHADE algorithm over the majority of benchmark functions in terms of solution accuracy.