An adaptive differential evolution algorithm

The performance of Differential Evolution (DE) algorithm is significantly affected by its parameter setting. But the choice of parameters is heavily dependent on the problem characteristics. Therefore, recently a couple of adaptation schemes that automatically adjust DE parameters have been proposed. The current work presents another adaptation scheme for DE parameters namely amplification factor and crossover rate. We systematically analyze the effectiveness of the proposed adaptation scheme for DE parameters using a standard benchmark suite consisting of ten functions. The undertaken empirical study shows that the proposed adaptive DE (aDE) algorithm exhibits an overall better performance compared to other prominent adaptive DE algorithms as well as canonical DE.