Differential evolution with quasi-reflection-based mutation

Differential evolution (DE) is one of the most successful evolutionary algorithms. However, the performance of DE is significantly influenced by its mutation strategies. Generally, different mutation strategies may obtain different search directions. The improper search direction misleads the search and results in the poor performance of DE. Therefore, it is vital to consider the search direction when designing new mutation strategies. Based on this consideration, in this paper, the quasireflection-based mutation is proposed to enhance the performance of DE. The quasi-reflection-based mutation is able to provide the promising search direction to guide the search. To extensively evaluate the performance of our approach, 30 benchmark functions are chosen as the test suite. Combined with SHADE, Re-SHADE is presented. Compared with different advanced DE methods, Re-SHADE can obtain better results in terms of the accuracy and the convergence rate. Additionally, further experiments on the CEC2013 test suite also confirm the effectiveness of the proposed method.