Experimental Study on Differential Evolution Strategies

Differential evolution (DE) algorithm has been proven to be a simple and efficient evolutionary algorithm for global optimization over continuous spaces, which is widely used in both benchmark test functions and real-world applications. Like genetic algorithms, differential evolution algorithm uses three typical operators to search the solution space: crossover, mutation and selection. Among these operators, mutation plays a key role in the performance of differential evolution algorithm and there are several mutation variants often used, which constitute several corresponding differential evolution strategies. By means of experiments, this paper investigates the relative performance of different differential evolution algorithms for global optimization under different differential evolution strategies respectively. In simulation studies, De Jongpsilas test functions have been employed, and some conclusions are drawn.