Preserving rotation invariant properties in differential evolution algorithm

Differential evolution (DE) is an efficient and powerful population-based stochastic direct search method for solving optimization problems over continuous space. It uses both crossover and mutation for producing offspring. Mutation is rotation-invariant while crossover is not rotation-invariant. As a result, the performance of DE degrades in problems with strong linkage among variables. In this paper, we propose a new DE algorithm that uses rotation-invariant crossover operators to achieve better optimization performance when solving rotated problems. The proposed algorithm has been examined on a test-suite of 12 benchmark functions. Experimental results have demonstrated the effectiveness of the proposed algorithm.