A quantum differential evolution algorithm for function optimization
Qiuyan Xu, Jun Guo · 2010
In this paper, we propose a quantum differential evolution (QDE) algorithm for function optimization, which can improve the performance of differential evolution (DE) algorithm. First, the algorithm does some basic operations, such as mutation, crossover and selection of vectors. And then a quantum computing method is utilized to search the global optimal solutions, which avoids falling into the local minimums. Our experimental results show the proposed algorithm is feasible. And compared with other algorithms, the proposed algorithm is more effective. Furthermore it can improve the speed of convergence.