Abstract��In order to solve the problem that high dimensional optimization problem is hard to optimize and time-consuming, a Differential Evolution for High Dimensional optimization problem (DEHD) was proposed. By introducing coevolutionary to differential evolution, a new coevolution scheme was adopted, which consisted of state observer and random grouping strategy. Specifically, state observer activated random grouping strategy according to the feedback of search status while random grouping strategy decomposed high dimensional problem into several smaller ones and then evolved them separately. The scheme enhanced the algorithm's search speed and effectiveness. The experimental results show that the proposed algorithm is effective and efficient while solving various high dimensional optimization problems. In particular, its search speed improves significantly. Therefore, the proposed algorithm is competitive on separable high dimensional problems.
2014, Vol. 34 
