Hybrid differential evolution algorithm based on complex method and cloud model
Quan Pan · 2013
In order to improve the differential evolution algorithm's convergence speed and optimization accuracy,this paper proposed a new algorithm which named a hybrid differential evolution algorithm based on complex method and cloud model(HDECC).The new algorithm used the differential evolution algorithm to search the optimal area first,then introduced the complex method to accelerate the convergence rate of the algorithm and made use of cloud model to improve the optimization accuracy of the algorithm,so that it balanced the preliminary search speed and accuracy in the later stage.Finally,it used seven standard constrained optimization problems and two typical engineering applications to simulate.Experimental results show that,compared with similar algorithms,HDECC is robust in solving global optimal solution,has higher accurate numerical solution,achieves more rapid convergence rate,and maintains good stability.