摘要
为了改善差分进化算法的收敛速度和优化精度,提出一种基于复形法和云模型的差分进化混合算法(HDECC)。该算法使用差分进化算法搜索局部最优域,引入复形法和云模型来加快算法的收敛速度和提高算法优化精度,使算法的初期搜索速度和之后的优化精度得到相互平衡。最后,使用七个标准约束优化问题和两个典型工程应用实例进行实验仿真,实验结果表明,与同类算法比较,HDECC算法全局搜索能力强、优化精度高、收敛速度快,且算法更稳定。
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 numeri- cal solution, achieves more raDid convergence rate. and maintains good stability.
出处
《计算机应用研究》
CSCD
北大核心
2013年第10期2981-2985,共5页
Application Research of Computers
基金
西部交通建设科技项目(2011318740240)
关键词
差分进化
复形法
云模型
混合
算法
differential evolution（ DE ） i complex method
cloud model
hybrid
algorithm
