摘要：

为解决差分进化算法求解高维优化问题时效率低下的问题,提出一种正交动态差分进化算法(ODDE),通过动态差分进化框架增强全局搜索能力,利用基于正交实验设计的正交交叉算子加强局部空间搜索能力。基于9个标准测试函数,选择30,100,300,500这4种维度对差分进化算法、动态差分进化算法、正交差分进化算法和ODDE算法进行对比实验,结果表明,ODDE算法的求解精度和收敛速率均优于对比算法,可广泛用于求解工程应用中的高维优化问题。

关键词: 高维优化问题, 动态差分进化, 正交设计, 局部搜索, 正交交叉

Abstract:

In order to enhance the performance of Dynamic Differential Evolution(DDE) for solving high dimensional optimization problems,an Orthogonal Dynamic Differential Evolution(ODDE) algorithm is proposed.ODDE is based on the framework of DDE algorithm,so it has very powerful global search ability.At the same time,orthogonal crossover operator based on orthogonal experiment design method is used to enhance the local search ability of algorithm.Nine commonly used benchmark problems with different dimensional size 30,100,300 and 500 are used to evaluate the performance of ODDE which is compared with Differential Evolution(DE),DDE,Orthogonal Crossover Differential Evolution(OXDE) algorithm.Numerical results show that the performance of solving accuracy and convergence rate of ODDE is superior to other algorithms,it can be widely used to solve high-dimensional optimization problems in engineering application.

Key words: high-dimensional optimization problem, Dynamic Differential Evolution(DDE), orthogonal design, local search, orthogonal crossover

中图分类号: 

• TP18