一种求解约束优化问题的自适应差分进化算法

摘要

The adaptive operator selection method is used to solve the global optimization problem and multi-objec-tive optimization problem of differential evolution algorithm.However,it is difficult to find a way to properly allocate credit for the adaptive operator selection in solving the constrained optimization problem.In order to realize the adaptive strategy selection in differential evolution,we present a combined population based adaptive fitness method to achieve the credit assignment of mutate strategies for constrained optimization problems and use probability matching method to select the mutate strategy adaptively.And we also set the mutation scaling factor and the crossover rate adaptively to improve the success rate of the algorithm.Experimental results show that the algorithm has higher accuracy and convergence speed comparing to CODEA/OED,ATMES,εBBO-dm,COMDE and εDE.We also test and verify the effectiveness of the adap-tive method.The algorithm can be used in forecasting,quality control,accounting process,and other scientific and engineer-ing applications.%自适应算子选择方式已被用于差分进化算法求解全局优化问题及多目标优化问题，然而在求解约束优化时难于为自适应算子选择方式找到一种方式来恰当分配信用。为此，本文提出了一种基于混合种群的自适应适应值方式来对约束优化问题中变异策略进行信用分配并采用概率匹配方法自适应选择差分变异策略，同时对算法变异缩放因子与交叉率进行自适应设置提高算法的成功率。实验结果表明算法在求解约束优化问题相比于CODEA／OED， ATMES，εBBO-dm，COMDE 以及εDE算法有较高的收敛精度及收敛速度，同时验证了自适应方式的有效性。该算法可用于预报、质量控制、会计过程等科学和工程应用领域。