一种动态自适应差分进化算法

Dynamic Adaptive Differential Evolution Algorithm

针对差分进化算法对参数和策略选择敏感而引起的收敛速度、计算代价及可靠性问题,结合抽象凸理论,提出一种动态自适应差分进化算法(DADE)。首先,通过对种群中的个体构建下界支撑面,建立目标函数的下界估计松弛模型;然后,利用下界估计松弛模型计算策略池中各策略生成的新个体的下界估计信息,进而根据下界估计信息及前期的进化经验动态自适应调整策略及其参数,并指导种群更新;最后,根据进化结果更新下界支撑面。6个标准测试函数的数值实验结果表明了所提算法的有效性。

To solve the problems of convergence speed,computational cost and reliability caused by the choice of parameters and strategies,a dynamic adaptive differential evolution algorithm was proposed in this paper incorporating the abstract convexity theory.Firstly,an underestimate relaxed model of the objective function is built by constructing the supporting hyperplanes for the individuals of the population.Then,the underestimate values of the trial individuals that generated by the strategies in the strategies pool can be obtained from the underestimate relaxed model.So the parameters and strategies can adjust adaptively according to the underestimate and the evolutionary experience before.In addition,the underestimate also can be used to guide the update process.Finally,the underestimate supporting hyperplanes are updated according to the result of evolutionary.Numerical experiment results of the six benchmark problems verify the effectiveness of the proposed algorithm.