基于状态估计反馈的策略自适应差分进化算法

借鉴闭环控制思想, 提出基于状态估计反馈的策略自适应差分进化(Differential evolution, DE)算法, 通过设计状态评价因子自适应判定种群个体所处于的阶段, 实现变异策略的反馈调节, 达到平衡算法全局探测和局部搜索的目的.首先, 基于抽象凸理论对种群个体建立进化状态估计模型, 提取下界估计信息并结合进化知识设计状态评价因子, 以判定当前种群的进化状态; 其次, 利用状态评价因子的反馈信息, 实现不同进化状态下策略的自适应调整以指导种群进化, 达到提高算法搜索效率的目的.另外, 20个典型测试函数与CEC2013测试集的实验结果表明, 所提算法在计算代价、收敛速度和解的质量方面优于主流改进差分进化算法和非差分进化算法.

Strategy Self-adaptive Differential Evolution Algorithm Based on State Estimation Feedback

Inspired by the idea of closed-loop control, a strategy self-adaptive differential evolution (DE) algorithm based on state estimation feedback is proposed, the stage of individual can be self-adaptively determined by designing the state judgment factor, and achieve the feedback adjustment of mutation strategies. Consequently, the algorithm can get a trade-off between the exploration and exploitation. Firstly, the estimation model of evolution state is established based on abstract convex theory, from which the underestimation information is extracted combining with the evolutionary information to design the state judgment factor, so that the evolution state of the current population is estimated. Secondly, according to the feedback information of the state judgment factor, the strategy in different evolution state is adaptively selected to guide the evolution of the population. Therefore, the searching efficiency of the algorithm can be improved. Additionally, experimental results of 20 benchmark functions and CEC2013 test set show that the proposed algorithm is superior to the main-stream differential evolution variants and non-differential evolution algorithms mentioned in this paper in terms of computational cost, convergence speed, and solution quality.