基于卡尔曼滤波预测策略的动态多目标优化算法

Dynamic multi-objective optimization algorithm based on Kalman filter prediction strategy

为利用种群历史信息更有效地处理动态多目标优化的环境变化问题,提出了一种基于卡尔曼滤波预测并修正种群中心点位置的动态多目标优化算法。当环境变化后,利用卡尔曼滤波预测模型结合上一时刻的中心点预测当前时刻的种群中心点,使用近似理想Pareto最优解集中心点对该预测值进行误差修正,并基于修正后的中心点生成新的个体以重新初始化种群。为增加种群多样性,在算法运行期间从搜索空间随机生成5个新的个体,并随机替换当前种群中相应数量的个体。将本文算法与其他动态多目标优化算法在多个测试函数中进行了对比实验,结果表明,本文算法在整个进化过程中,改进的逆世代距离(MIGD)值都相对比较小,逆世代距离(IGD)值总体上比对比算法小,计算耗时与对比算法相当。

In order to deal with the environmental changes of dynamic multi-objective optimization more effectively,a dynamic multi-objective optimization algorithm based on Kalman filter prediction strategy is proposed.In the evolution process,a new calculation method is used to calculate the population center point.When the environment changes,the Kalman filter prediction model is used to predict the current population center point,and the approximate true Pareto center point of optimal solution set is used to correct the prediction value,and new individuals are generated based on the modified center point to reinitialize the population during the running of the algorithm;In order to increase the diversity of the population,five new individuals are randomly generated from the search space during the operation of the algorithm,and the corresponding number of individuals in the current population are randomly replaced.Compared with other dynamic multi-objective optimization algorithms in multiple test functions,the results show that the value of Modified Inverted Generational Distance(MIGD)in the whole evolution process is relatively small,and the value of Inverted Generational Distance(IGD)in the evolution is generally smaller than that of the contrast algorithm,and the calculation time is equivalent to that of the comparison algorithm.