一种采用改进交叉熵的多目标优化问题求解方法

针对传统交叉熵算法不能解决多目标优化问题,采用单目标交叉熵优化算法提出了改进多目标交叉熵优化(Multi-Objective Cross Entropy Optimization,MOCEO)算法。首先,采用个体选择机制来保留进化过程中的优良个体,通过精英保留策略提取优良个体分布信息以不断修正算法正态分布概率模型参数;其次,引入进化方向在正态分布群体采样过程中,引导所产生新个体在解空间中的分布使得种群朝着性能提高的方向进化;最后,为了避免陷入局部最优点在参数平滑操作过程中,定义了调节系数随机调整正态分布概率模型参数。ZDT和DTLZ系列多目标问题的测试结果表明,与经典多目标优化算法NSGA-II、SPEA2、MOEAD、PAES相比,MOCEO在超体积和反转世代距离性能指标以及进化速度等方面较好,是一种收敛速度快、寻优能力强、鲁棒性高的算法。为验证MOCEO在工程实际中的效果,将其应用于某型高速列车悬挂系统横向平稳控制系统的参数优化中,仿真结果表明:相比于NSGA-II算法,使用MOCEO优化调整控制系统参数后,车体横向平稳性指标提高4.16%,横向加速度峰值减小10.34%,横向振动加速度在1～2 Hz人体敏感频率范围内有一定改善,列车具有更好的横向平稳性能。

Two-dimensional cross entropy multi-threshold image segmentation based on improved BBO algorithm

In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.