Particle filter (PF) is introduced to tackle the limitations of the Kalman filter which adopts Gaussian in the state and noise of the system. PFs have the problem of sample impoverishment and one approach to solve this problem is to optimize the proposal distribution shown by particles. This paper introduces a novel evolutionary PF based on Opposition based High Dimensional optimization Algorithm (OHDA) to reposition the particles of PF in high probable regions for estimation. OHDA will preserve the diversity of particles while emphasizing the more informative ones by information sharing and angular movement operators. Opposite particles are introduced in this paper to speed up the convergence of PF. Virtual forward movement by angular movement of OHDA is employed to better guide the search process. The optimized PF can improve the performance of the estimation algorithms in problems such as localization and SLAM. In robot localization problem, particles show the location of the robot in a known environment. For SLAM (Simultaneous Localization And Mapping), particles contain the location of the robot as well as estimated map of the environment. The application of the resulting evolutionary particle filter is tested in both localization and SLAM. Comparing the results of the proposed evolutionary particle filter with other algorithms confirms the efficiency of applying OHDA to PF in terms of improving estimation accuracy in the well-known Victoria park dataset and some other generated test environments. Comparing optimization algorithms on FASTSLAM and UFASTSLAM are PSO, FA, MVO, and MGWO.

粒子滤波器(PF)的引入是为了解决卡尔曼滤波器在系统状态和噪声方面采用高斯的局限性。PFs 存在样本贫乏的问题，解决这个问题的一种方法是优化粒子显示的建议分布。本文介绍了一种基于对立的高维优化算法 (OHDA) 的新型进化 PF，以在高概率区域重新定位 PF 的粒子以进行估计。OHDA 将保留粒子的多样性，同时通过信息共享和角运动算子强调信息量更大的粒子。反粒子本文引入了加速PF的收敛。采用 OHDA 角运动的虚拟向前运动来更好地指导搜索过程。优化后的 PF 可以提高估计算法在定位和 SLAM 等问题中的性能。在机器人定位问题中，粒子表示机器人在已知环境中的位置。对于 SLAM（Simultaneous Localization And Mapping），粒子包含机器人的位置以及环境的估计地图。由此产生的进化粒子滤波器的应用在定位和 SLAM 中进行了测试。将所提出的进化粒子滤波器的结果与其他算法进行比较，证实了将 OHDA 应用于 PF 在提高著名的维多利亚公园数据集和其他一些生成的测试环境中的估计精度方面的效率。比较 FASTSLAM 和 UFASTSLAM 上的优化算法是 PSO、FA、MVO 和 MGWO。