Two problems with Particle Filters (PF) are particle impoverishment and degeneracy. Resampling is introduced to solve degeneracy problem which happens when the majority of the particles have very small weight and a few particles have large weights and sample’s weight variance is too high. Resampling ignores the less informative particles by replacing them with the better ones but it can results in sample impoverishment or diversity loss problem in the particles if there is no controlling mechanism. BrunsVigia Optimization Algorithm (BVOA) is applied in this paper as an extra step to Pf in order to avoid these problems. Operators of BVOA balance between exploration and exploitation and as the result the optimized PF will put much emphasize on more informative particles while keeping the diversity among them. The optimized PF using BVOA, namely BVOA\_PF, is tested in localization problem in a simulating environment. Application of BVOA\_PF in localization and comparing the simulation results with two well-known optimization algorithms as PSO and GWO verify the efficiency of BVOA in real applications like robot localization.