An influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications.