The social group optimization algorithm is recently developed population based algorithm by Satapathy et al .It mimics the social behavior of the human beings in the society. The problems encountered in the society are generally non-linear and complex in nature. Typically, every individual uses their behavioral traits to solve such problems. It is also possible that the individuals may form in groups to solve some complex problems which cannot be solved with the knowledge possessed by an individual. This group formation facilitates the knowledge exchange or sharing that allows solving complex problems which require a high degree of knowledge coefficient. The knowledge coefficient of an individual and his capability to solve a problem or execute a task in life is composed of several behavioral traits of the individual itself. Some of these behavioral aspects are loyalty, fairness, team play, braveness etc.The negative shade like disloyalty of loyalty and other aspects are also listed under these traits. Based on the above aspect SGO is developed. In this talk its effectiveness to solve many optimization functions will be shown and some applications to data engg. will be discussed.