Nature is of course a great source of inspiration for solving complex problems in the electronics era since it exhibits exceptionally diverse, dynamic and complex phenomenon. Wireless sensor networks (WSN) have become popular in many applications area including environmental monitoring, military and medical industries. This paper deals about how the behavioural pattern of natural living organism could be used to make algorithms for optimization techniques. In WSN the sensors are randomly deployed in the sensor field and hence estimation of the localization of each deployed node has drawn more attention by the recent researchers, It's a unique problem to identify and maximizing the coverage where the sensors need to be placed in a position so that the sensing capability of the network is fully utilized to ensure high quality of service. In order to keep the cost of sensor networks to a minimum, the use of additional hardware like global positioning system (GPS) can be avoided by the use of effective algorithms that can be used for the same. In this paper we attempted to use and compare the performance of Particle Swarm Optimization (PSO) and Shuffled frog leaping (SFLA) to estimate the optimal location of randomly deployed sensors. The results were compared and published for the usefulness of further research.