Localization is one of the most critical issues for Wireless Sensor Networks (WSN), since numerous applications rely upon the exact location of the sensor nodes. In last two decades, many range based and range free localization algorithms have been proposed. Generally range free algorithms are more effective, but having poor localization accuracy. Distance Vector Hop (DV-Hop) algorithm has pulled more attention of scientists because of its steadiness, practicality and less equipment cost. To accomplish higher accuracy in range free localization algorithms, an Improved DV-Hop based on Teaching Learning Based Optimization (IDV-Hop based on TLBO) algorithm has been proposed. In the proposed method, normal hop size of the node is changed by refreshing a correction factor in it. For further enhancing the precision, TLBO method has been applied in IDV-Hop, since confinement is the optimization problem whose aim is to minimize the location errors in WSNs. The procedure of TLBO is partitioned into two stages: Teacher stage and Learner stage and it is free from the calculation parameters. With the assistance of limited populace possible region, IDV-Hop based on TLBO finds the normal nodes more precisely and accomplishes higher convergence rate. Simulation results demonstrate that proposed algorithm is effective and more precise in terms of localization accuracy compared to DVHop, DV-Hop based on Genetic Algorithm (GADV-Hop) and DV-Hop based on PSO (Particle Swarm Optimization) algorithms.