With the emergence of the Internet of Things (IoT) and machine to machine (M2M) communications, massive growth in the IoT-enabled wireless sensor node deployment is expected in the near future. The critical challenges for the sensor network include energy efficiency, optimum route calculation, and the overall transmission cost. To avoid the bias toward one of the objectives and also to facilitate ease of position updating, we propose a novel multi-objective optimisation (MOO) agent based on particle swarm grey wolf optimisation (PSGWO) and inverse fuzzy ranking. We initially developed an enhanced PSGWO model, and then it is utilised for the development of population and multi-criteria based soft computing algorithm, called fuzzy PSGWO. The performance of the proposed algorithm is validated and compared with the well-known techniques; for the proposed algorithm, residual energy of the nodes is much higher than that of other algorithms, and save up to 48% energy along with smaller variation in the standard deviation. The results also demonstrate the smaller average values of fitness function and computationally efficient capabilities of the proposed algorithm. Copyright © 2021 Inderscience Enterprises Ltd.