ABSTRACT
Several WSN applications collect data about physical activity in many harsh environments and the data collected without location information become useless. Hence, to solve multidimensional optimization problems such as localization, swarm intelligent algorithms are used. Nature-inspired metaheuristics algorithm extends high-performance more than the traditional approaches. Research is randomly spread on experimenting on quite a few distinct species of animal behavior, searching technique, and mating process. In this chapter we propose an innovative hybrid Nelder-Mead Butterfly Optimization Algorithm (NM-BOA) as meritorious and ideal localization algorithm for WSN. The proposed algorithm amalgamates the BOA and Nelder-Mead simplex method for precise node localization. The proposed is compared with standard Butterfly optimization algorithm, Firefly algorithm, Grasshopper optimization algorithm, and Particle swarm optimization. The end results reveal that the proposed model achieves faster convergence rate, low localization error, minimum computation time, and extended lifetime of the network.
