Nature-inspired techniques such as the Genetic Algorithm (GA) , Ant Colony Optimization (ACO) , and Particle Swarm Optimization (PSO)  have been shown to be some of the most effective global optimization strategies. Consequently, these techniques are currently in widespread use throughout the scientific and engineering communities. In this paper, we introduce a new type of global optimization algorithm that is inspired by the motion of wind in the Earth's atmosphere. We call this new nature-inspired technique Wind-Driven Optimization (WDO). WDO is a population based iterative heuristic global optimization technique for multi-dimensional problems. A population of infinitesimally small air parcels are distributed throughout an N-dimensional problem space and assigned random velocities such that the positions of air parcels are updated at each iteration based on the physical equations that govern large-scale atmospheric motion.