The Window Driven Optimization (WDO) Algorithm is a stochastic population-based global optimizer inspired by atmospheric motion. The Wind Driven Optimization (WDO) technique is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the ability to implement constraints on the search domain. At its core, a population of infinitesimally small air parcels navigates over an N-dimensional search space following Newton's second law of motion, which is also used to describe the motion of air parcels within the earth's atmosphere. Compared to similar particle based algorithms, WDO employs additional terms in the velocity update equation (e.g. gravitation and Coriolis forces), providing robustness and extra degrees of freedom to fine tune the optimization. In December 2015, a new self-adaptive version of the WDO called the Adaptive Wind Driven Optimization (AWDO) was developed. CMA-ES is used in an outer iteration to tune the internal parameters of WDO, freeing the user from having to guess what the optimal parameters should be.