Adaptive Wind Driven Optimization

In this paper, we propose two new methods to create an adaptive Wind Driven Optimization (WDO) algorithm, both of which are shown to outperform the classical WDO method while eliminating the need for fine-tuning the coefficients of the update equations. While the classical WDO offers a simple and efficient meta-heuristic optimization algorithm, the coefficients that are inherent to the workings of the algorithm create an undesired level of complexity especially for the novice users. To alleviate this complexity and automate the coefficient selection, two adaptive Wind Driven Optimization (AWDO) methods are proposed in this paper. First method is to replace the fixed values of the coefficients with randomly generated numbers from a uniform distribution at each iteration and the second method is to optimize the selection of the coefficients with the Covariance Matrix Adaptation Evaluation Strategy (CMAES). To evaluate the performance of the proposed methods for AWDO, four well-known numerical benchmark functions from the literature are utilized and results are compared against the classical WDO. Both of new methods outperform the classical WDO while the AWDO using CMAES performs the best among of all.