Real-world dynamic optimization using an adaptive-mutation compact genetic algorithm

While the interest in nature inspired optimization in dynamic environments has been increasing constantly over the past years, evaluations of some of these optimization algorithms are based on artificial benchmark problems. Little has been done to carry-out these evaluation using a real-world dynamic optimization problems. This paper presents a compact optimization algorithm for controllers in dynamic environments. The algorithm is evaluated using a real world dynamic optimization problem instead of an artificial benchmark problem, thus avoiding the reality gap. The experimental result shows that the algorithm has an impact on the performance of a controller in a dynamic environment. Furthermore, results suggest that evaluating the algorithm´s candidate solution using an actual real-world problem increases the controller´s robustness.