Guest Editorial: Special Issue on Theory, Algorithms, and Applications for Hybrid Intelligent Dynamic Optimization

Dynamic optimization problems are pervasive in various fields, ranging from chemical process control to aerospace, autonomous driving, physics, robotics, and beyond. These problems involve optimizing a dynamic system considering inputs, parameters, constraints, and a cost function. For dynamic optimization, two broad classes of strategies emerge: deterministic and heuristic methods. Deterministic optimization methods leverage the analytical properties of the problem, generating a sequence of points that converge to the optimal solution. These techniques are suitable when explicit models and constraints are available and easy to evaluate. On the other hand, heuristic approaches treat the problem as a black box, relying on iterative improvements to a fitness function. They are employed for complex problems with challenging system models or significant uncertainty.