Derivative-based hybrid heuristics for continuous-time simulation optimization

The topic of simulation–optimization has not been fundamentally tackled by many continuous-time modeling and simulation tools, yet. Common simulation-based optimization problems are usually coupled with standard optimization algorithms like any other simulation-free nonlinear optimization problems. While such couplings are usually based on many state-of-the-art software engineering concepts with a high-level user interface for flexible incorporation of simulation and optimization, the design of specialized optimization strategies targeting simulation-based objective functions is lacked within many simulation–optimization tools. In this work, new redefinition of Non Linear Programming (NLP) problems in the context of continuous-time simulation optimization is presented. Then, the modified optimization problems are efficiently tackled using derivative-based hybrid heuristics. In order to specify, illustrate and implement such heuristics, a new terminology is proposed. According to the proposed terminology, derivative-based hybrid strategies are implemented by hybridizing naive multistart derivative-based optimization methods with population-based metaheuristics. It is shown that the adoption of derivative-based optimization methods within hybrid optimization strategies significantly improves the solution quality of continuous-time simulation optimization problems.