Stochastic Scenario Evaluation in Evolutionary Algorithms Used for Robust Scenario-Based Optimization

This paper focuses on evaluating a scenario-based multiobjective evolutionary algorithm for real-world design problems in which the environment where a system will operate is dynamic, and uncertain. Subsequently, the performance of a stochastic scenario selection scheme, inspired by methods to reduce overfitting in genetic programming, is investigated for scenario-based optimization. Using a scenario-based scheme to address uncertainty in a real-world system's operational environment, system designs are developed via aggregating the performance of a solution evaluated across many scenarios. Within each generation of the evolutionary algorithm the evaluation suite is resampled and evaluated by the current generation's solutions. This scheme is evaluated on two historical noisy test problems and two real-world water resources design problem instances. For each case, the stochastic scenario selection scheme is compared to a static selection scheme at various evaluation suite sizes. Results show the proposed scenario selection scheme to outperform static sampling schemes and increase efficiency of a multiobjective evolutionary algorithm for robust optimization objectives.