Comparative empirical study on constraint handling in offline data-driven evolutionary optimization.

There are some practical optimization problems that can be only optimized using historical data, which is known as offline data-driven optimization problems. Since the real function evaluations are not available in the optimization process, surrogate models must replace the real fitness evaluations to guide the search. The key issue in offline data-driven optimization is how to make full use of offline data for building robust and accurate surrogate models. However, the existing offline algorithms do not consider optimization problems with constraints. Since the predicted values of the surrogate models for constraints are inaccurate, it becomes difficult to balance the objective function and the constraints during the optimization process. In this paper, we discuss a number of commonly used constraint handling techniques and combine them with an offline data-driven evolutionary algorithm. Also, four different test functions are designed with various constraints and difficulties. The results on the test function show that the multi-objective-based constraint handling technique is more likely to obtain feasible solutions, while stochastic ranking has better quality of feasible solutions. • The main challenges and research gap of offline data-driven constrained optimization are discussed. • Existing constraint handling techniques are embedded in offline data-driven evolutionary algorithm. • The performance of those constraint handling techniques for offline data-driven evolutionary optimization is studied via experiments. [ABSTRACT FROM AUTHOR]