Penalty and prediction methods for dynamic constrained multi-objective optimization

Dynamic constrained multi-objective optimization problems (DCMOPs) involve objective functions and constraints that vary over time, requiring optimization algorithms to track the changing Pareto optimal set (POS) quickly. This paper proposes a new dynamic constrained multi-objective evolutionary algorithm (NDCMOEA) to address this issue. Specifically, the constraint handling strategy based on a novel penalty function integrates the constraint deviation values in the objective space and the similarity deviation values in the decision space. This method promotes selecting promising infeasible solutions closer to the POS and drives the population towards the Pareto optimal front (POF). When environmental changes occur, we employ a dynamic response strategy based on random initialization and an inverse Gaussian process model (IGPM) predictor considering the information of feasible region changes. Then, the IGPM predictor uses the sampled points generated by the Latin hypercube sampling (LHS) mechanism in the preferred regions of the objective space to obtain the initial population with better convergence and diversity in the new environment. The proposed algorithm is validated on a set of test instances and a real-world fluid catalytic cracking-distillation process optimization problem. The experimental results indicate that NDCMOEA is very competitive in dealing with DCMOPs compared with several state-of-the-art algorithms.