An Adaptive Dual-Domain Prediction Strategy based on Second-Order Derivatives for Dynamic Multi-Objective Optimization

This paper tackles dynamic multi-objective optimization problems (DMOPs) by proposing novel change prediction strategies within an evolutionary algorithm framework. This framework combines an adaptive dual-domain change-response strategy with a second-order derivative prediction mechanism. In real-world scenarios, some Pareto Sets (PS) and Pareto Fronts (PF) evolve dynamically with environmental changes, while others remain stationary. Our algorithm employs an adaptive dual-domain approach that simultaneously monitors changes in both the PS and PF, and dynamically adjusts the allocation of prediction efforts between the decision and objective spaces according to environmental characteristics, thereby ensuring efficient sampling when objectives change. Furthermore, we incorporate a second-order derivative prediction scheme to actively reinitialize the population, enhancing the algorithm’s responsiveness to sudden or nonlinear changes. We evaluate the proposed method on 28 standard benchmark DMOPs and compare it with six state-of-the-art algorithms. The experimental results indicate that the proposed method achieves significant advantages in convergence and diversity on most test problems.