A Vector Autoregression-Based Algorithm for Dynamic Many-Objective Optimization Problems

Abstract

Dynamic Many-Objective Optimization Problems (DMaOPs) represent a significant challenge due to their inherent dynamism and the presence of a large number of objectives. In addressing this complexity, this paper proposes a new prediction-based strategy tailored to managing detected changes in such problems, which is one of the first attempts to address the DMaOPs. Our proposed algorithm constructs a Vector Autoregressive (VAR) model within a dimensionality-reduced space. This model effectively captures the mutual relationships among decision variables and enables an accurate prediction of the initial positions for the evolving solutions in dynamic environments. To accelerate the convergence process, the algorithm demonstrates adaptability by responding multiple times to the same detected change. In our empirical study, the performance of the proposed algorithm is evaluated using four selected test problems from various benchmarks. Our proposed approach shows competitive results compared to the other algorithms in most test instances.