Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. Such problems pose various challenges to evolutionary algorithms, which have popularly been used to solve complex optimisation problems, due to their dynamic nature and resource restrictions in changing environments. This paper proposes vector autoregressive evolution (VARE) consisting of vector autoregression (VAR) and environment-aware hypermutation to address environmental changes in DMO. VARE builds a VAR model that considers mutual relationship between decision variables to effectively predict the moving solutions in dynamic environments. Additionally, VARE introduces EAH to address the blindness of existing hypermutation strategies in increasing population diversity in dynamic scenarios where predictive approaches are unsuitable. A seamless integration of VAR and EAH in an environment-adaptive manner makes VARE effective to handle a wide range of dynamic environments and competitive with several popular DMO algorithms, as demonstrated in extensive experimental studies. Specially, the proposed algorithm is computationally 50 times faster than two widely-used algorithms (i.e., TrDMOEA and MOEA/D-SVR) while producing significantly better results.

翻译：动态多目标优化（DMO）处理在变化环境中具有多个（通常相互冲突）目标的优化问题。由于环境的动态特性和变化环境中的资源限制，此类问题对广泛用于求解复杂优化问题的演化算法提出了诸多挑战。本文提出由向量自回归（VAR）和环境感知超变异组成的向量自回归演化（VARE），以应对DMO中的环境变化。VARE构建了一个考虑决策变量之间相互关系的VAR模型，从而有效预测动态环境中的移动解。此外，VARE引入环境感知超变异（EAH），以解决现有超变异策略在预测方法不适用的动态场景中提升种群多样性时的盲目性。通过环境自适应方式将VAR与EAH无缝集成，使VARE能够有效处理广泛的动态环境，并在广泛的实验研究中证明其与多种主流DMO算法相比具有竞争力。特别地，所提算法在产生显著更优结果的同时，计算速度比两种广泛使用的算法（即TrDMOEA和MOEA/D-SVR）快50倍。