A Deep Reinforcement Learning-Assisted Operator Selection for Large-Scale Multi-Objective Optimization

Large-scale multi-objective optimization problems (LSMOPs) pose significant challenges to conventional multi-objective evolutionary algorithms due to the exponential search space from the high-dimensional decision variables. Recently, adaptive operator selection by deep reinforcement learning (DRL) has become a prominent trend for solving LSMOPs. However, existing methods rely too heavily on conventional evolutionary operators and neglect specialized operators designed for large-scale problems, severely jeopardizing their performance. To address this limitation, this paper proposes a multi-objective evolutionary algorithm with DRL-assisted operator selection for LSMOPs, termed DRLOS-LSMOEA. First, a heterogeneous operator pool is constructed by integrating four conventional evolutionary operators with four large-scale dedicated operators, which substantially enriches the action space of the DRL agent. Second, a compact state representation built upon convergence and diversity is employed, alongside an adaptively normalized reward function. This design captures evolutionary dynamics and alleviates the curse of dimensionality, enabling effective training of the DRL model. Furthermore, a dimension control mechanism is proposed to dynamically regulate the decision variables that conventional evolutionary operators can conduct on in the later stage of evolution, thereby adaptively transitioning the search focus from global exploration to local exploitation. Extensive experiments on the LSMOP and UF benchmark suites demonstrate that DRLOS-LSMOEA significantly outperforms six state-of-the-art baselines across various test instances. The source code is available at https://github.com/XJFCD/DRLOS_LSMOEA.