The field of autonomous systems and multi-objective optimization is experiencing significant growth, with a focus on developing innovative methods to tackle complex real-world problems. Recent research has explored the application of evolutionary algorithms, hierarchical planning, and lexicographic frameworks to improve the efficiency and effectiveness of autonomous systems. Notably, the development of adaptive weight vector optimization and simplified hypervolume indicators has enhanced the performance of many-objective evolutionary algorithms. Additionally, the integration of heterogeneous robots and agents has enabled more efficient exploration and coordination in unknown environments. Noteworthy papers include: A many-objective evolutionary algorithm using indicator-driven weight vector optimization, which proposes an adaptive algorithm for efficient optimization. HEHA: Hierarchical Planning for Heterogeneous Multi-Robot Exploration of Unknown Environments, which introduces a hierarchical method for intelligent allocation of robots in exploration tasks.