The field of multi-agent systems and swarm robotics is witnessing significant advancements, with a focus on developing innovative control strategies and optimization algorithms to enhance the coordination and adaptability of autonomous agents. Researchers are exploring bio-inspired approaches, such as leader-follower plasticity and distributed oscillatory guidance, to achieve efficient and scalable control of large-scale swarms. Additionally, improved optimization algorithms, including multi-strategy improved snake optimizers and enhanced particle swarm optimization, are being developed to tackle complex problems like three-dimensional UAV path planning and trajectory prediction. These advancements have the potential to revolutionize various applications, from swarm robotics to synthetic biology. Noteworthy papers include: Improved particle swarm optimization algorithm for real-time trajectory planning in dynamic environments, which introduces a persistent exploration mechanism and entropy-based parameter adjustment strategy. A minimalist controller for autonomously self-aggregating robotic swarms, which achieves compact formations in multitasking scenarios using a line-of-sight sensor. Multi-strategy improved snake optimizer for three-dimensional UAV path planning, which combines adaptive random disturbance and Levy flight strategies to improve convergence speed and precision. Distributed oscillatory guidance for formation flight of fixed-wing drones, which enables control over average velocity along a path without requiring actuation over speed.