The rapid evolution of intelligent cluster systems—such as UAV swarms and networked autonomous agents—has brought spatial optimization and decision-making to the forefront of intelligent systems research. This paper provides a systematic and critical review of recent advances in spatial optimization for multi-agent intelligent clusters, focusing on four core domains: UAV swarm path planning, resource allocation, traffic network analysis, and visualization technologies. A bibliometric analysis based on the Web of Science Core Collection (2000–2024) identifies two major methodological transitions. In path planning, research has moved from traditional algorithms (A*, Dijkstra, dynamic programming), effective in static settings but limited in dynamic and large-scale applications, to bio-inspired optimization and deep reinforcement learning methods that improve adaptability and efficiency. In resource allocation, studies have shifted from centralized single-algorithm models to distributed, self-organizing hybrid frameworks that enhance robustness and real-time responsiveness. Moreover, intelligent cluster technologies are increasingly applied to urban traffic management and visualization, where analysis has advanced from static 2D mapping to interactive 3D and immersive VR/AR environments. A comparative framework is proposed to evaluate existing algorithms by adaptability, computational complexity, and scalability. The review concludes that future research should emphasize hybrid algorithm integration, cross-disciplinary data-driven modeling, and immersive visualization to support real-time decision-making. This study consolidates the evolutionary trajectory of intelligent cluster optimization, identifies critical research gaps, and outlines a roadmap for the next generation of intelligent spatial optimization systems.
Zhu et al. (2026) studied this question.