AutoPSO：一种用于自动粒子群优化的元框架
AutoPSO: A Meta-framework for Automated Particle Swarm Optimization
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中文总结 AI 辅助
AutoPSO是用于构建定制PSO算法的自动化元框架，通过双层搜索结合EvoX实现高效评估，发现的新型PSO变体在数值基准和机器人控制任务上显著优于强基线。
中文摘要 AI 辅助
粒子群优化（PSO）是一种广泛使用的元启发式算法，以其简洁性和少量参数而受到青睐。尽管数十年的研究已产生大量通过修改关键组件（如参数调度、群体拓扑结构或更新规则）来提升性能的PSO变体，但仍存在两个根本性挑战。其一，现有大多数方法是针对特定问题且手工设计的，导致跨任务泛化能力差，迫使从业者在大得难以实际应用的设计空间中摸索，这也阻碍了先前有效机制的系统性复用。其二，主流实现仍受限于CPU，制约了可扩展性，大幅增加了实际应用中的计算成本。为应对这些挑战，我们提出AutoPSO，一种用于构建定制化PSO算法的高度自动化元框架。AutoPSO将基于PSO的优化表述为双层过程：外层搜索探索有效PSO组件的联合空间，内层循环实例化候选变体以求解目标任务并提供反馈。外层搜索在精心整理的开放设计组件池上运行，支持灵活替换组件集和外层优化器。关键的是，通过利用EvoX实现种群张量化和批量评估，AutoPSO可在实际时间预算内高效评估数千个粒子。在数值基准测试和神经进化机器人控制任务上的综合实验表明，AutoPSO能持续发现显著优于强基线的新型PSO变体。消融研究和可扩展性研究进一步凸显了单个算法组件的贡献，并证实AutoPSO在群体规模增大时可获得更高的性能增益。源代码将公开提供。
英文摘要
Particle swarm optimization (PSO) is a widely used metaheuristic, prized for its simplicity and small parameter set. Although decades of research have produced numerous PSO variants that improve performance by modifying key components (e.g., parameter schedules, swarm topologies, or updating rules), two fundamental challenges persist. First, most existing approaches are problem-specific and hand-crafted, leading to poor cross-task generalization and forcing practitioners to navigate an impractically large design space, which also hinders systematic reuse of prior effective mechanisms. Second, mainstream implementations remain CPU-bound, constraining scalability and substantially increasing computational cost in real-world applications. To address these challenges, we propose AutoPSO, a highly automated meta-framework for constructing customized PSO algorithms. AutoPSO formulates PSO-based optimization as a bi-level process: an outer search explores the joint space of effective PSO components, while an inner loop instantiates candidate variants to solve the target task and provide feedback. The outer search operates over a curated, open-design component pool, supporting flexible replacement of the component set and the outer optimizer. Crucially, by leveraging EvoX for population tensorization and batched evaluations, AutoPSO can efficiently assess thousands of particles within practical time budgets. Comprehensive experiments on numerical benchmarks and neuroevolution robotic control tasks demonstrate that AutoPSO consistently discovers novel PSO variants that significantly outperform strong baselines. Ablation and scalability studies further highlight the contribution of individual algorithmic components and confirm that AutoPSO achieves increasing performance gains with larger swarm sizes. Code is available at {https://github.com/EMI-Group/autopso}.
