Hierarchical Evolutionary Agent Simulation (HEAS) is a Python framework that unifies layered agent-based modeling with evolutionary optimization and tournament evaluation in a single, reproducible workflow. HEAS represents models as hierarchies of lightweight processes ("streams") scheduled in deterministic layers that read and write a shared context, making cross-scale couplings explicit and auditable. A compact API and CLI-simulate, optimize, evaluate-expose single- and multi-objective evolution, PyTorch policy integration via parameter flattening/unflattening, and general tournament tooling with user-defined scoring and voting rules. The framework standardizes evaluation through uniform per-step and episode metrics, persists seeds, logbooks, and hall-of-fame archives, and provides plotting helpers for traces, Pareto fronts, and comparative outcomes, reducing glue code and improving comparability across studies. HEAS emphasizes separation of mechanism from orchestration, allowing exogenous drivers, endogenous agents, and aggregators to be composed and swapped without refactoring, while the same model can be used for forward simulation, optimization, or systematic comparison. We illustrate usage with two compact examples-an ecological system and an enterprise decision-making setting. HEAS offers a practical foundation for cross-disciplinary, multi-level inquiry, yielding reliable, reproducible results.

翻译：分层进化智能体仿真（HEAS）是一个Python框架，它将分层智能体建模、进化优化与锦标赛评估统一于单一可复现的工作流中。HEAS将模型表示为轻量级进程（“流”）的层次结构，这些进程在确定性层级中被调度，读写共享上下文，从而使跨尺度耦合关系显式化且可审计。紧凑的API与命令行界面（模拟、优化、评估）支持单目标与多目标进化、通过参数扁平化/反扁平化实现PyTorch策略集成，以及支持用户自定义评分与投票规则的通用锦标赛工具。该框架通过统一的每步与每轮指标标准化评估过程，持久化保存随机种子、日志记录与名人堂存档，并提供用于轨迹追踪、帕累托前沿及对比结果的可视化辅助工具，从而减少粘合代码并提升不同研究间的可比性。HEAS强调机制与编排的分离，允许外生驱动因素、内生智能体与聚合器在不重构的情况下进行组合与替换，同时同一模型可用于正向仿真、优化或系统比较。我们通过两个简洁示例——生态系统与企业决策场景——说明其使用方法。HEAS为跨学科、多层级研究提供了实用基础，能够产生可靠且可复现的结果。