标题： HEAS：跨尺度建模和多目标搜索的分层进化代理模拟框架 显示英文标题

标题： HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search

摘要： 分层进化代理模拟（HEAS）是一个Python框架，它将分层的基于代理的建模与进化优化和比赛评估统一在一个可重复的工作流中。 HEAS将模型表示为轻量级进程（“流”）的层次结构，这些进程在确定性的层级中调度，读取和写入共享上下文，使跨尺度耦合显式且可审计。 一个紧凑的API和CLI-模拟、优化、评估-揭示单目标和多目标进化，通过参数展平/反展平集成PyTorch策略，并提供带有用户定义评分和投票规则的一般比赛工具。 该框架通过统一的每步和每回合指标标准化评估，持久化种子、日志簿和名人堂档案，并提供用于跟踪、帕累托前沿和比较结果的绘图辅助工具，减少粘合代码并提高研究间的可比性。 HEAS强调机制与编排的分离，允许外部驱动因素、内部代理和聚合器被组合和替换而无需重构，同时同一模型可用于正向模拟、优化或系统比较。 我们通过两个简洁的示例说明用法-一个生态系统和一个企业决策设置。 HEAS为跨学科、多层次的研究提供了实用的基础，产生了可靠、可重复的结果。 显示英文摘要

摘要： 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.