方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 多目标基于主体的建模× | 基于主体的建模（ABM）× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2001-2006 | 1970s–1990s (formalized as a field) |
| 提出者≠ | Deb, K.; Tesfatsion, L. et al. | Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s) |
| 类型≠ | Simulation-optimization hybrid | Computational simulation method |
| 开创性文献≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396 | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. DOI ↗ |
| 别名 | MO-ABM, Multi-objective ABM, Pareto-based agent-based modeling, Multi-objective agent simulation | ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling |
| 相关≠ | 4 | 5 |
| 摘要≠ | Multi-Objective Agent-Based Modeling (MO-ABM) couples agent-based simulation with multi-objective optimization to simultaneously optimize several conflicting performance criteria across complex adaptive systems. Autonomous agents interact according to behavioral rules while an optimizer searches for parameter configurations that achieve Pareto-optimal trade-offs among competing system-level goals. | Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone. |
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