方法对比
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| 基于代理的多目标优化× | 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≠ | 1990s–2000s | 1970s–1990s (formalized as a field) |
| 提出者≠ | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. | Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s) |
| 类型≠ | Simulation-driven multi-objective search | Computational simulation method |
| 开创性文献≠ | Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598 | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. DOI ↗ |
| 别名 | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO | ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling |
| 相关 | 5 | 5 |
| 摘要≠ | Agent-based multi-objective optimization (ABMOO) embeds autonomous agents inside a simulation environment and evolves their behavior or parameters to simultaneously optimize two or more conflicting objectives, yielding a Pareto-efficient frontier of solutions rather than a single optimum. It is suited to complex adaptive systems where objectives emerge from micro-level interactions rather than closed-form equations. | 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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