Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Многокритериальное агентное моделирование× | Агентное моделирование (АМ)× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2001-2006 | 1970s–1990s (formalized as a field) |
| Автор метода≠ | Deb, K.; Tesfatsion, L. et al. | Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s) |
| Тип≠ | Simulation-optimization hybrid | Computational simulation method |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | 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 |
| Связанные≠ | 4 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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