Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Агентное программирование целей× | Агентно-ориентированная многокритериальная оптимизация× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1990s-2000s (hybrid integration) | 1990s–2000s |
| Автор метода≠ | Charnes, Cooper (GP); Schelling, Holland (ABM foundations) | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. |
| Тип≠ | Hybrid simulation-optimization | Simulation-driven multi-objective search |
| Основополагающий источник≠ | Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138-151. DOI ↗ | Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598 |
| Другие названия | ABGP, Agent-Based GP, ABM-GP, Agent-Driven Goal Programming | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO |
| Связанные | 5 | 5 |
| Сводка≠ | Agent-Based Goal Programming (ABGP) integrates agent-based simulation with goal programming optimization to model systems where multiple autonomous decision-makers pursue competing, prioritized goals. It enables researchers to study how decentralized, adaptive behavior at the agent level leads to system-level outcomes measured against predefined targets, capturing both emergence and multi-criteria satisfaction simultaneously. | 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. |
| ScholarGateНабор данных ↗ |
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