方法对比
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| 基于智能体的NSGA-II× | 基于代理的多目标优化× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2000s–2010s | 1990s–2000s |
| 提出者≠ | Deb et al. (NSGA-II, 2002); integrated with agent-based modeling frameworks in the 2000s–2010s | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. |
| 类型≠ | Simulation-embedded evolutionary multi-objective optimizer | Simulation-driven multi-objective search |
| 开创性文献≠ | Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. DOI ↗ | Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598 |
| 别名 | AB-NSGA-II, ABM-NSGA2, agent-driven NSGA-II, simulation-based NSGA-II | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO |
| 相关≠ | 4 | 5 |
| 摘要≠ | Agent-based NSGA-II embeds the NSGA-II evolutionary algorithm inside an agent-based simulation loop so that objective values for each candidate solution are determined by running a full agent simulation rather than by evaluating a closed-form function. This coupling enables multi-objective optimization over systems whose performance emerges from the micro-level interactions of autonomous agents rather than from analytically tractable equations. | 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. |
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