方法对比
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| 基于代理的多目标优化× | 多目标优化× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| 提出者≠ | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| 类型≠ | Simulation-driven multi-objective search | Optimization framework |
| 开创性文献≠ | Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| 别名 | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| 相关≠ | 5 | 3 |
| 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. | Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis. |
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