Process / pipelineSimulation / optimization
基于代理的多目标优化 — 竞争目标之间的分散式演化搜索
基于代理的多目标优化（ABMOO）将自主代理嵌入到仿真环境中，并通过演化其行为或参数来同时优化两个或多个相互冲突的目标，从而产生帕累托有效解前沿而非单一最优解。它适用于目标从微观层面交互而非封闭形式方程中涌现的复杂自适应系统。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598
- Coello Coello, C. A., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems (2nd ed.). Springer. ISBN: 9780387332543
如何引用本页
ScholarGate. (2026, June 3). Agent-Based Multi-Objective Optimization — Decentralized evolutionary search across competing objectives. ScholarGate. https://scholargate.app/zh/simulation/agent-based-multi-objective-optimization
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 基于主体的建模（ABM）仿真↔ compare
- 多目标遗传算法 (MOGA)仿真↔ compare
- 多目标优化仿真↔ compare
- 多目标粒子群优化 (MOPSO)仿真↔ compare
- 随机多目标优化仿真↔ compare