Process / pipelineSimulation / optimization
基于智能体的NSGA-II — 仿真驱动的进化多目标优化
基于智能体的NSGA-II将NSGA-II进化算法嵌入到基于智能体的仿真循环中，使得每个候选解的目标值通过运行完整的智能体仿真来确定，而不是通过评估闭式函数。这种耦合使得对那些性能源于自主智能体微观层面交互而非解析可解方程的系统进行多目标优化成为可能。
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来源
- 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: 10.1109/4235.996017 ↗
- Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151-162. DOI: 10.1057/jos.2010.3 ↗
如何引用本页
ScholarGate. (2026, June 3). Agent-Based Non-dominated Sorting Genetic Algorithm II — Simulation-Driven Evolutionary Multi-Objective Optimization. ScholarGate. https://scholargate.app/zh/simulation/agent-based-nsga-ii
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