Process / pipelineSimulation / optimization

多目标遗传算法 (MOGA) — 帕累托最优解的进化搜索

多目标遗传算法 (MOGA) 是一种进化计算方法，它通过进化候选解种群来趋近帕累托最优前沿，同时优化两个或多个相互冲突的目标函数。它避免了将权衡折叠成单一分数，而是产生一组非支配解供决策者选择。

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多目标遗传算法 (MOGA)

遗传算法多目标优化多目标粒子群优化 (MOPSO)多目标模拟退火 (MOSA)Agent-Based Genetic Algo…基于代理的多目标优化基于智能体的NSGA-II 贝叶斯NSGA-II 确定性遗传算法多目标基于主体的建模

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来源

Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673
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 ↗

如何引用本页

ScholarGate. (2026, June 3). Multi-Objective Genetic Algorithm (MOGA). ScholarGate. https://scholargate.app/zh/simulation/multi-objective-genetic-algorithm

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被引用于

Agent-Based Genetic Algorithm 基于代理的多目标优化基于智能体的NSGA-II 贝叶斯NSGA-II 确定性遗传算法多目标基于主体的建模多目标蚁群优化 (MOACO)多目标元胞自动机多目标动态规划多目标粒子群优化 (MOPSO)多目标模拟退火 (MOSA)多目标禁忌搜索 (MOTS)策略情景遗传算法策略情景多目标优化稳健遗传算法 Robust NSGA-II 随机NSGA-II

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