一种基于MOEA/D框架的新超启发式多目标优化方法

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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Jiayi Han1, Shinya Watanabe1

  • 1Muroran institute of technology, Muroran 050-0000, Japan.

Biomimetics (Basel, Switzerland)
|November 24, 2023
PubMed
概括

本研究介绍了一种新的混合算法,用于多目标优化问题 (MOPs),该算法可以在差异演变 (DE) 和共变矩阵适应演变策略 (CMA-ES) 操作员之间动态切换. 这种新的方法提高了复杂的优化任务的效率和性能.

关键词:
在CMA-ES中,CMA-ES是指CMA.这是一个IDE.美国能源部/DEA.效率检查 检查 效率检查 检查进化的多目标优化 (EMO)超启发式的方法超启发式的方法.操作员切换机制 操作员切换机制

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