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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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一个基于优势的两阶段代孕辅助进化算法,用于高维昂的多目标优化.

Mengjiao Yu1, Zheng Wang2, Rui Dai1

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310015, China.

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概括
此摘要是机器生成的。

本研究介绍了一种新的算法,用于高维度昂贵的多目标优化问题 (EMOP). 基于优势的两阶段代孕辅助进化算法 (TSDEA) 提高了效率和性能.

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科学领域:

  • 优化算法 优化算法
  • 计算智能是一种计算智能.
  • 工程学数学 工程学数学

背景情况:

  • 替代辅助进化算法 (SAEA) 在昂贵的多目标优化问题 (EMOP) 中很受欢迎.
  • 由于替代模型的样本要求很大,现有的SAEA与高维的EMOP扎.
  • 这种限制阻碍了SAEA在复杂的现实场景中的应用.

研究的目的:

  • 为高维度EMOP开发一个高效的代孕辅助进化算法.
  • 为应对高维空间中大型训练样本要求所带来的计算挑战.
  • 提高SAEA在复杂的优化任务中的性能和适用性.

主要方法:

  • 提出了一种新的两阶段基于主导的代孕辅助进化算法 (TSDEA).
  • 使用RBF模型来近似目标函数.
  • 采用两阶段的选择策略和新的档案更新策略来管理计算成本并提高效率.

主要成果:

  • 拟议的TSDEA在高维EMOPs中显示出有希望的性能.
  • 与现有的最先进的SAEA相比,TSDEA显示出显著的计算效率.
  • 实验结果验证了开发的策略在处理高维问题的有效性.

结论:

  • 对于高维度昂贵的多目标优化问题,TSDEA提供了有效的解决方案.
  • 该算法平衡了性能和计算效率,使其适合复杂的应用.
  • 这项工作推进了代孕辅助进化计算领域,用于具有挑战性的优化任务.