一个膝盖点驱动的进化算法,用于多目标双水平优化
IEEE transactions on cybernetics
|April 11, 2024
概括
本研究介绍了一种有效的算法,用于解决复杂的多目标双级优化问题 (MBLOPs). 新方法优先考虑首先找到可行的解决方案,然后将计算资源集中在高性能结果上,提高效率.
科学领域:
- 优化优化 优化优化
- 运营研究 运营研究
- 计算数学 计算数学 计算数学
背景情况:
- 双级优化问题 (BLOP) 涉及嵌套的优化任务,使它们具有计算密集性.
- 多目标 BLOP (MBLOP) 增加了复杂性,因为解决方案必须在两个层面平衡多个目标.
- 现有的方法往往会把资源花在与MBLOP的帕雷托前线无关的低级别解决方案上.
研究的目的:
- 开发一个有效的算法来解决多目标双级优化问题 (MBLOPs).
- 为了解决MBLOP解决中的计算挑战和资源限制.
- 在MBLOP可行区域内,提高对高性能解决方案的关注度.
主要方法:
- 一个新的多目标双级优化解决程序,集成一个膝盖点驱动的算法.
- 一个两阶段的方法:首先,快速确定可行的解决方案,尊重较低级别的约束.
- 其次,将计算精力集中在改进性能优越的解决方案上.
主要成果:
- 开发各种各样的MBLOP测试问题,具有不同的特点 (可扩展性,欺骗性,凸度,连续性).
- 拟议的算法在现实世界石油炼油问题上的验证,涉及环境和运营目标.
- 通过全面的实验来证明算法在解决MBLOP方面的有效性.
结论:
- 建议的膝盖点驱动算法通过优先考虑可行和高性能解决方案,有效地解决MBLOPs.
- 该算法的有效性在合成测试案例和实际石油炼油应用中得到了证明.
- 这种方法为解决复杂的嵌套优化挑战提供了更有资源意识的策略.
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