模态检测 通过集成网络进行信息化分类评估,以实现昂贵的受约束多模态优化
IEEE transactions on neural networks and learning systems
|October 1, 2025
概括
本研究介绍了一种新的算法,MDICE,用于解决具有多个解决方案和约束的复杂优化问题. MDICE有效地识别了多个最佳解决方案,并有效地处理约束,即使评估有限.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 昂贵的受约束多模式优化问题 (ECMMOPs) 涉及复杂的模拟或实验,具有多个解决方案和约束.
- 有限功能评估 (FE) 在准确地找到多个最佳解决方案,同时满足约束条件时,带来了重大挑战.
研究的目的:
- 开发一个有效的算法来解决ECMMOPs.
- 为应对多模式,有限的 FE 和优化中的复杂约束所带来的挑战.
主要方法:
- 开发了一种代理辅助的自我聚类粒子群优化算法,并采用模态检测知情分类评估 (MDICE).
- 关键组件包括一个自我聚类更新机制,一种新的模式检测策略,一种模式导向的分类评估,以及一个代理辅助的可行性搜索.
主要成果:
- 在识别多个最佳解决方案和满足约束方面,MDICE表现出卓越的表现.
- 该算法有效地利用有限函数评估.
- 对33个基准函数的实验结果证实了MDICE的有效性.
结论:
- 对于昂贵的受约束的多式联运优化问题,MDICE提供了强大而高效的解决方案.
- 拟议的模式检测和指导分类方法显著提高了优化性能.
- MDICE的性能优于现有的最先进的代理辅助进化算法.
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