对微型人群差异进化的精英替代策略的实证评估
Irving Luna-Ortiz1, Alejandro Rodríguez-Molina2, Miguel Gabriel Villarreal-Cervantes1
1Centro de Innovación y Desarrollo Tecnológico en Cómputo, Red de Expertos en Robótica y Mecatrónica, Instituto Politécnico Nacional, Mexico City 07700, Mexico.
Biomimetics (Basel, Switzerland)
|October 28, 2025
概括
一个新的算法,微型人群差异进化与精英替代 (μ-DE-ERM),有效地保留优化问题的多样性. 它在资源有限的环境中提供强大可靠的解决方案,计算成本低.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 进化计算是一种进化计算.
背景情况:
- 微分演化 (DE) 是一种强大的优化算法.
- 保持人口多样性对于DE的表现至关重要,特别是在复杂的景观中.
- 现有的多样性保护方法可能在计算上昂贵,或者需要明确的测量.
研究的目的:
- 引入一种新的差异演化变体,μ-DE-ERM,旨在在有限的评估预算下提高效率.
- 纳入定期的精英替代机制,以隐含地保护多样性.
- 在基准函数和现实世界的工程问题上评估算法的性能.
主要方法:
- 开发了μ-DE-ERM,这是一个微型人口的DE变体,具有周期性精英替代策略.
- 在CEC 2005和CEC 2017基准套件 (单模,多模,混合,组合功能) 上测试了性能.
- 应用于两个工程问题:动态参数识别和PID控制器调整机器人操纵器.
主要成果:
- 与DE,μ-DE,L-SHADE和RuGA相比,μ-DE-ERM获得了竞争力或优异的结果.
- 在现实应用中,与其他竞争性替代机制 (μ-DE-Cauchy,μ-DE-Shrink) 相比,已证明有效性和稳定性.
- 在严格的计算约束和有限的评估预算下表现出特别强大.
结论:
- μ-DE-ERM 是一种实用且高效的替代方案,用于在资源有限的环境中进行优化.
- 拟议的定期精英替代机制有效地保护了多样性,没有明确的测量.
- 该算法以较低的计算成本提供可靠的解决方案,使其适合实际应用.
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