整合演员-关键强化学习与多模式多目标优化进化算法
IEEE transactions on neural networks and learning systems
|November 3, 2025
概括
本研究引入了关键演员强化学习 (RL) 方法,以增强多式多目标优化问题 (MMOP) 的进化算法. 该方法通过动态优化利基规模,平衡多样性和趋同以提高绩效来提高适应性.
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:
- 多模式多目标优化问题 (MMOP) 要求在多样性和趋同之间保持平衡.
- 传统的算法在环境选择中表现出有限的适应性,阻碍了各种MMOP的性能.
研究的目的:
- 提高MMOP进化算法的环境选择适应性.
- 引入一种新的方法,将演员关键强化学习 (RL) 与进化算法相结合.
主要方法:
- 开发了一个RL流程,以动态优化利基规模,平衡多样性和融合偏好.
- 定义状态 (融合/多样性措施),行动 (利基规模调整) 和奖励 (状态改善).
- 雇佣演员和批评者神经网络用于实时在线学习和适应性定位.
主要成果:
- 拟议的算法在48个基准问题和现实世界的应用中与十种最先进的方法相比,表现出了卓越的性能.
- 在保持多样性和融合之间的平衡方面取得了显著的改进.
- 与现有算法相比,展示了整体优化效率的提高.
结论:
- 与演进算法集成的关键角色RL显著提高了MMOP的环境选择适应性.
- 适应性化技术,结合本地趋同评估,提供了对优化进行全面评估.
- 提出的方法为复杂的优化挑战提供了强大而有效的解决方案.
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