一个基于强化学习的双人群 Nutcracker 优化器,用于全球优化
1School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518000, China.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
基于强化学习的新型双种群破子优化算法 (RLNOA) 通过平衡全球勘探和本地开发来提高优化. 这种改进的算法克服了复杂问题的局部最佳问题.
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:
- 传统的破子优化算法 (NOA) 在平衡全球勘探和本地利用方面面临挑战,往往导致本地最佳.
- 复杂的优化问题需要具有强大的探索和开发机制的算法.
研究的目的:
- 引入一种新的基于强化学习的双种群破子优化算法 (RLNOA).
- 通过改善全球勘探和当地开采之间的平衡,提高NOA在解决复杂优化问题的性能.
主要方法:
- 双种群机制根据适应性将人口划分为勘探和开发分种群.
- 使用基于随机对立的学习的改进的食策略增强了勘探子群体的多样性.
- 在剥削子群体中,Q学习被用作剥削策略的适应选择器.
主要成果:
- 与九个最先进的元启发算法相比,RLNOA表现出更高的性能.
- 对CEC-2014,CEC-2017和CEC-2020基准函数集的评估验证了算法的有效性.
- 拟议的RLNOA有效地平衡了全球勘探和当地开发,减轻了当地最佳陷.
结论:
- 通过有效解决勘探-开采困境,RLNOA显著改善了传统的NOA.
- 强化学习和双人群策略的整合为复杂的优化任务提供了强大的方法.
- 在RLNOA呈现了一个有前途的进步在metaheuristic优化算法.
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