多任务基于水平的学习队伍优化器
Jiangtao Chen1, Zijia Wang1, Zheng Kou2
1School Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
Biomimetics (Basel, Switzerland)
|November 26, 2024
概括
这项研究介绍了一种新的进化多任务优化算法 (EMTO),称为MTLLSO,基于粒子群优化 (PSO). MTLLSO有效地平衡了自我进化和知识转移,在基准问题上表现优于现有的方法.
科学领域:
- 人工智能的人工智能
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 进化型多任务优化 (EMTO) 利用任务之间的相关性进行同时优化.
- 现有的EMTO算法主要使用差异进化 (DE) 和遗传算法 (GA).
- 粒子集群优化 (PSO) 提供了更快的融合,特别是在后期的进化阶段,但在EMTO中探索的较少.
研究的目的:
- 提出一种基于粒子集群优化 (PSO) 的新型EMTO算法.
- 引入基于多任务级别的学习群集优化器 (MTLLSO).
- 在进化多任务处理中增强知识传递效率.
主要方法:
- MTLLSO维护多个群体,每个群体使用基于级别的学习群体优化器 (LLSO) 优化单个任务.
- 高级别的个体 (更好的健康状况) 引导低级别的个体 (更糟糕的健康状况) 在人群中和跨人群.
- 信息传输使用来自源任务的高级人员来指导低级人员完成目标任务.
主要成果:
- MTLLSO在CEC2017基准问题上表现出有效性.
- 拟议的MTLLSO显著优于其他比较算法.
- 该算法在自我进化和知识转移之间实现了令人满意的平衡.
结论:
- MTLLSO代表了使用PSO进化的多任务优化的一个有希望的新方法.
- 基于水平的学习和有针对性的知识转移机制是有效的.
- 在基准测试中,MTLLSO与现有方法相比,表现优越.
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