有助于学习的人工蜂群殖民地与神经知识传输全球优化神经知识传输
Gurmeet Saini1, Shimpi Singh Jadon2, Shshank Chaube3
1Department of Applied Sciences, Rajkiya Engineering College Kannauj, Kannauj, 209732, India.
Scientific reports
|February 3, 2026
概括
学习辅助人工蜂群 (LA-ABC) 框架使用神经知识传输来改善蜂群智能优化. 这种新的方法可以防止负面的知识转移,提高搜索效率,并优于现有的算法.
科学领域:
- 人工智能的人工智能
- 群集情报 群集情报 群集情报
- 进化计算是一种进化计算.
背景情况:
- 群体智能算法面临的挑战是有效的知识利用和负面的知识转移.
- 现有的方法往往难以防止在不同搜索空间区域之间有害的知识传输.
研究的目的:
- 为全球优化引入学习辅助人工蜂群 (LA-ABC) 框架.
- 开发一种新的神经知识传输机制,以增强群体智能.
- 为了减轻优化算法中的负面知识转移.
主要方法:
- LA-ABC采用双路径架构,将群体探索与知识传输路径相结合.
- 人工神经网络 (ANN) 从历史成功的解决方案中学习,以建模复杂的环境.
- 一个生成运营商转移验证的积极知识,以创建高质量的候选解决方案.
主要成果:
- 在23个标准测试功能和IEEE CEC 2019套件中,LA-ABC表现出卓越的性能.
- 该框架显著超过了12个最先进的算法,包括ABC,L-SHADE和RL变体.
- 严格的统计测试证实了LA-ABC实现的改进的重要性.
结论:
- 洛杉矶ABC将群集智能算法转化为智能代理,能够学习和导航健身场景.
- 拟议的神经知识转移机制为在进化计算中整合强化学习和知识转移提供了一个强大的范式.
- 在全球优化技术方面,LA-ABC提供了显著的进步.
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