一个劳动分工人工大猩猩部队算法用于工程优化
Chenhuizi Liu1, Bowen Wu2, Liangkuan Zhu1
1School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.
Biomimetics (Basel, Switzerland)
|March 26, 2025
概括
劳动部门大猩猩部队优化器 (LDGTO) 通过划分任务和调整搜索策略来提高元启发性能. 与传统方法相比,这种新的方法可以提高优化效率.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 超听证学是一种超听证学.
背景情况:
- 传统的人工大猩猩部队优化器 (GTO) 使用统一的搜索方程,限制了性能.
- 在GTO中缺乏劳动分工,限制了适应性勘探和开发.
- 统一的方法阻碍了复杂问题的优化效率.
研究的目的:
- 引入一个增强的劳动部门大猩猩部队优化器 (LDGTO).
- 为了解决GTO中统一搜索策略的局限性.
- 通过分工来提高元启发算法的性能.
主要方法:
- 纳入劳动分工和成果分配的自然机制.
- 为适应性任务差异化 (探索/利用) 设计了一个刺激-反应模型.
- 实施了三个行为发展模式 (自我提升,能力维护,消除) 针对不同的个体阶段.
主要成果:
- 在基准测试套件 (单模,多模,组合功能) 中,LDGTO表现出卓越的性能.
- 在现实世界的工程应用中进行评估:四条转移器机制设计和彩色图像细分.
- 始终超过了三个GTO变体和七个最先进的元启发算法.
结论:
- 通过分工和适应性策略,LDGTO有效地提高了优化性能.
- 拟议的模型提供了一个更有效和更强大的元启发式方法.
- LDGTO显示出在各种领域解决复杂的优化问题的巨大潜力.
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