莱尔伯德优化算法:一种新的生物灵感的元启发算法,用于解决优化问题.
Mohammad Dehghani1, Gulnara Bektemyssova2, Zeinab Montazeri1
1Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
一个新的Lyrebird优化算法 (LOA),灵感来自鸟类的行为,在探索和利用方面表现出色. 与现有方法相比,LOA在复杂的优化任务和现实应用中表现出优异的性能.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化
- 生物启发的算法
背景情况:
- 在解决复杂的优化问题时,Metaheuristic算法至关重要.
- 现有的算法往往难以平衡探索和开发阶段.
- 生物启发的方法为优化挑战提供了新的策略.
研究的目的:
- 介绍了一个新的生物启发的元启发算法,Lyrebird优化算法 (LOA).
- 基于鸟在危险时的行为,数学模型LOA.
- 在基准和现实问题上对LOA的表现进行评估,并与已建立的算法对比.
主要方法:
- 开发了乐鸟优化算法 (LOA),模拟了乐鸟逃跑 (探索) 和隐藏 (利用) 策略.
- 在CEC 2017基准测试套件上测试的LOA跨越了各种维度 (10,30,50,100).
- 对比了LOA的性能与十二个知名的元启发算法以及CEC 2011受约束的优化问题.
主要成果:
- 洛阿在勘探,开采和平衡这些阶段方面表现出强大的能力.
- 在大多数CEC 2017基准函数上,LOA在12个竞争算法中取得了优异的性能.
- 统计分析证实了LOA的显著优势,在现实世界的工程设计问题上取得了有效的结果.
结论:
- 莱尔伯德优化算法 (LOA) 是一种高效和强大的优化技术.
- 在理论和实践领域,LOA为复杂的优化任务提供了一个有希望的替代方案.
- 生物灵感的LOA方法在优化问题解决中提供了显著的优势.
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