气鱼优化算法:一种新的生物灵感的元启发算法,用于解决优化问题
Osama Al-Baik1, Saleh Alomari2, Omar Alssayed3
1Department of Software Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan.
Biomimetics (Basel, Switzerland)
|February 23, 2024
概括
一个新的鱼优化算法 (POA) 模仿了鱼的防御机制,以有效解决问题. 与现有方法相比,这种生物灵感算法在复杂的优化任务中表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 生物启发的计算 生物启发的计算
背景情况:
- 在解决复杂的优化问题时,Metaheuristic算法至关重要.
- 现有的算法经常在平衡探索和开发方面扎.
- 受自然启发的机制为算法设计提供了新的方法.
研究的目的:
- 介绍一种新的生物启发的元启发算法,即鱼优化算法 (POA).
- 基于气泡鱼对捕食者的防御机制来建模POA.
- 评估POA在解决基准和现实世界优化问题的有效性.
主要方法:
- 在两个阶段的POA的数学建模:探索 (掠食者攻击) 和利用 (掠食者逃脱).
- 使用CEC 2017测试套件在各种维度 (10,30,50,100) 的性能评估.
- 对12个已建立的元启发算法和现实世界的工程问题进行比较分析.
主要成果:
- 在搜索过程中,POA有效地平衡了勘探和开发.
- 与其他12个算法相比,POA在CEC 2017测试套件中取得了卓越的性能.
- 在受约束的优化问题和工程设计任务上,POA表现出有效的性能.
结论:
- 鱼优化算法 (POA) 是一个有前途的新型元启发.
- POA的生物灵感方法提供了一个强大而有效的优化策略.
- 对于理论和实际的优化挑战,POA提供了一个具有竞争力的替代方案.
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