巨型鸟优化:一个新的生物灵感的元启发算法,用于解决优化问题
Omar Alsayyed1, Tareq Hamadneh2, Hassan Al-Tarawneh3
1Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan.
Biomimetics (Basel, Switzerland)
|December 22, 2023
概括
一个新的巨型子优化 (GAO) 算法,灵感来自子的行为,有效地解决复杂的优化问题. 与现有的元启发方法相比,GAO表现出优越的性能和统计意义.
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.
- 生物启发的方法为优化挑战提供了新的策略.
研究的目的:
- 介绍了一种新的生物启发的元启发算法,巨型鸟优化 (GAO).
- 基于巨型鱼狩猎策略的GAO算法进行建模和数学公式.
- 评估GAO在基准优化问题上的表现,并与现有算法进行比较.
主要方法:
- 开发了巨型鸟优化 (GAO) 算法,其灵感来源于鸟的食和挖掘行为.
- 在两个阶段建模GAO:探索 (向猎物移动) 和利用 (挖掘猎物).
- 在各种维度 (10,30,50,100) 中在CEC 2017测试套件上测试GAO,并与12个已建立的算法比较结果.
主要成果:
- GAO通过平衡勘探和开采,展示了优化任务的有效解决方案.
- 在大多数基准函数上,GAO与12个知名的元启发算法相比取得了更高的性能.
- 使用Wilcoxon等级总和测试进行的统计分析证实了GAO的显著优势.
- GAO在CEC 2011测试套件和现实世界的工程设计问题上表现出了有效的表现.
结论:
- 拟议的巨型鸟优化 (GAO) 算法是一种高度有效的元启发.
- GAO提供了一种强大的方法来解决复杂的优化问题和现实世界的应用.
- 在勘探,开采和在搜索过程中平衡这些阶段方面,GAO具有显著的优势.
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