库卡布拉优化算法:一个新的生物灵感的超启发式算法,用于解决优化问题
Mohammad Dehghani1, Zeinab Montazeri1, Gulnara Bektemyssova2
1Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
一个新的库卡布拉优化算法 (KOA) 模仿鸟类狩猎行为,以有效解决问题. 这种以生物为灵感的方法平衡了勘探和开发,在优化任务中表现优于现有的方法.
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.
- 生物启发的方法通过模仿自然现象提供了新的策略.
- 现有的算法在有效平衡勘探和开采方面可能面临挑战.
研究的目的:
- 介绍了一种新的生物启发的元启发算法,即库卡布拉优化算法 (KOA).
- 模型KOA基于库卡布拉的狩猎和猎物杀戮行为.
- 评估KOA在基准和现实世界的优化问题上的表现.
主要方法:
- 开发了库卡布拉优化算法 (KOA),灵感来自于库卡布拉狩猎策略.
- 数学模型的KOA分为两个阶段:探索 (狩猎) 和利用 (确保猎物).
- 在CEC 2017基准函数 (10-100维) 和CEC 2011受约束问题上测试了KOA.
主要成果:
- 科亚在勘探和开发之间取得了良好的平衡,以实现高效的搜索.
- 与12个知名的基准函数元启发算法相比,KOA在基准函数上取得了更高的性能.
- 在受约束和工程设计问题上,KOA显示了可接受和优异的性能.
结论:
- 库卡布拉优化算法 (KOA) 是一个高效和有效的优化元启发法.
- KOA的生物启发方法在现有算法上提供了竞争优势.
- 对于处理复杂的现实世界优化应用程序,KOA显示出了前景.
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