大鼠算法 (GCRA):一种以自然为灵感的,用于优化问题的元启证
Jeffrey O Agushaka1, Absalom E Ezugwu2, Apu K Saha3
1Department of Computer Science, Federal University of Lafia, Lafia 950101, Nigeria.
Heliyon
|June 7, 2024
概括
大鼠算法 (GCRA) 是一种用于优化的新元启发式,灵感来自于老鼠的食行为. 它有效地找到最佳解决方案,并在各种基准和工程问题中避免局部最小值.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 优化问题在科学和工程学科中普遍存在.
- 现有的元启发算法经常面临局部最佳和融合速度的挑战.
- 持续需要新的,高效的优化技术.
研究的目的:
- 介绍一个新的元启发算法,大鼠算法 (GCRA).
- 为了优化,模拟大鼠的智能食行为.
- 评估GCRA在各种基准和工程问题上的表现.
主要方法:
- GCRA是基于大鼠的食和社会行为开发的,包括勘探和开发阶段.
- 该算法的有效性在22个经典基准函数,10个CEC 2020复杂函数和CEC 2011现实问题上进行了测试.
- 通过使用六个工程领域问题进一步验证性能.
主要成果:
- GCRA表现出卓越的性能,在测试的函数上实现了最佳或接近最佳的解决方案.
- 该算法有效地逃避了局部最小值,超过了十个最先进的算法.
- 使用弗里德曼和威尔科克森签名等级测试的统计分析证实了GCRA的有效性和稳定性.
结论:
- 大鼠算法 (GCRA) 是对复杂的优化任务的有前途的新型元启发式.
- 在解决方案的质量和稳定性方面,GCRA以生物为灵感的方法提供了优势.
- 该算法的源代码是公开可用的,用于进一步的研究和应用.
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