一个改进的多策略白优化,用于解决全球优化问题
Hongmin Chen1, Zhuo Wang1, Di Wu2
1School of Information Engineering, Sanming University, Sanming 365004, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
一个改进的白优化 (IBWO) 算法通过平衡勘探和开发来增强全球问题解决能力. 这种新的方法提高了复杂的工程挑战的融合速度和准确性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 大自然启发的计算
背景情况:
- 传统的白优化 (BWO) 面临着探索-开发平衡的挑战,导致低于最佳的融合速度和准确性.
- 全球优化和复杂的工程问题需要强大的算法,能够提供高效和准确的解决方案.
研究的目的:
- 引入一个改进的白优化 (IBWO) 算法,旨在克服原始BWO的局限性.
- 为了提高勘探和开采之间的平衡,并提高趋同的准确性和速度.
主要方法:
- IBWO算法采用了一种新的集体行动策略 (GAS),灵感来自白捕行为,取代了BWO探索阶段.
- 动态针孔成像策略 (DPIS) 和二次对位策略 (QIS) 集成,以提高全球优化能力,搜索率,并保持人口多样性.
- 绩效评估利用了CEC2017和CEC2020各个维度的基准函数,以及统计Wilcoxon等级总和测试.
主要成果:
- 实验结果表明,与现有方法相比,IBWO表现出优越的优化性能和稳定性.
- 对收曲线和框图的分析证实了IBWO算法的提高效率和稳定性.
- 威尔科克森排名总和测试验证了IBWO提供的显著改进.
结论:
- IBWO算法有效地解决了BWO的局限性,为全球优化任务提供了更好的性能.
- 整合GAS,DPIS和QIS策略显著提高了算法的解决复杂优化问题的能力.
- IBWO证明了其在解决实际工程问题的适用性和有效性,并展示了其强大的性能.
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