一个改进的鱼优化算法,用于解决工程优化问题
Shuai Zhang1, Shuai Zhang1, Jinhuang You1
1School of Information Engineering, Fujian Key Lab of Agriculture IOT Application, Sanming University, Sanming, China.
PloS one
|February 26, 2026
概括
改进的鱼优化算法 (ICOA) 提高了使用Sobol序列和Lévy航班的种群多样性和探索. 这种先进的算法克服了原始COA的局限性,在复杂的工程优化任务中实现了卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化
- 群集情报 群集情报 群集情报
背景情况:
- 鱼优化算法 (COA) 平衡了全球搜索和本地探索.
- COA面临的挑战包括多样性退化,有限的勘探和过早地趋于当地最小值.
研究的目的:
- 引入一个改进的鱼优化算法 (ICOA),解决原始COA的局限性.
- 为了提高优化效率,人口多样性,以及在元启发算法中的探索能力.
主要方法:
- 使用Sobol序列映射来增加多样性的人口初始化.
- 在食阶段整合Lévy飞行战略,以加强探索.
- 欧几里德距离适应平衡竞争战略在竞争阶段同时开发和勘探.
主要成果:
- ICOA在各种维度的IEEE CEC2019和CEC2020基准函数上表现出卓越的表现.
- 对五个工程优化问题的验证显示,与COA相比,COA显著改善,收益高达88.8%.
- 敏感性,定量和非参数统计分析证实了ICOA的稳定性和有效性.
结论:
- ICOA有效地克服了COA的局限性,为复杂的问题提供了增强的优化功能.
- 提出的策略显著提高了鱼优化算法的有效性.
- ICOA显示出在各种工程优化挑战中应用的巨大潜力.
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