一个改进的多策略鱼优化算法,用于解决数值优化问题
Ruitong Wang1, Shuishan Zhang1, Guangyu Zou2
1Leicester Institution, Dalian University of Technology, Dalian 124221, China.
Biomimetics (Basel, Switzerland)
|June 26, 2024
概括
改进的鱼优化算法 (IMCOA) 通过平衡勘探和开采来提高性能,克服了原始COA中发现的缓慢融合和局部最佳问题. 在数值和工程优化任务中,IMCOA展示了卓越的速度,准确性和稳定性.
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:
- 优化算法 (COA) 是一种由行为启发的新的元启发.
- 虽然COA表现良好,但与缓慢的收和过早的局部优化作斗争.
- 解决这些局限性对于提高其在复杂的优化场景中的应用性至关重要.
研究的目的:
- 为数值优化提出一个改进的多策略鱼优化算法 (IMCOA).
- 增强原始COA的全球勘探和当地开发平衡.
- 为了提高收速度,准确性,以及逃避局部最佳状态的能力.
主要方法:
- 引入了洞穴候选策略和健身距离平衡的竞争策略,以改善夏季避暑和竞争阶段.
- 修改了采食阶段,采用了食物协变性学习策略,以提高人口多样性和准确性.
- 整合了最佳的非断性搜索策略,以完善全球最佳解决方案.
主要成果:
- 与COA和其他算法相比,IMCOA在勘探和开采之间取得了更好的平衡.
- 在CEC2017和CEC2022测试套件上的实验显示了对汇率速度和优化准确度的显著改进.
- 统计分析证实IMCOA的增强性能和稳定性,优于传统的COA.
结论:
- IMCOA有效地解决了原来的COA的缺陷,特别是在收速度和局部最佳规避方面.
- 拟议的战略增强了人口的多样性和寻找全球最佳状态的能力.
- IMCOA显示了解决现实世界工程设计优化问题的实际潜力.
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