多策略改进的沙猫群优化:全球优化和特征选择
Liguo Yao1,2, Jun Yang1,2, Panliang Yuan3
1School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
本研究介绍了一种多策略改进的沙猫群优化 (MSCSO) 算法. 与现有方法相比,增强的MSCSO在全球优化和特征选择任务中表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 群体情报算法 群体情报算法
- 生物仿真优化 生物仿真优化
背景情况:
- 沙猫群优化 (SCSO) 算法,灵感来自沙猫的行为,显示承诺,但遭受当地最佳,低效率和有限的准确性.
- 这些局限性源于原始SCSO模型内固有的生物约束.
研究的目的:
- 通过解决其固有的局限性来增强沙猫群优化 (SCSO) 算法.
- 开发一个多策略改进的沙猫群优化 (MSCSO) 算法,以提高搜索效率和准确性.
主要方法:
- 提出了三个新的策略:基于对立的学习,增强的探索机制和生物淘汰更新机制.
- 将这些策略集成到原来的SCSO中,以创建MSCSO算法.
- 评估MSCSO的全球优化问题 (20个非固定的和10个固定的维度函数) 和特征选择任务 (24个数据集).
主要成果:
- 该MSCSO算法在各种问题上的优化能力显著改善.
- 使用最先进的算法进行的比较分析表明MSCSO的性能优越.
- 该算法显示了适应广泛的优化挑战的能力,包括高维函数和复杂的特征选择场景.
结论:
- 拟议的MSCSO算法有效地克服了原来的SCSO的局限性.
- MSCSO为全球优化和功能选择提供了强大而适应性的解决方案.
- 这些新的策略有助于提高搜索效率,准确性和避免局部最佳.
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