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多种策略改善了蜘蛛黄蜂优化工程优化解决问题的优化优化
Jinxue Sui1, Zifan Tian2, Zuoxun Wang2
1Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264005, China. suijx@sdtbu.edu.cn.
Scientific reports
|November 23, 2024
概括
多策略改进的蜘蛛黄蜂优化器 (MISWO) 增强了对复杂问题的群体智能. 这种改进的算法 (MISWO) 克服了局部最佳值,并提高了收速度,以获得更好的优化结果.
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
- 超启发式优化优化方法
背景情况:
- 蜘蛛黄蜂优化 (SWO) 算法,以社会动物行为为灵感,提供快速搜索和高准确性.
- 然而,SWO在局部最佳状态下扎,初始收速度缓慢,并且需要手动"折扣率" (TR) 调整来解决复杂的问题.
研究的目的:
- 为了提高蜘蛛黄蜂优化 (SWO) 算法的性能和多功能性.
- 解决局部最佳,缓慢收和参数灵敏度等局限性的问题.
主要方法:
- 集成灰狼算法,以改善最初的人口健康和全球优化.
- 引入适应性步骤大小和高斯突变,以提高搜索准确度和避免局部最佳值.
- 对权衡率 (TR) 的动态选择和动态镜头成像反向学习的实施,以获得卓越的个人更新.
主要成果:
- 多策略改进的蜘蛛黄蜂优化器 (MISWO) 展示了卓越的优化能力,稳定性和适应性.
- 在23个基准函数和7个工程优化问题上,MISWO的性能优于现有的最先进的算法.
- 观察到避免局部最佳情况和加速早期收的显著改善.
结论:
- MISWO有效地解决了原来的SWO算法的局限性.
- 提议的改进带来了一个更强大,更通用的优化技术.
- MISWO为解决各种领域的复杂优化挑战提供了一个有前途的替代方案.
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