基于帐混乱映射和新规则的增强Aquila优化器
Youfa Fu1, Dan Liu2, Shengwei Fu1
1Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China.
帐增强的Aquila优化器 (TEAO) 通过使用帐混乱地图来改善人口分布和用于更快的融合的新公式来提高元启发算法性能. 这款增强的Aquila优化器在优化任务中展示了卓越的解决方案质量和稳定性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 超启发式算法对于复杂的问题解决至关重要,它提供了简单性和强大的优化.
- 阿奎拉优化器 (AO) 是有效的,但可能会受到缓慢的融合和局部优化问题的影响.
研究的目的:
- 引入一个增强的Aquila优化器,帐增强的Aquila优化器 (TEAO),以克服标准AO的局限性.
- 改善人口初始化和平衡勘探-开发,以实现加速和精确的优化.
主要方法:
- TEAO集成了帐混乱地图,以改善最初的人口分布.
- 引入了新的配方,以提高勘探和开发阶段之间的平衡.
- 算法的性能是使用23个基准函数和6个受约束的工程问题来评估的.
主要成果:
- 与14个跨基准函数的最先进的算法相比,TEAO表现出了优越的性能.
- 该算法在应用于受限制的工程问题时,实现了更好的解决方案质量和稳定性.
- 实验结果始终显示TEAO在现有的先进优化技术上的优势.
结论:
- 帐增强的 Aquila 优化器 (TEAO) 有效地解决了标准 Aquila 优化器的融合速度和局部最佳问题.
- 对于各种优化任务,包括复杂的工程问题,TEAO提供了更具竞争力和更强大的解决方案.
- 拟议的改进在元启发算法设计方面取得了重大进展.
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