一个增强的以团队为导向的群体优化算法 (ETOSO) 提供强大的和高效的高维搜索
1College of Engineering, Islamic University of Madinah, Madinah 42351, Saudi Arabia.
Biomimetics (Basel, Switzerland)
|April 25, 2025
概括
增强的团队导向群体优化 (ETOSO) 算法克服了以自然为灵感的方法的停滞. 在复杂的,高维度的优化问题中,ETOSO表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 大自然启发的计算
背景情况:
- 自然启发的优化算法 (NIOA) 经常遭受停滞.
- 现有的算法,如面向团队的群体优化 (TOSO),需要改进以提高性能.
- 解决停滞对于提高解决方案准确性和融合速度至关重要.
研究的目的:
- 介绍了以团队为导向的增强群体优化 (ETOSO) 算法.
- 通过整合新的勘探和开发策略来改进TOSO算法.
- 评估ETOSO在处理高维搜索空间和缓解停滞方面的有效性.
主要方法:
- 开发了带有增强的勘探和开发机制的ETOSO算法.
- 对26个已建立的NIOAs进行了比较评估.
- 为了严格测试,在各种维度 (D=2到200) 中使用了15个基准函数.
主要成果:
- 在解决方案准确性和融合速度方面,ETOSO表现出卓越的性能.
- 与其他NIOA相比,该算法显示了更好的计算复杂性和一致性.
- ETOSO有效地解决了高维优化任务中的停滞问题.
结论:
- ETOSO是对TOSO的一种强大而高效的改进,用于以自然为灵感的优化.
- 该算法为复杂的优化挑战提供了一种简化但强大的方法.
- 在解决高维搜索空间的停滞方面,ETOSO代表了一项重大进展.
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