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用人群交互策略完善群体行为:用于多维优化问题的改进子算法
Yong Deng1,2, Yazhou Zhang3,4, Xianming Shi5,6
1School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, Guangdong, China.
人群交互 (HSI) 策略增强生物灵感的群体智能 (SI) 算法,如子算法 (MA). 这种方法提高了复杂问题的优化准确性和效率.
科学领域:
- 计算机科学
- 人工智能
- 优化算法
背景情况:
- 传统的群集智能 (SI) 算法,如子算法 (MA),在复杂的搜索空间中面临着过早的融合和计算效率低下等局限性.
- 通过人类智能增强SI算法是提高其性能和适应性的有希望的途径.
研究的目的:
- 引入和评估人群交互 (HSI) 策略,以增强生物启发的群体智能 (SI) 算法.
- 通过整合人类智能来增强优化来解决传统子算法的局限性.
- 评估HSI策略在复杂的优化任务中提高准确性,稳定性和效率的有效性.
主要方法:
- 提出了三种HSI整合策略:间歇性,持续性和参数设置相互作用.
- 在七个维度中使用七个基准函数 (一个单模,六个多模) 验证了HSI增强的MA (HSI-MA).
- 根据原来的MA和四个基线SI算法对HSI-MA的性能进行了评估.
- 在5个工程设计问题上评估HSI-MA,与36个最先进的优化器进行比较.
主要成果:
- 与MA和基线SI算法相比,HSI- MA的准确性和稳定性在统计上显著 (p < 0. 05).
- 在基准测试案例中取得了85%的优势,并减少了代数量.
- 在70%的工程设计问题场景中超过36个最先进的优化器.
- 在实际应用中提高了精度和效率.
结论:
- 人群交互 (HSI) 策略有效增强生物启发的群体智能 (SI) 算法,特别是子算法 (MA).
- 拟议的HSI框架可以提高复杂和多维问题的优化准确性,稳定性和计算效率.
- 提供一种新的方法来系统地将人类智能整合到SI中,同时保持理论基础,提高适应性和性能.
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