北极松鼠优化算法 集成基于对立的学习和差异进化与工程应用程序
Yating Zhu1, Tinghua Wang1, Ning Zhao1
1Key Laboratory of Data Science and Artificial Intelligence of Jiangxi Education Institutes, Gannan Normal University, Ganzhou 341000, China.
Biomimetics (Basel, Switzerland)
|November 26, 2025
概括
改进的北极松鼠优化 (IAPO) 算法通过解决缓慢的融合和局部优化来增强群体智能. 它在基准和工程测试中表现出卓越的准确性和速度.
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
- 优化技术 优化技术
背景情况:
- 群体智能算法,包括北极鱼优化 (APO),经常面临挑战,如缓慢的融合和过早的局部优化.
- 在开发有效的优化算法时,平衡勘探和开发仍然是一个关键问题.
研究的目的:
- 引入一个改进的北极优化 (IAPO) 算法,旨在克服原始APO的局限性.
- 为了提高融合速度,准确性,以及在优化任务中逃避局部最佳的能力.
主要方法:
- 整合了基于对立的镜像学习机制,以扩大搜索范围并提高解决方案寻找效率.
- 集成动态差异演变策略与适应参数,以提高局部最佳逃逸和精度.
- 在基准函数 (CEC2019,CEC2022) 和工程问题上对其他八种优化算法进行比较实验分析.
主要成果:
- 与现有算法相比,IAPO算法实现了更高的准确性,更快的融合,以及更强大的稳定性.
- IAPO在各种基准测试套件中获得了第一名的平均排名,包括CEC2019和CEC2022.
- 该算法获得了三个工程优化设计问题的最佳解决方案,证明了其实际有效性.
结论:
- 拟议的IAPO算法有效地解决了传统小群情报方法的局限性.
- 在复杂的优化任务中,IAPO在融合速度,准确性和稳定性方面取得了显著的改进.
- 算法的基准和工程问题的性能验证了它的有效性和实际应用性.
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