适应性动态自学灰狼优化算法,用于解决全球优化问题和工程问题
1School of Artificial Intelligence and Computer Science, Jiangnan University, WuXi 214122, China.
Mathematical biosciences and engineering : MBE
|March 29, 2024
概括
适应性动态自学灰狼优化算法 (ASGWO) 通过提高种群多样性和融合速度来增强原来的灰狼优化 (GWO). 这种新的方法有效地解决了局部最佳和优化任务中过早的趋同等局限性.
科学领域:
- 计算智能是一种计算智能.
- 超学优化算法 超学优化算法
- 群集情报 群集情报 群集情报
背景情况:
- 灰狼优化 (GWO) 算法是一种流行的元启发法,以其简单的结构和效率而闻名.
- 然而,标准GWO受到人口多样性较低,易受局部最佳情况的影响,趋同缓慢,勘探开发不平衡等问题所困扰.
- 这些局限性阻碍了它在复杂的优化问题上的性能.
研究的目的:
- 提出一个改进的元启发算法,即自适应动态自学灰狼优化 (ASGWO),以克服原始GWO的缺陷.
- 增强融合率,人口多样性和逃离当地最佳条件的能力.
- 验证ASGWO在经典测试函数和工程问题上的有效性.
主要方法:
- 非线性化和细分融合因子以平衡全球和本地搜索.
- 引入一个动态的对数螺旋路径狼的运动,以扩大搜索范围和加强当地发展.
- 基于演化成功率和代数来设计动态自学步骤大小,以防止振荡和局部最佳陷.
- 提出一种新的位置更新策略,使用全球最佳和随机位置来增加人口多样性并避免过早的趋同.
主要成果:
- 与GWO,PSO和WOA相比,ASGWO在23个经典测试函数上表现出优越的性能.
- 拟议的算法在趋同的准确性和速度方面显示出显著的改进.
- ASGWO表现出一种强大的能力,可以逃避局部最佳状态,避免过早的趋同.
- 在工程应用中也观察到有效的性能,如轮,压力容器和汽车防撞问题,以及在特征选择任务中.
结论:
- ASGWO算法有效地解决了标准GWO的局限性,提供了改进的优化功能.
- 适应性和自我学习机制显著提高了与局部最佳相对的融合速度,准确性和稳定性.
- ASGWO为广泛的优化问题,包括复杂的工程应用和特征选择提供了一个有前途的元启发式方法.
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