一种混合粒子群优化算法,用于解决工程问题
Jinwei Qiao1,2, Guangyuan Wang1,2, Zhi Yang3,4
1School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China.
本研究介绍了NDWPSO算法,通过混合策略增强粒子群优化,以避免局部最佳和过早的融合. 改进的算法在基准函数和工程问题上表现出卓越的性能.
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:
- 粒子优化 (PSO) 可能会受到过早的收和局部优化的影响.
- 开发强大的优化算法对于复杂的问题解决至关重要.
研究的目的:
- 提出一个改进的粒子集群优化 (PSO) 算法,命名为NDWPSO,以解决过早收和局部最佳的局限性.
- 通过混合战略提高全球搜索速度和融合率.
主要方法:
- 利用精英的基于对立的学习来进行粒子初始化.
- 实现了用于早期全球搜索的动态惯性重量参数.
- 引入了一个新的本地最佳跳出策略.
- 纳入鱼优化算法 (WOA) 的螺旋收缩和差异进化 (DE) 的突变策略,以实现后期趋同.
主要成果:
- 在所有49个数据集中,NDWPSO的表现优于3个PSO变体.
- 在各个维度 (30,50,100) 的基准函数中获得了69.2%至84.6%的最佳结果.
- 确保了80%的最佳最佳解决方案,用于10个固定多式联运基准函数.
- 为3个实际的工程问题提供了最佳的解决方案.
结论:
- 该NDWPSO算法有效地克服过早的收和局部最佳.
- 与其他受自然启发的算法相比,NDWPSO在基准和工程任务上表现出卓越的性能.
- 混合战略显著提高了全球勘探和当地开采能力.
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