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改进的粒子群集优化与双混沌搜索相结合

Xuepeng Zheng¹, Bin Nie¹, Jiandong Chen¹

¹School of Computer, Jiangxi University of Chinese Medicine, Nanchang 330004, China.

Mathematical biosciences and engineering : MBE

|November 3, 2023

概括

本研究介绍了双混沌搜索粒子群集优化 (DCS-PSO),这是一种增强的算法,可以缩小搜索空间并改善人口多样性. 在复杂的优化问题上,DCS-PSO表现出卓越的融合精度和速度.

科学领域:

计算智能是一种计算智能.
优化算法优化算法
群集情报群集情报群集情报

背景情况:

标准粒子群集优化 (PSO) 面临着由于依赖全球最佳粒子而导致人口多样性和过早融合的挑战.
局部优化可以陷入标准PSO,限制其在复杂的优化任务中的有效性.
混沌搜索机制为全球搜索提供了潜力,并通过混乱动态逃离局部最佳.

研究的目的:

提出一个改进的粒子集群优化 (PSO) 算法,称为双混沌搜索PSO (DCS-PSO).
解决标准公共服务任务的局限性,特别是过早的融合和缺乏人口多样性的局限性.
通过整合双混沌搜索机制来提高优化性能.

主要方法:

引入了一种双混沌搜索机制,以缩小PSO的搜索空间.
在缩小的搜索空间内整合了全球搜索的物流地图,以增强人口多样性.
利用物流和人口搜索中的最佳解决方案来引导融合.

主要成果:

DCS-PSO有效地缩小了搜索空间,将搜索群集中在有前途的地区.
在大多数经过测试的场景中,与标准的公共服务任务相比,拟议的方法显示了更好的趋同准确性.
实验结果表明,DCS-PSO的融合速度有所提高.

结论:

DCS-PSO克服了标准PSO的局限性,通过改善人口多样性和避免局部最佳情况.
整合双混沌搜索显著提高了粒子集群优化的效率和有效性.
DCS-PSO提供了一种有希望的方法,以更高的准确性和速度解决复杂的优化问题.

关键词:

混沌优化算法的算法混沌的动力学混沌的动力学优化问题优化问题粒子群集优化粒子群集优化

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