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PECSO:一个改进的群优化算法与性能增强战略及其应用

Yufei Zhang¹, Limin Wang², Jianping Zhao¹

¹School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.

Biomimetics (Basel, Switzerland)

|August 25, 2023

概括

一个新的群优化算法 (CSO) 策略,PECSO,提高了融合速度和准确性. 这种增强的算法有效地平衡了勘探和开发,在基准测试和工程应用中表现优于其他算法.

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:

标准的群优化算法 (CSO) 具有较低的收精度,速度较慢,并倾向于落入局部最佳状态.
解决这些局限性对于提高CSO在复杂的优化任务中的适用性至关重要.

研究的目的:

为 CSO 算法 (PECSO) 提出一个性能提升策略,以克服其固有的缺陷.
改善CSO算法中的多样性,勘探范围和勘探与开发之间的平衡.

主要方法:

实施了自由分组机制以建立层次结构,增强个人多样性和探索太空探索.
引入了以为中心的利基区分,采用同步更新和螺旋式学习来实现更好的勘探-开发平衡.
使用CEC2017基准函数验证PECSO并将其应用于工程优化案例和机器人反向动力学.

主要成果:

与基准函数上的其他算法相比,PECSO表现出更快的融合,更高的精度和更强的稳定性.
该算法成功地获得了对三个工程优化问题的良好解决方案.
在解决机器人的逆动力学方面,PECSO显示出具有竞争力的效果.

结论:

拟议的PECSO算法有效地提高了CSO的性能,在融合速度,准确性和稳定性方面提供了显著的改进.
PECSO显示出强大的实际应用潜力,包括工程优化和机器人动力学.

关键词:

群的优化群的优化自由分组机制的自由分组机制.利基技术技术的利基技术优化问题优化问题螺旋式学习策略的螺旋式学习策略同步更新是一次同步更新.

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