DCWPSO:通过更新动态惯性权重和增强的学习策略来优化粒子群
Yibo Han1, Meiting Lin2, Ni Li2,3
1School of Computer Science and Technology, Hainan University, Haikou, China.
PeerJ. Computer science
|September 24, 2024
概括
本研究介绍了一种新的粒子群集优化 (PSO) 变体,以提高优化任务的性能. 改进的算法平衡了勘探和开发,与现有方法相比,取得了更好的结果.
科学领域:
- 计算智能是一种计算智能.
- 群集情报 群集情报 群集情报
- 优化算法 优化算法
背景情况:
- 粒子集群优化 (PSO) 是一种广泛使用的元启发式算法,其灵感来源于鸟群的行为.
- 现有的PSO变体在实现各种优化问题的令人满意的性能方面表现出局限性.
研究的目的:
- 引入一种新的PSO变体,旨在克服现有方法的性能限制.
- 为了提高PSO的勘探和开采能力之间的平衡.
主要方法:
- 引入了一个动态振荡惯性重量来平衡勘探和开采.
- 采用了共弦相似性和动态邻里策略,以提高解决方案质量和人口多样性.
- 开发了一个最坏-最好的例子学习策略,以提高最不有利的解决方案和总体人口表现.
主要成果:
- 新的PSO变体在与最先进的算法相对抗时表现出了竞争力.
- 使用CEC2014和CEC2022基准测试套件的验证证实了算法的有效性在实参单一目标优化问题上.
结论:
- 拟议的PSO变体在优化性能方面提供了显著的改进.
- 这些新的策略有效地提高了解决方案的质量,人口的多样性,以及处理低于最佳的解决方案.
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