在代表性的引力搜索算法中,人口交互网络:物流分布导致性能恶化
Haotian Li1, Yifei Yang2, Yirui Wang3,4
1Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan.
Heliyon
|June 3, 2024
概括
人口交互网络揭示了元启发式算法 (MHA) 如何共享信息. 强大的算法,如微分进化 (DE) 倾向于波桑分布,而较弱的算法,如粒子群优化 (PSO) 和引力搜索算法 (GSA) 倾向于物流分布.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 网络科学 网络科学
背景情况:
- 元启发式算法 (MHA) 被广泛用于复杂的优化问题.
- 了解MHA内部的个人间信息交换对于绩效分析至关重要.
- 人口交互网络 (PIN) 为可视化和分析这些交互提供了一个新的框架.
研究的目的:
- 研究个人间信息交互模式与各种MHA的表现之间的关系.
- 分析代表性MHA的信息动态,包括微分进化算法 (DE),粒子群优化 (PSO) 和引力搜索算法 (GSA) 以及其变体.
- 评估人口互动网络 (PIN) 在描述和比较MHA性能方面的有效性.
主要方法:
- 利用人口交互网络 (PIN) 模拟MHA中个人之间的信息流.
- 在IEEE进化计算大会2017年基准函数上分析了七个MHA,包括DE,PSO,GSA和四个GSA变体.
- 将PIN中的节点度的累积分布函数 (CDF) 调整为七个分布模型.
主要成果:
- 差异进化算法 (DE) 显示出对波桑分布的偏差更强.
- 粒子群优化 (PSO),引力搜索算法 (GSA) 和其变体显示了对物流分布的偏差.
- 与物流分布的较大偏差与GSA变体的性能改善相关,表明其对GSA增强的好处.
结论:
- 人口互动网络 (PIN) 提供了一种强大的方法来描述MHA信息动态.
- 该研究表明,PIN和MHA性能中节点度的分布模式之间存在联系.
- 偏离物流分布被认为是改善GSA绩效的关键因素.
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