一个基于模糊系统的自我适应的模拟算法,使用人口多样性控制来进行进化多目标优化
Brindha Subburaj1, S Miruna Joe Amali2
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India. brindha.s@vit.ac.in.
Scientific reports
|February 17, 2025
概括
本研究介绍了使用多样性控制 (F-MAD) 的基于Fuzzy的Memetic算法,这是一个强大的,自我适应的进化算法,用于多目标优化. 在基准问题上,F-MAD表现出卓越的性能,在没有广泛的参数调整的情况下,其性能优于最先进的方法.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 进化计算是一种进化计算.
背景情况:
- 进化算法需要对各种问题领域进行广泛的参数调整.
- 现有的方法往往在过早的融合和平衡勘探-开发方面扎.
- 多目标优化问题在寻找最佳解决方案方面存在重大挑战.
研究的目的:
- 为多目标优化问题开发一个强大的,自我适应的模拟算法.
- 通过结合全球和本地搜索策略来增强进化算法.
- 创建一个自动调整参数的算法,减少手工微调的需要.
主要方法:
- 使用多样性控制 (F-MAD) 开发了基于 Fuzzy 的记忆算法.
- 集成差异演化 (DE) 具有受控的本地搜索程序.
- 采用模糊系统来自我调整DE控制参数 (交叉率,缩放因子) 以管理人口多样性.
主要成果:
- 在CEC 2009和DTLZ基准测试问题上,F-MAD表现优异.
- 在 8/10 CEC 2009 问题和所有 7 个 DTLZ 问题上取得比最先进的算法更好的结果.
- 统计分析 (弗里德曼等级测试) 证实了F-MAD的显著表现.
结论:
- F-MAD为多目标优化提供了一种强大而自我适应的方法.
- 该算法有效平衡勘探和开发,确保多样性和融合.
- F-MAD的适应性使其适用于各种应用领域,而无需参数试错.
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