神经网络增强的竞争性群体优化器用于大规模的多目标优化
IEEE transactions on cybernetics
|July 24, 2023
概括
本研究引入了一个神经网络增强的竞争性群群优化器 (NN-CSO) 来改善大规模多目标优化问题 (LMOPs). 该NN-CSO增强了赢家粒子演变,大大提高了性能比标准的CSO和其他算法.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 竞争性群体优化器 (CSO) 对大规模多目标优化问题 (LMOP) 显示出前景.
- 现有的CSO研究往往忽视了赢家粒子进化在最终性能中的关键作用.
- 在CSO框架内增强赢家粒子的进化动态存在差距.
研究的目的:
- 提出一种神经网络增强的新型CSO (NN-CSO),以提高LMOP的性能.
- 解决传统的CSO中忽视赢家粒子演变的局限性.
- 为了利用神经网络来演变赢家粒子并增强优化动态.
主要方法:
- 通过对对竞争将群粒子分为赢家和输家组.
- 训练一个神经网络 (NN) 模型,使用失败者粒子作为输入,获胜者粒子作为输出.
- 演化赢家粒子使用训练NN模型,而输家粒子则由赢家引导.
主要成果:
- 非公开的CSO显著提高了CSO在LMOP上的表现.
- 实验结果表明,与最先进的大规模多目标进化算法相比,它们具有优势.
- 该NN模型有效地学习并将有前途的进化动态应用于获胜粒子.
结论:
- 拟议的NN-CSO有效地增强了获胜粒子演变,从而实现了卓越的优化性能.
- NN-CSO提供了一种可行和改进的方法来解决复杂的大规模多目标优化问题.
- 这项工作突出了将神经网络集成到集群智能的潜力,以实现高级优化.
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