一种使用中心和多方向预测策略的动态多目标进化算法
Hongtao Gao1, Hecheng Li2, Yu Shen1
1School of Computer Science and Technology, Qinghai Normal University, Xining 810008, China.
Mathematical biosciences and engineering : MBE
|March 29, 2024
概括
本研究介绍了一种用于动态多目标优化问题的新型预测模型. 新战略通过适应不同类型的环境变化,改善融合和多样性,提高预测效率.
科学领域:
- 优化优化 优化优化
- 计算智能是一种计算智能.
- 算法设计 算法设计
背景情况:
- 动态多目标优化问题 (DMOPs) 由于帕雷托前线 (PF) 和帕雷托集 (PS) 的变化而带来挑战.
- 现有的战略往往侧重于加速融合,并在动态环境中保持人口多样性.
- 预测策略是常见的,但面临效率的局限性.
研究的目的:
- 为动态多目标优化开发一个更有效的预测模型.
- 通过提高预测准确度,提高算法在动态环境中的性能.
- 为了解决在动态优化中提高预测效率的关键挑战.
主要方法:
- 提出了一个新的预测模型,利用个人的排名和值.
- 一个指标是根据两个相邻环境中个体的位置差异来定义的,以分类变化类型.
- 预测策略是根据已确定的环境变化类型量身定制的.
主要成果:
- 拟议的算法与五种最先进的方法进行了比较.
- 对20个动态多目标问题的基准实例进行了实验.
- 该算法在动态环境中展示了有效的收和分布特性.
结论:
- 开发的预测策略有效地处理动态的多目标优化问题.
- 该方法在目标或约束不断变化的环境中改善算法性能方面具有前景.
- 该方法提供了一个可行的解决方案,用于在动态优化环境中提高预测效率.
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