一个基于分解的多目标飞优化算法及其应用
Chen Zhang1, Ziyun Song1, Yufei Yang1
1Software College, Northeastern University, Shenyang 110169, China.
Biomimetics (Basel, Switzerland)
|July 26, 2024
概括
这项研究引入了一个新的多目标优化算法,MOEA/D-FFO,灵感来自飞狐的行为. 它增强了人口管理,以更好地探索和融合,在复杂的优化任务中表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 生物启发的计算 生物启发的计算
背景情况:
- 飞狐优化 (FFO) 算法在单个目标优化方面表现出有效性.
- 现有的多目标优化算法在复杂的问题解决方面面临挑战.
研究的目的:
- 为了适应飞狐优化策略的多目标问题.
- 引入一种基于分解的多目标飞优化算法 (MOEA/D-FFO).
主要方法:
- 开发了一种新的后代生成机制,以加强外围太空探索.
- 实施了人口更新方法,并调整了邻近矩阵以改善融合.
- 将MOEA/D-FFO与既有和最先进的算法进行比较.
主要成果:
- MOEA/D-FFO在11个以上的基准测试中实现了卓越的性能,超过了经典和尖端算法.
- 实验结果表明,在不同种群大小中具有很高的适应性.
- 该算法在勘探和融合率上显示出显著的改进.
结论:
- MOEA/D-FFO有效地解决了多目标优化挑战.
- 拟议的算法为工程应用提供了一个有前途的方法.
- 加强人口管理战略是其成功的关键.
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