针对多目标进化算法的新搜索策略
Liu Yuejun1,2
1Software School of Anyang Normal University, Anyang, 455002, Henan, China.
Heliyon
|December 25, 2024
概括
本研究引入了一种新的搜索策略,以增强多目标进化算法 (MOEA),提高融合速度和解决方案准确性. 这种新方法可以提高NSGA-III和MOEA/D.等算法的性能.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 进化计算是一种进化计算.
背景情况:
- 多目标进化算法 (MOEA) 在代过程中经常面临搜索效率方面的挑战.
- 现有的MOEA可能需要提高融合速度和非主导解决方案集的准确性.
研究的目的:
- 提出一个新的搜索策略,重点是基于个人的解决方案生成,以提高MOEA的性能.
- 将这一战略纳入已建立的MOEA,特别是NSGA-III和MOEA/D,创建NSGA-III/NG和MOEA/D-NG.
主要方法:
- 开发了基于以个人为中心的方法来改进解决方案生成的新型邻居和指导策略.
- 将新的搜索策略应用于NSGA-III和MOEA/D算法.
- 对标准测试集 (ZDT,DTLZ,WFG) 进行比较实验,与各种最先进的MOEA进行比较.
主要成果:
- 增强的NSGA-III/NG和MOEA/D-NG算法与其基线对应器和其他先进算法相比,表现出更高的性能.
- 拟议的战略提高了12.54%的收速度和3.67%的非主导解决方案设置精度NSGA-III和MOEA/D.
- 实验结果证实,修改后的MOEA的搜索能力得到了显著的改善.
结论:
- 新的搜索策略有效地提高了多目标进化算法的性能.
- 该战略显示出很好的适用性,并且可以轻松地与主流MOEA结合.
- 这项研究为提高进化优化的效率和准确性提供了一种有价值的方法.
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