MOBCA:多目标围攻和征服算法
Jianhua Jiang1,2, Jiaqi Wu1,2, Jinmeng Luo1,2
1Center for Artificial Intelligence, Jilin University of Finance and Economics, Changchun 130117, China.
Biomimetics (Basel, Switzerland)
|June 26, 2024
概括
本研究介绍了一种新的多目标围攻和征服 (BCA) 算法,用于复杂的优化任务. 新的BCA算法有效地估计了帕雷托的最佳解决方案,在多目标优化问题中展示了具有竞争力的准确性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 多目标优化 多目标优化
背景情况:
- 围攻和征服 (BCA) 算法在单个目标优化方面表现出色.
- 关于将BCA应用于多目标优化问题的研究有限.
研究的目的:
- 提出一个新的多目标围攻和征服 (BCA) 算法.
- 解决关于BCA的文献差距,以实现多目标优化.
主要方法:
- 将集成的网格,存档和领导人选择机制集成到BCA.
- 估计了帕雷托最佳解决方案,并接近了帕雷托最佳边界.
- 对IMOP和ZDT基准函数的算法进行了测试,与MOPSO,MOEA/D和NSGAIII对比.
主要成果:
- 拟议的多目标BCA算法取得了竞争性结果.
- 在估计帕雷托最佳解决方案方面证明了准确性.
- 在多目标优化问题解决方面表现出有效性.
结论:
- 新的多目标BCA算法是解决复杂优化挑战的可行方法.
- 具体机制的整合增强了帕雷托最佳解决方案的估计.
- 该算法对未来的多目标优化研究具有前景.
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