MOANA:用于优化问题的多目标巢算法
Noor A Rashed1, Yossra H Ali1, Tarik A Rashid2
1Computer Sciences Dept., Univ. of Technology, Baghdad, Iraq.
Heliyon
|January 15, 2025
概括
新的多目标巢算法 (MOANA) 有效地解决了复杂的优化问题. 与现有方法相比,它提供了更好的融合和解决方案多样性,帮助工程设计.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 工程应用 工程应用
背景情况:
- 多目标优化问题 (MOP) 在科学和工程中很常见.
- 现有的进化算法在MOP中面临着可扩展性和解决方案多样性的挑战.
- 巢算法 (ANA) 是一个元启发,但它的多目标能力是有限的.
研究的目的:
- 引入多目标巢算法 (MOANA) 来解决MOP.
- 在多目标优化中增强勘探开发平衡和解决方案多样性.
- 为了证明MOANA在基准问题和现实工程任务中的有效性.
主要方法:
- 通过扩展巢算法 (ANA) 开发了MOANA.
- 包含适应性沉积重量参数,用于平衡勘探和开采.
- 利用多项式突变策略来确保解决方案的多样性和质量.
- 评估了ZDT功能和CEC 2019多模式基准的绩效.
主要成果:
- 与MOPSO,MOFDO,MODA和NSGA-III相比,MOANA显示出更高的收速度和帕雷托前线覆盖率.
- 该算法在接梁设计问题中实现了广泛的最佳解决方案.
- MOANA有效地解决了传统进化算法的可扩展性和多样性的局限性.
结论:
- MOANA是一个强大而有效的算法,用于解决复杂的多目标优化任务.
- 它的适应机制和突变战略有助于提供高质量和多样化的解决方案.
- MOANA为工程和其他优化密集型领域的决策提供了一个实用的工具.
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