创新的并行优化算法用于可靠性优化
1Department of Applied Mathematics, Gautam Buddha University, Greater Noida, India.
MethodsX
|January 9, 2026
概括
本研究介绍了一种并行草优化算法 (p-GOA) 用于可靠性优化. 这种新的平行方法平衡了全球勘探和本地改进,在更快地找到可靠的系统方面超过了现有方法.
科学领域:
- 工程优化工程优化
- 计算智能是一种计算智能.
背景情况:
- 对工程系统来说,可靠性优化至关重要.
- 现有的混合算法经常使用顺序策略.
- 平衡全球勘探和本地提炼是一个关键的挑战.
研究的目的:
- 为可靠性优化问题引入一种新的并行优化算法 (p-GOA).
- 在优化方面,提高全球勘探与本地改进之间的平衡.
- 为了解决资源限制的冗余分配问题.
主要方法:
- 开发了一个并行的合作策略,整合了草优化算法 (GOA),SOMA和非统一突变运算机 (NUMO).
- 将人口分为两个平行组:一个用于全球探索 (SOMA迁移),另一个用于本地改进 (NUMO突变).
- 采用无罚款的方法来引导寻找可行的解决方案.
主要成果:
- 与现有方法相比,p-GOA始终确定了更可靠的系统.
- 在解决可靠性优化问题时,证明了更快的融合率.
- 有效地处理现实世界的工程约束,包括成本,重量和体积.
结论:
- 通过其并行的双重战略,p-GOA提供了一种优越的可靠性优化方法.
- 该算法有效地平衡了勘探和开采,没有处罚功能.
- p-GOA显示了实际工程应用的巨大潜力.
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