交叉摩生长优化:用于全球生产和优化的增强生物灵感算法.
Biomimetics (Basel, Switzerland)
|January 24, 2025
概括
新的交叉生长优化 (CCMGO) 算法通过改善信息交换和平衡勘探来提高全球优化. 在基准测试和现实世界水库生产问题上,CCMGO的表现优于现有方法.
科学领域:
- 计算智能是一种计算智能.
- 优化算法的优化算法
- 生物模拟计算是生物模拟计算.
背景情况:
- 全球优化问题在科学和工程中很常见,需要高效的搜索算法.
- 原始的生长优化 (MGO) 算法,灵感来自殖民地,面临着诸如过早融合等挑战.
- 需要改进的算法来改善复杂的搜索空间中的探索和利用.
研究的目的:
- 引入一个改进的生物灵感算法,交叉生长优化 (CCMGO).
- 通过解决其局限性来改进生长优化算法.
- 评估CCMGO在基准功能和现实世界的应用方面的表现.
主要方法:
- 通过结合交叉 (CC) 策略和动态分组参数,开发了CCMGO.
- 模仿了的交织生长,以改善信息交换和子分散的多样性.
- 模拟适应性资源分配,以平衡勘探和开采.
主要成果:
- 在CEC2017基准套件上,CCMGO表现出与九个已建立的元启发算法相比的优异性能.
- 该算法在三通道水库生产优化问题中实现了显著更高的净当前价值 (NPV).
- 实验结果证实了CCMGO在多样化和复杂的优化任务中的有效性.
结论:
- CCMGO为复杂的全球优化挑战提供了强大而适应性的解决方案.
- 改进的算法显示了现实应用的巨大潜力,特别是在资源管理方面.
- CCMGO代表了自然灵感优化技术的进步.
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