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一个改进的免疫算法与并行突变及其应用

Lulu Liu¹, Shuaiqun Wang¹

¹College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Mathematical biosciences and engineering : MBE

|July 28, 2023

概括

这项研究引入了一种改进的免疫算法 (IIA),具有并行突变,以实现更快的优化. IIA有效地解决了像伦纳德-斯潜力问题这样的复杂问题中的缓慢融合和局部最小值问题.

科学领域:

计算智能是一种计算智能.
优化算法优化算法
大自然启发的计算

背景情况:

免疫算法 (IA) 模拟生物免疫系统以进行优化.
由于随机搜索,标准IA方法面临着缓慢的融合和局部最佳的挑战.
现有的改进包括适应性参数设置和多样性维护.

研究的目的:

为优化问题设计一个快速高效的免疫算法 (IIA).
为了解决传统免疫算法的局限性,特别是缓慢的融合和局部最小值的捕获.
将拟议的算法应用于莱纳德-斯潜力问题 (LJPP).

主要方法:

提出了一种改进的免疫算法 (IIA),其中包含了并行突变机制 (PM).
实现了三个不同的突变运算符:考希突变 (CM),高斯突变 (GM) 和侧向突变 (LM),具有条件选择.
对二维函数,基准函数和莱纳德-斯潜在问题进行IIA评估.

主要成果:

与传统方法相比,IIA证明了较好的融合速度.
该算法在搜索过程中在平衡勘探和开发方面表现出有效性.
实验结果证实了IIA在高质量的解决方案中解决LJPP的能力.

结论:

开发的改进免疫算法 (IIA) 提供了一种快速有效的优化方法.
平行突变机制有效地提高了搜索性能,克服了常见的IA缺点.
IIA显示了现实世界应用的巨大潜力,包括列纳德-斯潜力问题.

关键词:

考希突变是一种突变.斯基基突变是高斯基的突变.列纳德-斯潜在问题免疫算法免疫算法侧向突变是一种侧向突变.变种经营者是变种经营者.

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