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一个基于候选合作和竞争的新型元启发式算法.

Yue Cong1, Bingnan Yang2, Jie Wei3

  • 1Modern Business School, Zhejiang Agricultural Business College, Shaoxing, 312088, China.

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概括

新的候选人合作竞争算法 (CCCA),灵感来自人类的社会行为,有效地解决持续优化问题. 它表现出卓越的性能和稳定性,在基准测试中表现优于现有的算法.

关键词:
候选人合作竞争算法人类社会行为 人类社会行为超启发式算法 超启发式算法优化优化 优化优化

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科学领域:

  • 计算智能是一种计算智能.
  • 优化算法 优化算法
  • 人类启发的计算

背景情况:

  • 现有的元启发算法往往缺乏来自人类社会行为的灵感.
  • 当前的以人类社会行为为灵感的算法与过早的融合和局部最佳情况作斗争.

研究的目的:

  • 引入一种新的元启发式算法,即候选合作竞争算法 (CCCA),用于连续优化问题.
  • 通过结合独特的人类社会行为来解决现有算法的局限性.

主要方法:

  • CCCA采用两阶段的方法:自我学习和候选人的相互影响.
  • 相互影响包括合作行为 (例如,协助,讨论) 和竞争机制 (例如,比赛,淘汰).
  • 该算法在23个经典基准函数上进行了测试,并与PSO,FA和CSA等既有算法进行了比较.

主要成果:

  • 在23个测试功能中,CCCA在9个功能中获得了最佳解决方案.
  • 在7个单模和多模功能中表现出强大的逃离局部最佳的能力.
  • 统计分析证实,在90%以上的测试案例中,性能显著改善,超过了8个比较算法.

结论:

  • 与现有的算法相比,CCCA表现出卓越的性能,稳定性和强大的逃离局部最佳的能力.
  • 该算法的实际有效性通过对容量分配问题的成功应用来验证.
  • 在受人类启发的元启发性优化中,CCCA代表了重大进步.