• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2013, Vol. 35 ›› Issue (4): 120-124.

• 论文 • 上一篇    下一篇

基于自适应t分布混合变异的人工鱼群算法

王波   

  1. (齐齐哈尔大学教育与传媒学院,黑龙江 齐齐哈尔 161006)
  • 收稿日期:2012-02-20 修回日期:2012-05-07 出版日期:2013-04-25 发布日期:2013-04-25
  • 基金资助:

    国家自然科学基金资助项目(61100103);黑龙江省教育科学规划课题资助项目(JJC1211088);齐齐哈尔大学教育研究重点资助项目(2012017)

Artificial fish-school algorithm based on
adaptive t distribution mixed mutation 

WANG Bo   

  1. (College of Educational and Technology,Qiqihar University,Qiqihar 161006,China)
  • Received:2012-02-20 Revised:2012-05-07 Online:2013-04-25 Published:2013-04-25

摘要:

针对人工鱼群算法在非全局极值点出现较严重聚集情况时,收敛速度降低,甚至陷入局部极值,搜索性能劣化的问题,把变异操作加入到人工鱼群算法中,增加了种群的多样性,从而在一定程度上避免算法陷入局部最优。提出了一种基于自适应t分布混合变异的人工鱼群算法。该算法对最优鱼进行高斯最优调教变异,对非最优鱼进行自适应t分布变异。引入t分布变异算子将高斯变异和柯西变异的优点结合起来,使得算法在进化初期具有良好的全局探索性,而在进化后期具有较优的局部开发性。四个典型函数算例、多个应用算例及大量的实验数据仿真结果表明,该算法是可行有效的,较ACM-AFSA和AGM-AFSA求解精度更高,算法更稳定。

关键词: 人工鱼群算法, 自适应t分布变异, 高斯最优调教变异

Abstract:

This paper proposed an artificial fish school algorithm (AFSA) based on adaptive t distribution mixed mutation, aiming at some defects of traditional AFSAs,such as low optimization precision and long running time. Gauss optimization adjustment mutation is executed on the excellent fish. Adaptive t distribution mutation is executed on the no excellent fish. In this algorithm, a new mutation operator following the t distribution is used to integrate the advantages of Gaussian and Cauchy mutation. Emulation test of four representative test functions, many application examples and a large number of test data show that this algorithm is more reliable and efficient. And this algorithm is better than ACMAFSA algorithm and AGMAFSA algorithm in the stability aspect and precision aspect.           

Key words: artificial fishschool algorithm(AFSA); adaptive t distribution mutation;Gauss optimization adjustment mutation