计算机应用 ›› 2013, Vol. 33 ›› Issue (07): 1922-1925.DOI: 10.11772/j.issn.1001-9081.2013.07.1922

• 人工智能 • 上一篇    下一篇

基于自适应t分布混合变异的人工萤火虫算法

杜晓昕,张剑飞,孙明   

  1. 齐齐哈尔大学 计算机与控制工程学院,黑龙江 齐齐哈尔 161006
  • 收稿日期:2013-01-22 修回日期:2013-02-24 出版日期:2013-07-01 发布日期:2013-07-06
  • 通讯作者: 杜晓昕
  • 作者简介:杜晓昕(1983-),女,江苏徐州人,讲师,硕士,主要研究方向:人工智能、图像处理;张剑飞(1974-),男,黑龙江齐齐哈尔人,副教授,博士研究生,主要研究方向:数据挖掘;孙明(1979-),男,山东烟台人,副教授,博士,主要研究方向:混沌神经网络。
  • 基金资助:

    国家自然科学基金资助项目(51169007);黑龙江省教育厅科学技术研究项目(12531758)

Artificial glowworm swarm optimization algorithm based on adaptive t distribution mixed mutation

DU Xiaoxin,ZHANG Jianfei,SUN Ming   

  1. College of Computer and Control Engineering, Qiqihar University, Qiqihar Heilongjiang 161006, China
  • Received:2013-01-22 Revised:2013-02-24 Online:2013-07-06 Published:2013-07-01
  • Contact: DU Xiaoxin

摘要: 针对人工萤火虫(AGSO)算法中存在一些漫无目的随机运动的萤火虫及一些萤火虫在非全局极值点出现严重聚集时,收敛速度降低,甚至陷入局部极值的问题,提出一种基于自适应t分布混合变异的人工萤火虫算法。用自适应t分布变异和最优调教变异来增强种群的多样性,限制算法陷入局部最优;定义了变异控制因子对变异的运行进行控制,结合历史状态信息给出了自适应t分布混合变异描述。该变异方法能使算法同时提高全局探索能力和局部开发能力。通过典型函数算例和实际应用算例实验结果表明,该算法是可行有效的,比传统算法具有较快的寻优速度和较高的寻优精度。

关键词: 人工萤火虫算法, 自适应t分布变异, 最优调教变异, 变异控制因子, 全局探索能力, 局部开发能力

Abstract: The convergence speed of Artificial Glowworm Swarm Optimization (AGSO) algorithm declines, even falls into local minimums, when some glowworms gather in non whole extreme points or some glowworms wander around aimlessly. Concerning this problem, an AGSO algorithm based on adaptive t distribution mixed mutation was proposed. Adaptive t distribution mutation and optimization adjustment mutation was introduced into the AGSO algorithm to improve the diversity of glowworm swarm, and prevent the AGSO algorithm from falling into local minimums. Mutation control factor was defined. Combining history status information, the description of adaptive t distribution mixed mutation was given. The mutation method could enhance ability of global exploration and local development. The emulation results of representative test functions and many application examples show that the proposed algorithm is reliable and efficient. Meanwhile, this algorithm is better than tradition algorithm in terms of speed and precision for seeking the optimum.

Key words: Artificial Glowworm Swarm Optimization (AGSO) algorithm, adaptive t distribution mutation, optimization adjustment mutation, mutation control factor, global exploration ability, local development ability

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