计算机应用 ›› 2015, Vol. 35 ›› Issue (4): 1057-1061.DOI: 10.11772/j.issn.1001-9081.2015.04.1057

• 人工智能 • 上一篇    下一篇

分阶段搜索的改进人工蜂群算法

李国亮, 魏振华, 徐蕾   

  1. 东华理工大学 信息工程学院, 南昌 330013
  • 收稿日期:2014-10-22 修回日期:2014-12-25 出版日期:2015-04-10 发布日期:2015-04-08
  • 通讯作者: 李国亮
  • 作者简介:李国亮(1990-),男,天津人,硕士研究生,主要研究方向:群体智能算法、GPU并行计算; 魏振华(1981-),女,内蒙古通辽人,讲师,博士,主要研究方向:数据库、空间信息科学、三维地质模拟; 徐蕾(1991-),女,江西景德镇人,硕士研究生,主要研究方向:数据库、网络教育模拟。
  • 基金资助:

    江西省教育厅科技计划项目(GJJ12398);东华理工大学博士基金资助项目(DHBK201102)。

Improved artificial bee colony algorithm using phased search

LI Guoliang, WEI Zhenhua, XU Lei   

  1. College of Information Engineering, East China Institute of Technology, Nanchang Jiangxi 330013, China
  • Received:2014-10-22 Revised:2014-12-25 Online:2015-04-10 Published:2015-04-08

摘要:

针对人工蜂群(ABC)及其改进算法在求解高维复杂函数优化问题时,存在求解精度低、收敛速度慢、易陷入局部寻优且改进算法控制参数多的不足,提出一种分阶段搜索的改进人工蜂群算法。该算法设计了分阶段雇佣蜂搜索策略,使雇佣蜂在不同阶段具备不同的搜索特点,降低了算法陷入局部极值的概率;定义逃逸半径,使其能够更好地指导早熟个体跳出局部极值,避免了逃逸行为的盲目性;同时,采用均匀分布结合反向学习的初始化策略,促使初始解分布均匀且质量较优。通过对优化问题中8个典型高维复杂函数的仿真实验结果表明,该改进算法求解精度更高,收敛速度更快,更加适合高维复杂函数求解。

关键词: 人工蜂群算法, 数值函数优化, 逃逸半径, 自适应, 均匀分布, 反向学习

Abstract:

Aiming at the shortcomings of Artificial Bee Colony (ABC) algorithm and its improved algorithms in solving high-dimensional complex function optimization problems, such as low solution precision, slow convergence, being easy to fall in local optimum and too many control parameters of improved algorithms, an improved artificial bee colony algorithm using phased search was proposed. In this algorithm, to reduce the probability of being falling into local extremum, the segmental-search strategy was used to make the employed bees have different characteristics in different stages of search. The escape radius was defined to guide the precocity individual to jump out of the local extremum and avert the blindness of escape operation. Meanwhile, to improve the quality of initialization food sources, the uniform distribution method and opposition-based learning theory were used. The simulation results of eight typical high-dimensional complex functions of optimization problems show that the proposed method not only obtains higher solving accuracy, but also has faster convergence speed. It is especially suitable for solving high-dimensional optimization problems.

Key words: Artificial Bee Colony (ABC) algorithm, numerical function optimization, escape radius, self-adaption, uniform distribution, opposition-based learning

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