计算机科学 ›› 2013, Vol. 40 ›› Issue (3): 279-282.

• 人工智能 • 上一篇    下一篇

求解高维函数优化问题的混合蜂群算法

林志毅,王玲玲   

  1. (广东工业大学计算机学院 广州510006) (武汉大学软件工程国家重点实验室 武汉430072)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Hybrid Artificial Bee Colony Algorithm for Solving High-dimensional Function Optimization Problems

  • Online:2018-11-16 Published:2018-11-16

摘要: 为了提高人工蜂群算法求解复杂优化函数的全局搜索能力,提出了多父体杂交算法、差分进化算法和蜂群算法的混合蜂群算法(Hybrid artificial bcc colony algorithm, HABC) 。 HABC的核心在于,采用多父体杂交算子提高人工蜂群算法的全局搜索能力,通过淘汰相同个体保证群体的多样性,利用差分进化算子加快人工蜂群算法的收敛速度。高维函数优化问题的仿真结果表明,该算法全局搜索能力好,收敛速度快。

关键词: 多父体杂交,差分进化算法,人工蜂群算法,HABC

Abstract: In order to enhance the global search ability of artificial bee algorithm in solving complex function optimizalion problem,a hybrid artificial bee colony algorithm (HABC) was proposed. HAI3C is based on multi-parent crossover and differential evolution, and the key points of it lie in;1) employs multi parent crossover to enhance the global search capability of the algorithm;2) removes identical individuals from the population for maintaining the diversity;3) adopts differential evolution operator to speed up the evolution. Experimental results on high-dimensional function optimization problems show that HABC possesses more powerful global search capability and better convergence rate.

Key words: Multi-parent crossover, Differential evolution, Artificial bee colony algorithm, HABC

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