文章摘要
引用本文:
基于行动轨迹的人工蜂群算法
Improved artificial bee colony algorithm based on actions trajectory
投稿时间:2017-04-06  修订日期:2017-06-06
DOI:
中文关键词: 人工蜂群算法  群智能  函数优化问题
英文关键词: artificial bee colony  swarm intelligence  numerical function optimization
基金项目:福建省科技厅引导性项目(2017H0001);福建省教育厅科研支助项目(JA15356);国家自然科学基金资助项目(11501114);福建省青年教师教育研究基金资助项目(JA14217)
作者单位E-mail
何尧 福建工程学院信息科学与工程学院 240678575@qq.com 
郭文忠 福州大学数学与计算机科学学院  
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中文摘要:
      人工蜂群算法(artificial bee colony algorithm ,ABC)中蜜蜂在开采蜜源时,随机选择维度,随意决定开采方向和步伐来搜索新蜜源,没有利用以往的搜索经验,导致ABC收敛速度过慢。本文对此提出了基于行动轨迹的人工蜂群算法(EDABC), 记录跟随蜂开采蜜源的行动轨迹,并以此为经验引导下一次开采,以提高ABC算法的开采能力。通过对优化函数寻优测试,实验结果表明该算法不仅加快收敛速度,提高寻优能力,还具有良好的鲁棒性和稳定性。
英文摘要:
      In the model of ABC(artificial bee colony algorithm), the bee’s randomly selection of dimension ,direction and step size to exploit the food source resulting in slow convergence speed. In this paper, an improved artificial bee colony algorithm based on bees actions trajectory (EDABC) is presented. EDABC records the historical actions of a bee when it exploit honey, and analyses to guide the generation of a new candidate solution. The performance of proposed approach was examined on benchmark functions. The experimental results show that the proposed approach is successful in terms of solution quality, robustness and convergence to global optimum. Keywords: artificial bee colony ; swarm intelligence; numerical function optimization
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