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    刘东林, 陈银银. 基于花香浓度的人工蜂群算法在机器人路径规划中的应用[J]. 华东理工大学学报(自然科学版), 2016, (3): 375-381. DOI: 10.14135/j.cnki.1006-3080.2016.03.013
    引用本文: 刘东林, 陈银银. 基于花香浓度的人工蜂群算法在机器人路径规划中的应用[J]. 华东理工大学学报(自然科学版), 2016, (3): 375-381. DOI: 10.14135/j.cnki.1006-3080.2016.03.013
    LIU Dong-lin, CHEN Yin-yin. A Fragrance Concentration Based Artificial Bee Algorithm and Its Application in Robot Path Planning[J]. Journal of East China University of Science and Technology, 2016, (3): 375-381. DOI: 10.14135/j.cnki.1006-3080.2016.03.013
    Citation: LIU Dong-lin, CHEN Yin-yin. A Fragrance Concentration Based Artificial Bee Algorithm and Its Application in Robot Path Planning[J]. Journal of East China University of Science and Technology, 2016, (3): 375-381. DOI: 10.14135/j.cnki.1006-3080.2016.03.013

    基于花香浓度的人工蜂群算法在机器人路径规划中的应用

    A Fragrance Concentration Based Artificial Bee Algorithm and Its Application in Robot Path Planning

    • 摘要: 传统的人工蜂群算法是在一种较理想的环境中进行的,不会考虑风的阻力、长时间飞行找不到蜜源使体力下降等现实因素。本文提出了基于花香浓度的人工蜂群算法——FABC算法,在传统的人工蜂群算法中加入了步长和视野范围两个因素提升求解精度,并在侦查蜂阶段提出了花香浓度机制避免陷入局部最优,提高收敛速度。为了验证FABC算法的有效性,采用4个经典测试函数对FABC算法进行了仿真实验,并将实验结果与传统人工蜂群算法以及其他改进人工蜂群算法进行对比。最后将FABC算法应用到机器人路径规划仿真实验中,实验结果证明FABC算法能够有效地解决机器人路径规划问题。

       

      Abstract: Traditional artificial bee colony algorithm is realized in an ideal environment in which some factors such as the wind resistance and physical decreasing are ignored.This paper proposes a fragrance concentration based artificial colony algorithm——FABC algorithm,which utilizes the step and visual to improve solution accuracy.Besides,the fragrance concentration mechanism is also introduced in the scouts phase to avoid falling into local optimum and improve the convergence speed.Moreover,four classic test functions are utilized in this work to verify the effectiveness of FABC algorithm,whose results are further compared with traditional artificial bee colony algorithm and other improved bee colony algorithms.Finally,the robot path planning experiment results show that the proposed FABC algorithm can effectively solve the problem of robot path planning.

       

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