An Lévy Flight Based Honey Badger Algorithm for Robot Gripper Problem

J. Zhong, Xinguang Yuan, Bo Du, Gang Hu, Congyao Zhao
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Abstract

The honey badger algorithm (HBA) is a recent meta-heuristic optimization algorithm that solves optimization problems by simulating the foraging behavior of honey badgers. To solve the poor convergence of this algorithm in the face of complex optimization problems and to improve the optimization performance of HBA, this paper proposes an enhanced Lévy based HBA algorithm and applies it to the optimization problem of the robot gripper. First, we improve the optimization efficiency of the basic HBA by using the Lévy flight strategy to enhance the local search capability and avoid falling into the local optimum. Secondly, we verify the performance of LHBA by the CEC2020 test function. The experiments show that the LHBA algorithm has good optimization ability. Finally, LHBA is used to solve the robot gripper optimization problem. The results show that LHBA can obtain the minimum value of the difference between the minimum force and the maximum force and successfully solve this optimization problem.
基于lsamvy飞行的蜂蜜獾算法求解机器人抓取问题
蜜獾算法(honey badger algorithm, HBA)是一种新兴的元启发式优化算法,它通过模拟蜜獾的觅食行为来解决优化问题。为了解决该算法在面对复杂优化问题时收敛性较差的问题,提高HBA的优化性能,本文提出了一种增强的基于l )的HBA算法,并将其应用于机器人夹持器的优化问题。首先,利用lsamvy飞行策略提高基本HBA的优化效率,增强局部搜索能力,避免陷入局部最优;其次,通过CEC2020测试函数验证LHBA的性能。实验表明,LHBA算法具有良好的优化能力。最后,利用LHBA求解机器人夹持器优化问题。结果表明,LHBA能求出最小力与最大力之差的最小值,成功地解决了这一优化问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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