An Improved HBA Method for Path Planning of Substation Inspection Robots
DOI:
https://doi.org/10.13052/dgaej2156-3306.39410Keywords:
Substation inspection robot, reverse learning mechanism, adaptive weight factor, elite mutation strategy, path planningAbstract
Aiming at the problems of honey badger algorithm (HBA) in the path planning of substation inspection robot, such as long path length, time-consuming search and high obstacle risk rate, an improved HBA algorithm called IHBA is proposed. Firstly, the honey badger population is initialized by reverse learning mechanism to increase its diversity. Secondly, the adaptive weight factor is used to improve the density factor of the HBA, which effectively balances the exploration capacity in different stages and improves the optimization accuracy of the population. Finally, the elite mutation strategy is used to strengthen the honey badgers’ location, which can guide the population to produce offspring in the food source area with better adaptability. To verify the improved effect of the algorithm and its path planning performance, the path planning experiments are designed, which indicate that compared with the BOA, IACO, GFA and classic HBA, the proposed IHBA in this paper has shorter path, higher search efficiency and lower obstacle risk rate, which can not only help the substation inspection robot plan the optimal path globally, but also increase the smoothness of the planned path.
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