Path planning in disaster scenarios based on improved artificial bee colony algorithm

doi:10.7523/j.ucas.2021.0027

Abstract

Abstract: Aiming at the shortcomings of artificial bee colony algorithm in previous studies, such as exploration limitations and development inefficiency, an improved artificial bee colony algorithm with adaptive convergence is proposed. The algorithm uses global sampling and random initialization to ensure the integrity of the initial solution set. The mining times factor is added to the selection probability calculation to increase the probability of potential solutions. Combining the characteristics of the cosine function change, the selected individuals are subjected to adaptive partial development under the guidance of the global optimal individual to improve the accuracy of local development. Finally, through comparison with multiple algorithms in different disaster scenarios, the results show that the improved algorithm has higher solution accuracy, better global convergence, and can efficiently solve path planning problems in complex disaster scenarios.

Key words: artificial bee colony algorithm, global sampling, potential solution, path planning, disaster scenario, global convergence

CLC Number:

TP311

ZHU Jinlei, YUAN Xiaobing, PEI Jun. Path planning in disaster scenarios based on improved artificial bee colony algorithm[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(3): 397-405.

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