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.
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