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首页> 外文期刊>Optimal Control Applications and Methods >A new search scheme using multi-bee-colony elite learning method for unmanned aerial vehicles in unknown environments
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A new search scheme using multi-bee-colony elite learning method for unmanned aerial vehicles in unknown environments

机译：A new search scheme using multi-bee-colony elite learning method for unmanned aerial vehicles in unknown environments

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摘要

In this research, a cooperative search for multiple dynamic targets in an unknown marine environment by multiple unmanned aerial vehicles is studied based on a novel multi-bee-colony (MBC) elite learning algorithm. First, a specialized searching model is established which includes the UAV dynamics, the sensor model, the target probability and environmental certainty at different flight altitudes. Then, a new search strategy, which consists of rough search and accurate search, is proposed by maximizing the multiobjective utility function with the dynamic changing of the flight altitude. In order to solve the optimization problem, an improved MBC algorithm based on elite learning is designed, which can improve the adaptability and computation speed of the standard artificial bee colony (ABC) algorithm under different search missions. Finally, extensive simulations are conducted to show the effectiveness and superiority of the proposed search strategy.

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