UAV collaborative path planning based on decision learning dung beetle optimization algorithm
A decision learning dung beetle optimization algorithm(DLDBO)solves the problem of multi-UAV collaborative path planning.The traditional dung beetle optimization algorithm(DBO)lacks information exchange among populations and easily falls into local optimal solutions.Therefore,this paper used the Pearson correlation coefficient to calculate the similarity between individuals and used the similarity index to make decisions.If individuals were not similar,it applied refraction reverse learning to calculate the candidate solution,which improved interaction among individuals and enhanced the algo-rithm's ability to escape local optima.If individuals were similar,it guided dung beetle individuals using the proposed chain proximity learning,increasing factors affecting individual renewal and promoting information exchange.Comparative experi-ments on 29 test functions of the CEC2017 test suite show that the DLDBO algorithm significantly outperforms six other advanced variants.Using DLDBO for UAV flight path planning obtains an ideal collaborative path,effectively avoiding threats and surpassing three other excellent collaborative path planning algorithms,meeting the needs of UAV collaborative flight.
万方数据
维普
