3D-path optimization of UAV based on multi-strategy improved dwarf mongoose optimization algorithm
The limitations of the dwarf mongoose optimization method include its slow convergence rate and insufficient precision in solving three-dimensional path planning issues for UAVs.This article presents several enhancements,including strengthening algorithmic exploration and advancement capabilities,refining algorithmic optimization performance,and proposing an improved version of the dwarf mongoose approach.The technique employs a strategy for generating potential food by integrating optimal leadership and Gaussian variance to amplify individual optimization capacities.Moreover,it integrates a dynamic convergence coefficient derived from a sine function to effectively harmonize the algorithm's exploration and advancement capabilities.Employing a strategy focused on centroids for exploration broadens the algorithm's search space and enhances its ability to identify the global optimum.To substantiate the algorithm's efficacy,numerical experiments,and simulation analyses were executed on twelve standard test functions alongside the UAV three-dimensional path planning quandary.The outcomes were compared with those of five alternative swarm intelligence algorithms.Experimental findings demonstrate that IDMO outperforms the comparative algorithm in terms of convergence rate,optimization precision,resilience,and scalability.
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维普
